DEV Community: Bhagvan Kommadi

State of Aerospace Industry in 2020

Bhagvan Kommadi — Tue, 11 Feb 2025 05:38:17 +0000

Global spending on engineering services was USD 750 billion in 2004, with aerospace accounting for 8 percent; this could rise to USD 1.1 trillion by 2020, according to NASSCOM. The total offshore engineering expenditure is expected to grow to between USD 150 to USD 225 billion by 2020 and India, with its talent pool and experience in engineering services, could assume 25 percent of this market. US DoD defense programs representing billions of US dollars, are likely to start soon, enter the engineering manufacturing design phase, and reach low-rate or full-scale production over the next few years.These programs include Ohio Class Submarine replacement, F-35 fighter jet, KC-46A aerial refueling tanker, Long Range Strike Bomber, USAF T-X trainer, and Rafale fighter programs. Activities in engineering services that could be outsourced range from concept and detailed design stages to the testing, production and support stages. Some of these activities include industrial / mechanical/electrical design and analysis, reverse engineering, system engineering, Computer Aided Design (‘CAD”) work, embedded software, derivative products, auxiliary functions (piping, cabling, controls), component testing, test equipment design, prototyping, technical manuals, manufacturing engineering, tooling design and build and value/ cost engineering. The Aerospace sector continues to improve its productivity and efficiency due to several initiatives taking hold over the last several years.

Companies have improved their performance in reducing inventories, rationalizing their asset footprint, better managing their supply chain, and increasingly replacing labor with process automation on the factory floor. In addition, the transition of paper drawings to computer-aided design has brought a significant leap in employee productivity. Digital product development allows the entire product to be designed and tested in the computer, without the need for costly physical mockups. For example, the modeling and simulation allowed by digital product development significantly reduces design flow time, tolerance buildup and engineering errors UK’s “full spectrum approach.” has resulted in an additional £12 billion of funding over the next ten years to the equipment and support. This funding will support many initiatives, including the acquisition of nine new Boeing P-8 maritime patrol aircraft, continued investment and international collaboration in unmanned combat air system and complex weapon programs, an acceleration of the procurement of 138 F-35 combat aircraft, alongside a £1.9 billion investment over the next five years in the UK’s intelligence and cyber capabilities.

In the commercial aerospace subsector, the UK continues to enjoy a record backlog of orders across narrow and wide-body aircraft. In the French aerospace sector, air traffic control in the national airspace sector is gaining attention. Airspace challenges in Western Europe exacerbate the need for a more efficient and improved air transport system. Under the guidance of Eurocontrol, the introduction of sophisticated information systems is designed to optimize airspace management, but security and system integration complexities will affect progress shortly. Improvements in system design with the interconnection between ground control systems and aircraft are a high priority due to cyber security threats India India is expected to spend USD 100 billion in the next decade towards the purchase of defence equipment. India is believed to be the world’s second-largest buyer of weaponry. India’s Aerospace Industry is rapidly building capabilities to emerge as a preferred destination for manufacturing of aerospace components. India has skills and competencies in areas that include engineering, production, etc. These capabilities have been recognized and harnessed by foreign companies outsourcing manufacturing work to India.
A potential opportunity exists to demonstrate India’s expertise in the process beginning right from initial design and ending with the final manufacture. This is where India’s real and sustainable advantage exists. State governments are helping boost development in the industry by establishing special economic zones (SEZs) for the aerospace industry. These include: The INR 3,000 crore Aerospace and Precision Engineering Special Economic Zone to be set up at Adibatla, Ranga Reddy district in Andhra Pradesh The specialised aerospace park of 1,000 acres proposed near the Bangalore International Airport The 2,500 acre SEZ for the aerospace and avionics industry, proposed to be established in south Gujarat, close to the Delhi-Mumbai industrial corridor. This is likely to have several MRO facilities. Indian industry today is on the threshold of entering into a new era where it will assume greater responsibility in making the nation self-reliant in Defence Production. The resurgence of India’s manufacturing sector has been remarkable.

Not only are the profits soaring, but the sector is also making its presence felt abroad as many Indian firms are becoming transnational companies. The Indian manufacturing sector is internationally competitive with international quality standards, efficiency and manufacturing facilities. India is fast developing into a manufacturing hub for world corporations wanting to leverage the sector’s proven skills in product design, reconfiguration and customization with creativity, assured quality and value addition. India, is also keen to strengthen its aerospace industry and has asked major weapon exporting countries to transfer technology to India. Presence of large Defence PSUs Presence of scientific and technical institutes Deep aerospace expertise - a network of 2000 SMEs that do niche subcontracting work for the DPSUs Information Technology (‘IT’), design and engineering expertise Manufacturing expertise Proximity to vendor base Government support Opportunity for related services like ground handling, and the manufacture of ground support equipment Other advantages regarding location, excellent telecommunications networks Job Market in Aerospace Engineering Global aerospace majors are facing a shortage of engineering talent. India has a large talent pool of English-speaking engineering graduates; approximately 500,000 engineers graduate each year. Indian IT firms have developed best practice processes for quality, project management, and organizational maturity. Many of these practices can be transferred to the aerospace industry which can, in turn, leverage these mature processes to bring improvements into the project lifecycle, covering core R&D services, design and development, verification and validation, development of tools, reverse engineering and maintenance services.
Aerospace engineers are responsible for creating exceptional machines—like airplanes which weigh more than half a million pounds to spacecraft which travel at a speed of more than 17,000 miles/hour. They are in charge of designing, developing and testing aircraft, spacecraft and missile systems as well as supervising the manufacturing process of these products. Aeronautical Engineers are those aerospace engineers who deal with airplanes, while Astronautic engineers are engineers who deal specifically with spacecraft. Technologies developed by aerospace engineers are used in aviation, defense, and space exploration and aerospace engineers may specialize in structural designing, guiding, navigating and controlling, instrumentation and communication, or production methodology.
Technology like computer-aided design (CAD) software, robotics, laser and advanced electronic optics are used by them. Specialization in commercial transport, military fighter planes, helicopters, spacecraft, missiles or rockets within the aerospace product is also possible. Aerospace engineers might also specialize in aerodynamics, thermodynamics, celestial mechanic systems, propulsion systems, acoustics, or guidance and control systems. These engineers usually gain employment in the aerospace product and parts industry, though the skills of such engineers are now being valued in different fields. An example of this is in the manufacture of motor vehicles, aerospace engineers are responsible for designing vehicles which have a low resistance to air and greater efficiency in fuel consumption. Aeronautical Engineers also get excellent job opportunities in foreign countries like America, United Kingdom, Germany and France. On the commercial side, the outlook is fairly good in the Western world. Commercial market forecasts show a steadily increasing demand for air travel, while most of the big airframe manufacturers have ageing workforces. All the retiring engineers have to be replaced somehow! So the outlook is reasonably good. And if you are in China or India, very good! On the military side, the outlook is less rosy, particularly in the US and Europe, as those governments are desperately trying to reduce defence spending without jeopardizing their military commitments. But even so, defence firms have the same problem of ageing workforces.
Aerospace Jobs Electronic & Electrical Engineering Flight Engineering Materials Process & Physics Mechanical & Structural Engineering Production Engineering Software Engineering Systems Engineering Test & Evaluation The huge thrust being given to the indigenisation of military aircraft coupled with the ‘Make in India’ initiative. Considering that about 80% of our military aerospace life-cycle expenditure is met through imports of parts, sub-assemblies and complete equipment, there is a dire need for Indian companies, small and large, to grab this opportunity, not for the business alone, but more for ensuring that security of our country is in our own hands. Hero Motors Hero Motors plans to produce light aircrafts at its 300 acre aerospace park in Madhya Pradesh, in partnership with an unidentified European manufacturer. Tata Group The Tata group is keen to move into full-scale aircraft assembly and production in both the civil and defence markets. The group sought approval to set up an aerospace manufacturing facility on the outskirts of Hyderabad. The company has already signed deals with several International companies, including one to manufacture components for Boeing. It has assumed a one-third stake in Italy’s Piaggio Aero, while Israel Aerospace Industries and the Tata group signed a memorandum of understanding to establish a new company to develop, manufacture and support a wide range of defence and aerospace products, including missiles, Unmanned Aerial Vehicles(‘UAVs’), radars, electronic warfare systems and homeland security systems. Mahindra & Mahindra Mahindra & Mahindra has signed deals with BAE Systems and is jointly developing a five-seat light aircraft with the National Aerospace Laboratories. Mahindra Aerospace offers a number of career opportunities in diverse fields, including: Managerial, Finance, Production, Engineering, Marketing, Administration and more Larsen and Toubro Larsen and Toubro is in the process of forming a joint venture with the European EADS to develop high-tech defence electronics in Pune. This venture will focus on developing electronic warfare, radar, defence avionics and mobile systems for defence applications Skills required for Aerospace Industries One is a Graduation Degree (B.Tech) & other is an Aircraft Maintenance Engineering (AME) course which is a diploma. The subjects that you would learn during the bachelor’s course in Aerospace Engineering are:

Aerodynamics
1. Aircraft structures
2. Flight Dynamics
3. Strength of materials
Space stability
1. Astrodynamics
2. Control systems
3. Propulsion
4. Thermodynamics and Fluid Mechanics
5. Vibrations and Theory of elasticity
6. Aircraft materials and composites
7. Space systems
8. Computational fluid dynamics(Elective)
9. Digital signal processing (Elective) Professional Associations Aeronautical Society of India (AeSI) Computer Society of India (CSI) The Institution of Engineers (India) (IEI) The Institution of Civil Engineers (India) (ICEI) Institution of Electronics and Telecommunication Engineers (IETE) Indian Institute of Chemical Engineers (IIChe) Indian Institution of Industrial Engineering (IIIE) Society of EMC Engineers (India) (SEMCE(I)) Indian Society for Technical Education (ISTE) Indian Science Congress Association (ISCA) NASA & their projects Europa NASAs planned mission would conduct detailed reconnaissance of Jupiters moon and investigate whether the icy moon could harbor conditions suitable for life. Ground Systems Development and Operations (GSDO) Building on five decades of launch and processing excellence, the Ground Systems Development and Operations Program is transforming Kennedy Space Center into a multi-user spaceport capable of accommodating a wide array of government and commercial space activities.

HEO-ESD Integration Exploration Systems Development Division (ESD) has been charged to oversee the development of nation’s next generation of human exploration systems. ESD Responsibilities include: Provide HEO MD with insight and oversight of programs developing human exploration capabilities (MPCV, SLS, and 21st Century Ground Systems), Complete Constellation Transition during FY11, Manage cross-program integration across MPCV, SLS and 21CGS Programs (Manage interfaces between programs and cross-program risks as well as ensure cross-program integration is occurring). InSight InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) is a NASA Discovery Program mission that will place a single geophysical lander on Mars to study its deep interior. Ice, Cloud, and Land Evaluation Satellite-2 (ICESat-2) The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is the 2nd-generation of the laser altimeter ICESat mission (January 13, 2003 to August 14, 2010). ICESat- 2 is scheduled for launch in early 2016. International Space Station (ISS) The International Space Station is an unprecedented achievement in global human endeavors to conceive, plan, build, operate, and utilize a research platform in space. With the assembly of the space station at its completion and the support of a full-time crew of six, a new era of utilization for research has begun. James Webb Space Telescope (JWST) The James Webb Space Telescope is a large, infrared-optimized space telescope. The project is working to a 2018 launch date. Webb will probe for the first light after the Big Bang and find the first galaxies that formed in the early Universe, connecting the Big Bang to our own Milky Way Galaxy. Webb will peer through dusty clouds to see stars forming planetary systems, connecting the Milky Way to our own Solar System and look for the chemical signatures of extra-terrestrial life. Joint Polar Satellite System-2 (JPSS-2) JPSS-2 will provide operational continuity of satellite-based observations and products for NOAA Polar-Orbiting Environmental Satellites (POES) and Suomi NPP satellite and ground systems. The baseline plan for JPSS Ground System will be sustained to support JPSS-2, similar to JPSS-1. Joint Polar Satellite System Ground (JPSS Ground) The ground system for the JPSS mission is a global network of receiving stations linked to NOAA, which distributes the satellite data and derived products to users worldwide. The versatile ground system controls the spacecraft, ingests and processes data and provides information to users like NOAAs National Weather Service. Joint Polar Satellite System Flight (JPSS Flight) The Joint Polar Satellite System (JPSS) is the Nations next generation polar- orbiting operational environmental satellite system. JPSS is a collaborative program between the National Oceanic and Atmospheric Administration (NOAA) and its acquisition agent, National Aeronautics and Space Administration (NASA).

Mars 2020 Designed to advance high-priority science goals for Mars exploration, the mission would address key questions about the potential for life on Mars. The mission would also provide opportunities to gather knowledge and demonstrate technologies that address the challenges of future human expeditions to Mars.. Multi-Purpose Crew Vehicle (Orion MPCV) This spacecraft will serve as the primary crew vehicle for missions beyond low Earth orbit. The Orion MPCV is capable of conducting regular in-space operations (rendezvous, docking, extravehicular activity) in conjunction with payloads delivered by the Space Launch System. Origins-Spectral Interpretation-Resource Identification-Security-Regolith Explorer (OSIRIS-REx) The OSIRIS-REx spacecraft will travel to a near-Earth carbonaceous asteroid (101955) 1999 RQ36, study it in detail, and bring a sample back to Earth. This mission will help scientists investigate planet formation and the origin of life, and the data collected at the asteroid will also aid our understanding of asteroids that can impact Earth. Solar Probe Plus (SPP) NASA has tapped the Johns Hopkins University Applied Physics Laboratory (APL) to develop the ambitious Solar Probe Plus mission, which will study the streams of charged particles the Sun hurls into space from an unprecedented vantage point: inside the Suns corona - its outer atmosphere - where the processes that heat the corona and produce solar wind occur. Space Launch System (SLS) The Space Launch System (SLS) Program will develop a heavy-lift launch vehicle to expand human presence to celestial destinations beyond low Earth orbit. This launch vehicle will be capable of lifting the Orion MPCV to asteroids, the moon, Lagrange points and, ultimately, to Mars.

Space Network Ground Segment Sustainment (SGSS) The mission of the SN Ground Segment Sustainment (SGSS) Project is to implement a flexible and extensible ground segment that will allow the Space Network (SN) to maintain the high level of service in the future, accommodate new users and capabilities, while reducing the effort required to operate and maintain the system in the future. Opportunities in NASA, ISRO, DRDO & other private firms Pilots or Crew Members of a Spacecraft Pilot Astronaut, Mission Specialist, Payload Specialist Physical Scientists Astronomer, Chemist, Geologist, Meteorologist, Oceanographer, Physicist Life Scientists Biologist, Medical Doctor, Nutritionists, Physiologist, Psychologist Social Scientists Economist, Sociologist Mathematicians Computer Scientist, Mathematician, Statistician, Systems Analyst Other Fields Quality Control Inspector, Ground Radio Operator, Teletypist Engineers ISRO Government of India established the Department of Space in 1972 to promote development and application of space science and technology in the country for the socio-economic benefits.

Indian Space Research organization (ISRO) is the primary agency under the Department of Space for executing space programmes. During the early seventies, India undertook demonstration of space applications for communication, television broadcasting and remote sensing building experimental satellites namely, APPLE, Bhaskara – and experimental satellite launch vehicles, SLV-3 and ASLV. Today, India has an impressive array of achievements with the largest constellation of domestic communication satellites called Indian National Satellite System (INSAT) in the Asia pacific region with about 210 transponders in orbit. And, India has plans to augment the capacity with the launching of INSAT satellites and increase it to about 500 in 4-5 years to meet its growing needs. India also has the largest constellation of earth observation satellites called Indian Remote Sensing (IRS) satellites with better than one meter resolution. IRS data is being used for a variety of applications such as crop yield estimation, drinking water missions, waste land development, forest cover mapping and a host of other applications benefiting the common man. Using INSATs, besides TV Broadcasting, telecommunications and meteorological applications societal applications such as tele-education, telemedicine applications have been operationalised. Village Resource Centers (VRCs) combining the services of IRS and INSAT satellites for providing an array of services have been established. India, today is considered as a leader in the application of space technology. INSAT and IRS satellites are also providing invaluable services in disaster management. To put the IRS and INSAT satellites into orbit, India has developed two work horse launch vehicles namely the Polar Satellite Launch Vehicle (PSLV) and Geo- synchronous Satellite Launch Vehicle (GSLV). PSLV weighing about 300 tons at lift off has the capability to put 1500 kg satellite in polar sun-synchronous orbit. PSLV with eleven consecutively successful launches has demonstrated its high reliability. PSLV has launched eight satellites for various customers from abroad. GSLV with four successful flights is capable of launching 2200 kg satellites into geo-stationary Transfer Orbit. India has also created world class facilities at its space port in Sriharikota near Chennai with launch pads besides a host of test facilities for testing satellites and launch vehicle systems.
DRDO
DRDO is working in various areas of military technology which include aeronautics, armaments, combat vehicles, electronics, instrumentation engineering systems, missiles, materials, naval systems, advanced computing, simulation and life sciences. DRDO while striving to meet the Cutting edge weapons technology requirements provides ample spinoff benefits to the society at large thereby contributing to the nation building.

DRDO is a network of 52 Defence Laboratories in India which are deeply engaged in developing critical defence technologies covering various disciplines like aeronautics, armaments, electronics, combat vehicles, engineering system, instrumentation, missiles, advanced computing and simulation, special materials, naval systems, life sciences, information systems and agriculture. Presently over 5000 scientists and about 25000 other scientific technical and supporting personnel back the organization. Several major projects for the development of missiles, armaments, light combat aircrafts, radars, electronic warfare systems etc are on hand and significant achievements have already been made in several such technologies.
Hindustan Aeronautics Limited (Hal)
HAL, a Defence PSU, is a major player in the global aviation arena. It has built up comprehensive skills in design, manufacture and overhaul of fighters, trainers, helicopters, transport aircraft, engines, avionics and system equipment. Its product track record consists of 12 types of aircraft from in-house R&D and 14 types by licence production inclusive of 8 types of aero engines and over 1000 items of aircraft system equipment (avionics, mechanical, electrical). HAL has produced over 3550 aircraft, 3650 aero-engines and overhauled around 8750 aircraft & 28400 engines besides manufacture/overhaul of related accessories and avionics. The Company has the requisite core competence base with a demonstrated potential to become a global player. HAL has 19 production divisions for manufacture and overhaul of aircraft, helicopters, engine and accessories. It has also 9 R&D Centres to give a thrust to research & development. HAL’s major supplies/services are to Indian Air Force, Indian Navy, Indian Army, Coast Guard and Border Security Force. Transport aircraft and Helicopters have been supplied to Airlines as well as State Governments. The Company has also achieved a foothold in export in more than 20 countries, having demonstrated its quality and price competitiveness.

HAL is a major partner for the Space Vehicle programmes of the Indian Space Research Organisation. It has also diversified into the fields of industrial & marine gas turbine business and real-time software business. HAL is now ranked 34th in the list of world’s top 100 defence companies. HAL continues its growth with a sales turnover of 2.1 Billion US Dollars during the financial year 2007-08. It has doubled its turnover in 3 years. It has declared profit before tax of 538 Million US Dollars. The company has made supplies to almost all the major aerospace companies in the World like Airbus, Boeing, IAI, IRKUT, Honeywell and Ruag etc. All the production Divisions of HAL have ISO 9001-2000 accreditation and sixteen divisions have ISO-14001-2004 environment management system (EMS) certification. Six divisions have also implemented the aerospace sector quality management system requirements stated in As 9100 standard and obtained certification. Four of these divisions have also obtained NADCAP certification (National Aerospace Defence Contractors Accreditation programme –USA) for special processes such as NDT, heat treatment, welding etc. In order to meet with the challenges in the 21st Century, the Company has redefined its mission as follows: “To become a globally competitive aerospace industry while working as an instrument for achieving self-reliance in design, manufacture and maintenance of aerospace equipment, Civil Transport Aircraft, helicopter & missiles and diversifying to related areas, managing the business on commercial lines in a climate of growing professional competence.” HAL has successfully designed & developed the Advanced Light Helicopter, which is currently being operated by the Defence Services of India and private Companies. The Advanced Light Helicopter also has great export potential.
Apart from licence production of front line fighters like Su-30 MKI, HAL is also developing the following products through design and development: -- Intermediate Jet Trainer (IJT) -- Light combat helicopter (LCH) -- Weaponization of Advanced Light Helicopter (ALH) -- Tejas-Light Combat Aircraft
Bharat Electronics Limited (BEL)
BEL was established in 1954 to meet the specialized electronic needs of the country’s defence services, is a multi-product, multi-technology, multi-unit company. It serves the needs of domestic and foreign customers with the products/services manufactured in its nine state-of-the-art ISO 9001/2 and ISO 14000 certified manufacturing plants in India. BEL manufactures a wide repertoire of products in the field of Radars, Naval systems, Defence Communication, Telecommunication and Broadcasting, Electronic Warfare, Opto Electronics, Tank Electronics and Electronic Components. With the expertise developed over the years, the company also provides turnkey systems solutions and Electronic Manufacturing Services (EMS) on “Build to Print” and “Build to Spec” basis. BEL has become a US $ 1 Billion company in the financial year 2007-08.
Bharat Dynamics Limited (BDL)
BDL is fully owned by the Government of India, was established in 1970. BDL manufactures guided weapons & related test equipment, Launchers, under water weapon systems and decoys for the Indian Defence Services. BDL is the nominated Production Agency for the indigenous Integrated Missile Development Programme. Starting with production of 1st Generation Anti tank Guided missiles, the Company has grown into a multi technology and multi product organization.
Collaborative association with DRDO and world leaders in missile manufacturing has enabled BDL assimilate critical technologies and emerge as a globally competitive and reliable defence equipment manufacturer. Bharat Earth Movers Limited (BEML Limited) BEML Limited, Government of India Company, is one of the largest manufacturers and suppliers of earthmoving, construction and mining equipment in Asia. With over 42 years of experience, BEML is one of the premier engineering companies in India and plays a significant role in providing vital inputs to the core sectors of the economy, apart from manufacturing a wide range of tailor-made equipment for the Indian Defence sector. BEML manufactures a wide range of sophisticated hi-tech equipment like bulldozers, rear dump truck, front-end loaders, hydraulic excavators, rope shovels, motor graders, walking draglines, pipe layers, tyre handling equipment, aircraft towing tractor, heavy duty transportation trailers, heavy duty trucks and its prime movers, rail coaches including day coaches, sleeper coaches, postal vans, track-laying equipment, overhead Inspection cars, diesel engines and gensets. Ordinance Factory Board (OFB) The Indian Ordinance Factories possess the unique distinction of more than 200 years of experience in Defence production. Under the aegis of its corporate headquarters the Ordinance Factory Board, the organization is currently engaged in production, logistics, research & development and trade in the field of defence. Ordinance Factory Board offers comprehensive range in the areas of land, naval and air defence systems. These include small, medium and large calibre weapons & ammunition, mortars, explosives, pyrotechnics, armoured & soft skin vehicles, optical & night vision devices, parachutes and troop comfort items. Mishra Dhatu Nigam Limited (MIDHANI)
MIDHANI – an ISO 9002 company - caters to domestic and international customers with modern metallurgical facilities and high degree of technical competence for manufacturing its diverse product mix of superalloys, titanium alloys, special purpose steels, electrical resistance & soft-magnetic alloys, molybedenum and other alloys meeting the stringent requirements of the strategic sectors like defence, aerospace, power and general engineering etc. MIDHANI employs its highly integrated and flexible manufacturing facilities to produce a wide variety of special metals and alloys in various mill forms such as ingots, forged bars, hot rolled steels and bar, cold rolled sheets, strips and foils, wires, castings and tubes.
** BrahMos Aerospace**
BRAHMOS-supersonic cruise missile is designed for use in multiple platforms- ships, silos, mobile launchers, aircrafts and submarines against land and sea targets. BRAHMOS is the World leader flying all through supersonic with maneuverable trajectories ensuring no reaction time to the enemy and a lethal punch owing to huge kinetic energy of impact. BRAHMOS has attained 100% success rates in all flight trials proving the adequacy of the missile system to a maximum range of 290 km with high accuracy and lethality establishing the reliability of the system in all weather conditions. Indian Navy & army have started the induction of the weapon system. The system will also be exported to a few friendly countries.
Honeywell Technology Solutions
Honeywell Technology Solutions is a research lab headquartered in Bangalore and is working on developing and testing flight management systems. The lab offers technical research and development services to Honeywell business across the globe.
*Infosys Technologies Ltd *
Infosys Technologies Ltd, another major software firm, is a partner in forward integration and helps build aircraft components and systems for customers, such as Boeing and Airbus, through local vendors. In addition to delivering software and engineering services for aerospace clients, the company is now part of the product supply chain.
*Wipro *
Wipro Ltd is another large software firm that helps build electronic warfare systems, radars, aviation electronics and flight simulators locally for US defence contractors, such as Lockheed Martin andNorthrop Grumman. The company is setting up dedicated units for these systems and anticipates larger revenues from defence customers moving forward. The company also maintains a tie-up with Britain’s largest defence manufacturer, BAE Systems, to build sub-systems for aircraft engines that power business jets.
*HCL Technologies *
HCL Technologies is also a leading software firm with a good clientele in engineering services for aviation and aerospace sector. The company is a strategic partner for Boeing’s Dreamliner program and is a major player in the offshoring of aerospace technological development services. The company has also augmented its aviation and aerospace capability by its joint venture with Smith’s Aerospace and by its acquisition of Axon Consulting which has strengths in aviation MRO.
*Airbus *
Airbus established an engineering centre in Bangalore in October 2007. Almost 120 staff are employed and Airbus hopes to increase its staff strength to 400 by 2012. Airbus plans to outsource 40 percent of aircraft design to local companies. This engineering centre is the only one outside Europe for Airbus. Additionally, Airbus outsources work to 20 Indian IT and engineering service providers that include Infosys, Quest, HCL Technologies, etc . According to media reports, Airbus plans to move 20 percent of its engineering design activities to low cost countries, most of it to India.
*QuEST Global *
QuEST Global, the Bangalore-based engineering company, entered into a 10-year strategic relationship with Magellan Aerospace. In 2007, the firm set up the country’s first special processing facility for aerospace manufacturing, delivered the first set of A-380 components to SABCA and achieved Airbus AS 9100 certification. Quest is the first Indian private sector player qualified to offer end-to-end solutions to EADS. Engineering Centres Airbus Engineering Centre in Bangalore is the company’s high-tech aircraft component manufacturing facility that works on the development of tools to aircraft design and structural analysis using software based simulation, among other things. CAE, a global leader in aviation simulation products and training services, made Karnataka its base in India, and is operating from an owned facility near the new Bangalore international airport. The Airbus A320 and Boeing 737 Level D full-flight simulators in the CAE facility are certified by India’s Directorate General of Civil Aviation. Boeing entered into agreements with Indian Institute of Science, WIPRO and HCL to develop wireless and other network technologies for aerospace related applications. Mahindra and Mahindra signed an agreement for the design and development of a new general aviation aircraft with NAL, CSIR and the Government of India.

The wind-tunnel testing center at NAL is the primary simulation testing facility for aircraft engines in India Courses Hindustan Institute of Technology & Science, Chennai offers several short-term courses. The duration of these courses is one to two weeks. Eligibility: B.E. / AM AeS.I. / AME / Diploma. The following are the courses offered: Introduction to Aviation Engineering Aircraft Electrical & Instrument Systems Avionics Design & Maintenance Aircraft Systems Airframe Maintenance Procedures Aero Engine Maintenance & Overhauling Fuel and Lubrication Management Flight Safety and Airworthiness Repair and Maintenance of Composite Aircraft Structures TQM in Aviation Industry Entrepreneurship Opportunities Newspace companies are attracting investments from a variety of sources both within and beyond the aerospace community, in a multitude of forms and sizes. What are these investors interested in, why do they find newspace companies to be attractive investments, and what are some of ways newspace companies are being accelerated, incubated, and invested in? This panel will explore these questions and more, investigating how and why the newspace industry has become so attractive to investors.
*Startup Accelerators *
Airbus BizLab
Starburst Accelerator
Space Tango NewSpace
LightSpeed Innovations
WayPaver Foundation
Corporate Innovation
*100 Open Startups *
The 100 Open Startups program was started in Brazil 9 years ago through the launch of Centre for Open Innovation in Sao Paulo by the Father of Open Innovation, Dr. Henry Chesbrough, Haas School of Business, Berkley. This program that had 40 large corporations as founding members including Abbott has now become a massive movement with 120 large corporations from different verticals actively engaged in the platform. The 100 Open Startups is a network where large corporations and Startups interact in the pursuit of high impact innovations to society. The main expected result is to identify through a crowdsourcing rating process, the ranking of the 100 most attractive Startups to the market. Top ranked Startups will have the opportunity to interact with senior executives of large corporations to work on their proposals and to compete for prizes and the attention from investors and the media. This is an ideal platform for you in the areas of your interest like Innovative Cities, Future Education, Information Society, Healthcare & well-being, Agriculture, Food and allied areas as this is most unique program and ecosystem of that kind in the world as brings all the key stakeholders in your industry together in a true Open Innovation platform. As you can see in our website, large corporations like 3M, Accenture, Cisco Brazil, IBM, Intel, HP, J&J, Telefonica, Whirlpool and several others are deeply engaged with us for long term in creation of Grand Challenges in 21 sectors. The Industrial Sectors are added based on the demand from large corporations.
Campus Party Entrepreneurs are a fundamental part of Campus Party. During the week of the event, we will be supporting them! We show you how to turn your ideas into business models, we will give advice on how their startups can be transformed into success stories and create an environment where they can share experiences, find talent and end up being the best version of themselves. Campus Party, the biggest event the world in the areas of innovation, creativity, science, entertainment and entrepreneurship, wants the legacy of his business project is help build the future of societies where operates, forming and training young people for their ideas, technology based, successful and continuity. This is where start-ups, campuseros, ecosystem players (accelerators, investors, enthusiasts) and visitors come together to acquire knowledge, share experiences and develop their businesses. Start-ups are also welcome to showcase their products and services in an open display area for all the participating public.

Old post : My startup Architect Corner: In Fastest 25 Growing Startups

Bhagvan Kommadi — Mon, 10 Feb 2025 08:31:08 +0000

My startup Architect Corner that focuses on AI products to transform lives of cities across different geographies, is a smart enterprise stabilized by an digital technology infrastructure backbone. The products of Architect Corner are Smart City Platform, AI Deep Learning Platform, IOT Platform and Enterprise Mobile Platform. The cutting-edge AI products of Architect Corner not only address today’s challenges but also deliver value to customers in their digital transformation.

Please check out Silicion City Article

With an unflinching commitment to excellence and relentless passion for bringing value to the businesses through innovative AI solutions, Bhagvan Kommadi laid the foundation stone of Architect Corner in 2016, following PM Modi’s Digital India initiative. Now the company has consolidated its position as a prominent name among the AI companies. The strength of Architect Corner lies in AI Deep Learning, IOT, Big Data and Machine Learning competencies. It enables clients to stay relevant in a highly connected, rapidly evolving world. This Hyderabad based AI - Deep Learning Product company has forayed its vision, which is an ambitious and comprehensive initiative that aims to make Architect Corner a million dollar company.“We were two software architects who wanted to start a product company.Architect Corner was the name coined by us on the company inception date. We had years of experience in Artificial Intelligence, Big Data and Machine Learning. We observed the pain points in the systems using AI and Big Data. There was lack of learning in the systems. Historical data is available but patterns were not used for prediction. The idea was to come up with product for prevention by prediction,” said Bhagvan Kommadi, founder of Architect Corner.nbsp; Since its inception in 2016, Architect Corner’s commitment has centered on providing the highest quality of AI services while maintaining integrity and empathy to the benefit of all its clients as well as the citizens. Stemming out of this pledge, Architect Corner has delivered projects in Africa and Latin America. With its verticalization strategy and pioneering technology-based innovations, Architect Corner has been growing both organically and inorganically. Architect Corner was one of the startups which participated in the expert discussions for the NASSCOM Indian Start-up report 2016. Architect Corner fostered relationships with Indian government to be part of the Smart City Governance Council which comes under ministry of communications and Information Technology. In addition to lead the significant transformation, the firm is currently working with NASSCOM, Campus Party and 100 Open startups for bringing startups on to a platform of innovation and entrepreneurship.nbsp; Architect Corner is building successful customer base in the area of Hospitality, Banking and Financial Services. Architect Corner is working with CIO of Kansas and CIO of Chicago on Smart city initiatives. The company is working with Public Policy Analyst of USPS on financial and economic initiatives. Architect Corner is partnering with Carnegie Mellon University, Case Western Reserve University and University of Toronto on Smart Cities initiative. They have collaboration with Volpe -National Transportation Systems Center related to CyberSecurity initiatives. Implementing AI Deep Learning platform at Hyatt in 2016, Architect Corner began its journey. Within just one year, Architect Corner has turned out to be a known entity. Architect Corner is working with OECD to promote policies with governments to improve the economic and social well-being of people around the world. The company is also working with Brazil Ministry towards Brazils post Election economic initiatives. “Our company is working with South African Ministry in Urban Planning of South African cities and towns. We are working with Kenyan Government for Joint economic initiatives and raising funds through World Bank. We are in the process of finalizing a pilot with European Smart village initiative in Italy,” said Mr Kommadi.nbsp; Commenting on their unique service, Bhagvan Kommadi said, “The key differentiator of Architect corner from other companies is that the platform will be able to detect and isolate infected or malicious AI programs immediately, and develop the effective policy and laws for governing their development and use, so that government and citizen’s information is safeguarded and not misused. The USP is prevention by prediction in the area of smart cities.” Known to be a result driven professional, Bhagvan Kommadi brings over twenty years experience spanning in the creation of products amp; incubation of Product Startups. He is currently working as Rapporteur of the Smart City Governance Working Group from Architect Corner. He has done Masters in Industrial Systems Engineering at Georgia Institute of Technology (1997) and Bachelors in Aerospace Engineering from Indian Institute of Technology, Madras (1993). When asked about the business problem it hopes to solve with its solutions and offerings, he said, “Artificial intelligence can help government handle their compliance monitoring by creating a natural language processing system to read through the legalities of regulations and reassemble the words into a set of computer- understandable rules. Multi Channel Information Analysis and Social Media Analysis help to providing locational intelligence and information for prevention by prediction. AI can be used to track users’ habits, activities, and behavioral characteristics. Financial data and products can be personalized to meet and anticipate each user’s unique and changing needs. Each user will have one’s own digital personal financial assistant.Artificial intelligence can help banks handle their compliance monitoring by creating a natural language processing system to read through the legalities of regulations and reassemble the words into a set of computer-understandable rules. IoT, AI, VR, AR, and bots technologies are changing the way data is created, collected, interpreted, and communicated. It will be important to be able to detect and isolate infected or malicious AI programs immediately, and develop the effective policy and laws for governing their development and use, so that personal information is safeguarded and not misused.”nbsp; What is the biggest risk that the company has taken? Asked STARTUP CITY.“The risk was to start off with self funding and grow the company by partnerships and product development strategy. Architect Corner has partners in Data Cleansing (Alteryx), IOT Hardware platform (Technosphere), Digital Marketing(Orion) and Communications as a service (QuickBlox) technology areas. ISV partners are in the areas of Graph Database (Neo4j) and No-Sql Database (HazelCast),” he revealed. With associates in London, San Franscisco, Dubai and Iran and headed by Aerospace Engineer Bhagvan Kommadi, Architect Corner takes flight since the year of inception. Now the firm aspires to scale new heights.nbsp; “We are planning to expand into different countries. We hired cofounders to encourage the team members to participate in engineering, sales, marketing and recruitment. The work culture is based onnbsp;very flexible work style.” “We are optimistic that company will be the leader in the AI space in next three years. Architect Corner will achieve around ten million $ revenue in next three years. We plan to have over hundred customers by 2022,” Kommadi added. When its comes to Architect Corner, passionate approach and enthusiasm of the ten-member team are hallmarks that have contributed to grow this firm rapidly. This AI - Deep Learning Product company Architect Corner owes its to the co-founders’ decades old diverse experience. nbsp;

IOT Topologies

Bhagvan Kommadi — Fri, 07 Feb 2025 05:17:47 +0000

General Topology Sensors and devices connect to IOT Communications Networks like M2M (3G/4G), LoRa, SigFox and private local Networks such as Z-Wave, Zigbee, Bluetooth, WIFI and Threat. Smart Grid, Smart Homes, Smart water networks, intelligent transportation, early warning systems are based on IOT communication networks to connect to the world. A Wireless Sensor Network (WSN) is a network of sensor nodes to measure light, heat, pressure and other physical phenomena. There are three kinds of Wireless sensor networks : star, tree and chain. Security, identity, trust management, dynamic characteristics and terminals are important qualities to be considered for wireless sensor networks. Wireless sensor networks protect the information and resources from security attacks like Sybil, DoS and abnormal nodes. There are evolving technologies in the areas of node security, cryptography, key management, authentication, authorization, network security, tamper-proofing, encryption, secure routing and secure data aggregation. Public networks like low-power wide area networks (LPWAN) operate in 800-900 MHz bandwidth and transmit 100-140 messages per day. Small sensors on low-power networks operate on regular batteries for 10-15 years. M2M networks can transmit large data over 3G/4G networks. Global international roaming is allowed on these networks.Local Networks consist of local gateways covering short range distances which allow sensors and actuators. They exchange information using local RF technologies like WIFI or Fixed internet lines, ZigBee,Z-Wave, WIFI, Bluetooth Low Energy (BLE) and Threat. These technologies have a very low duty cycle. Zigbee networks have hundreds of nodes in a network.Bluetooth is limited two nodes and Wi-Fi to 15 devices per access point. Topologies used in IOT deployment networking architectures are Point to Point, Star and Mesh Networks. Each IOT deployment network topology has different attributes and capabilities. Typical attributes which are used for selection are range, power consumption, reliability, scalability, bandwidth, data rate, flexibility, interoperability and cost. A broader solution consists of a mix of networking technologies to address device to device communication. Point-to-Point Network Two network nodes are connected directly in a point to point network. Communication happens between the two nodes or devices. Point-to-point networking is simple and cost is low. Scalability is an issue as it cannot go beyond two nodes. The network range is one hop and the transmission range for a single device. Typically one node is connected through internet gateway or another network which gives access to the users to use the device. Star Network The sensor nodes are connected to one central hub which will be the gateway node in the star network. The hub will be exposed to the external world through gateway node. Central hub is a common connection point for all nodes in star network. The performance of the star network is consistent, fast and predictable. It has low latency and high throughput. Data packet travels one hop to reach from hub and sensor or at most two hops between two sensors. Reliability is high in star network as faults and devices can be isolated. Single link is required for each device and isolation of individual devices is easy. The range of a single device is limited to the transmission range. Single point of failure is the gateway node in star network. Exchange and storage of data internally is possible when the gateway fails. Mesh Network Mesh Network has gateway, router and simple sensor nodes. The gateway node is exposed to the external world. Router nodes act as a relay for other nodes and capture and share their data. Collaboration happens in the network with neighbour nodes for sharing the data. In a mesh network, all nodes are within the transmission range of atlas one router node. Routers share the data packets received to gateway node. Mesh network topology is used for long range and broad area coverage. The network can scale up to thousands of nodes. A high density of coverage with a broad arrangement of sensors and devices can be provided in this network. Mesh network has flexibility to face High Radio Frequency challenges and obstacles. Self healing and packet retransmission features mitigate intermittent network interruptions. This type of network has high degree of network resilience. Recommendation Mesh Networks are more complex than point to point or star network topologies. Network latency is higher in mesh networks. This is because of multiple network hops from sensor to gateway. In a smart building application, the energy usage is analyzed and monitored in the building which consists of floors and rooms in each floor. Monitoring and control points are deployed on each floor. Occupancy, pressure, humidity, gas, light and temperature sensors are installed on each room and floor. Sensor data will be aggregated in the central management centre in the building. The applications in central management centre will monitor, analyse and optimise energy usage in the building. Utilisation patterns are identified from the data collected. Higher the resolution of information related to heat and occupancy provides better insight. Zigbee mesh networking architecture is preferred in the building management applications. The other applications include surveillance, industrial automation, logistics, inventory and asset management. There are many standards for different protocols in the networking of IOT sensors. The different protocols are WiFI, WiFi LP, Bluetooth, Bluetooth LE, Zig bee, Z-Wave , EnOcean, 6LoWPAN and others. Some of the challenges related to the sensors and devices are malfunctioning of sensor or device, improperly provisioned SIM card and wrongly set APN within the device. Network latency, bandwidth, jitter malfunctioning and stability are the quality of service issues related to network technologies. Cost management and growing interconnections are important challenges in the IOT network deployment. There are pool plans to manage cost like Fixed, Flexible and Add-on rate pool plans to manage a set of devices and scale the network with additional devices and sensors. Integrity of corporate network, Access controls, user management, audit and reporting of devices status and information are managed by the standard IOT platforms. There are open source IOT frameworks like Leshan & Wakama (LWM2M implementations), Kura & Mihini (M2M gateways), OneM2M, Eclipse Smart Home and Eclipse SCADA. Messaging Protocols Mosquitto is an open source MQTT message broker service. It uses the MQTT protocol for the device to communicate by sending and receiving messages. Among the message brokers that support MQTT, Mosquitto is a small and light weight implementation of MQTT v3.1/3.1.1. Mosquitto runs well on small compute models like the Raspberry PI and Intel Edison. Arduino IDE can be used for building communication services. Mosquitto is based on Eclipse which is a lightweight server implementation of MQTT protocol. Sensors and actuators are the sources and destinations of MQTT messages. Message Queuing Telemetry Transport (MQTT) is a standard protocol. Mosquitto is a bridge which connects to other MQTT based messaging servers. Bridge has features of passing MQTT messages from source to destination. Mosquitto based application consist of components connecting to a messaging server, subscribing to a topic and publishing to the topic. Andy Stanford-Clark of IBM and Arlen Nipper - past CTO of EuroTech are the MQTT Technology founders. MQTT is used for real time tracking of assets and fleets. The event information can be efficiently sent and received with MQTT and OWA 11 A devices. The other area where MQTT is used are home automation related to power monitoring, lighting control and gardening. Disaster & emergency alerting management, Energy Monitoring, Burglar detection system and Location aware messaging systems use MQTT. MQTT services are deployable on CloudMQTT, HiveMQ, AWS IoT and Azure IoT Hub. Publish-Subscribe pattern is used to publish and subscribe messages to topics. The mosquitto broker will distribute messages to clients subscribed on a topic. JSON and XML are typically used for distributing messages. JSON is a preferred choice for IOT applications. IOT devices use standard protocols. Small devices use MQTT and CoAP as the code footprint is small. CoAP is the constrained application protocol from Constrained Resource Environments IETF group. Message Queuing Telemetry Transport (MQTT) is message-oriented and typical architecture is based on a client/server model. Sensor is a client and it connects to the broker over TCP. Message is published to a topic (an address). Clients subscribe to multiple topics. The publish subscribe model is used by MQTT to communicate one-to-one, one-to-many and many to one clients. The typical scenario in different real life industries is remote sensors sending messages to the central system for processing and central system sending control commands to the devices. Sensors can send the condition of the devices to the server. MQTT enables connectivity to internet for data transportation. MQTT brokers have application level Quality of service types which are At most once Delivery, Delivered at Least Once and Delivered Exactly Once.In the case of At most once delivery service level, response is not expected and there are no retry semantics. Delivered at Least once service level ensures delivery of message at-least once and duplicate message can be there. The message is delivered once and only once in the case of Delivered exactly once. MQTT messages can be persisted on the broker. The most recent persistent messages are stored. Client receives the persisted message when one subscribes to the topic. MQTT brokers do not persist message to back up inside the server. The credentials are required for authenticating clients connecting to the server. TCP connection is encrypted with SSL/TLS to ensure privacy. CoAP is the constrained application protocol for transferring the documents. It is designed for constrained device needs. CoAP packets are easy to generate and smaller than HTTP TCP flows. CoAP does not use TCP but uses UDP. UDP broadcasting and multicasting is used for addressing through CoAP. CoAP is based on the client & server model. GET, PUT, POST and DELETE methods are allowed on CoAP services. It interoperates with HTTP and REST web methods. CoAP can be used on packet-based communication protocols like SMS. Confirmable or Non-Confirmable messages are two types of application-level service quality. Fire and Forget are Non confirmable messages. Content Negotiation is supported by CoAP. Accept options are used by clients to express a preferred representation of resources. Reply with a Content Type option for servers. DTLS capable CoAP devices messages can be transported after encryption using RSA, AES or ECC and AES. Servers can send stream changes to clients using CoAP requests and responses. CoAP client can discover resources along with their metadata. MQTT is many communication protocol used by multiple clients through a central broker. CoAP is one to one protocol between client and server for state transfer. MQTT broker creates a long lived outgoing TCP connection to the clients. UDP packets are used by CoAP brokers for communications. MQTT has no feature to label message or any other management. CoAP has support for content negotiation.

Tactile Internet Platform

Bhagvan Kommadi — Thu, 06 Feb 2025 06:34:31 +0000

The area of focus for my old Startup -Architect Corner was in Next Gen Telecom Platform - Tactile Internet Next Generation Telecom Platform focuses on tactile internet. Tactile Internet provides capabilities to provide physical experiences remotely on wearables, haptic interfaces, tactile interfaces, biometrics, hologram TV, connected cars, unmanned aerial vehicles (UAV), devices, sensors and smart components. NextGen Telecom Platform- Tactile Internet has Disaster Support, Payments, Delivery, Control, Remote Monitoring, Surveillance, Telematics, Customer Mgmt, Order Mgmt, Sensor Network Mgmt, IOT Cloud, Smart home and building, Smart Cities, Payments, Big Data and Enterprise Mobility Products. The emerging opportunities are e-health, e-education, smart logistics, mobile video, mobile corporate services, and car entertainment. e-Banking Solution The solution targets branch recognition, automated investment advice, ATM monitoring, identity management customer interaction by video, cardless payments, asset management system for secured loans and location-based customer care services, and banking products. e-FinTechcover digital treasure trove, personal insurance alert mgmt, business insurance mgmt, investment, insurance linkage, investment accounts amp; health monitoring linkage, augmented reality applications for property information amp; mortgage management, virtual video interaction and tracking employees for personal and travel insurance. e-Grid e-Grid targets energy generation using fuel, gas, electricity and renewables. It has modules for distribution systems, usage systems, and equipment management systems. e-Safecities solution consists of features to support weather amp; emissions, security, traffic mgmt, public transport, electric vehicles, events and emergency mgmt.

Videos for ITS CEO Meet

Bhagvan Kommadi — Wed, 05 Feb 2025 09:09:43 +0000

Please check out the 2016 CXO Meet at ITS Ghaziabad.

https://pg.its.edu.in/cxo-meets-conclave

https://youtu.be/FOhHUnfKOgY

Smart Grid - Internet of Energy

Bhagvan Kommadi — Mon, 03 Feb 2025 05:21:18 +0000

Internet of the energy is a network of devices, energy systems, network plants, generator plants and nbsp;network components. They can exchange information among each other and align & optimize the processes on their own. To take the current energy grid where the information is unidirectional communication to interactive communication, the approach will be evolutionary instead of big bang reengineering. New business models, new players in the market will evolve in the next generation Smart Grid - Internet of Energy. This will consist of various aspects - distributed energy resources,energy storage, electric transportation,network communications,advanced metering infrastructure,distribution grid management and cyber security.

Smart Cities : Digital Security Strategy

Bhagvan Kommadi — Fri, 31 Jan 2025 11:27:43 +0000

Smart Cities : Digital Security Strategy Social media platforms are changing the way organisations communicate the information to the public. There is need for the Communications Group to be timely and proactively aware of the reactions and opinions expressed by the general public as it relates to the country’s center amp; state security and its actions on a variety of subjects. Multi Channel Information Analysis consists of analysis of enterprise and government employees activity and reach amp; spread of information from various channels like email,sms, chat, web, mobile and ftp using pattern recognition and case based reasoning techniques. This is based on the basic premise that the insider is the threat to the enterprise or nation. This will help handle the crisis situations, monitor conversations continuously, identifying and reaching out to key bloggers and influencers. The emerging trends, discussion themes and topics are spotted from various sources like social media, blogs, forums, You Tube, Press news, state based media translated information and subscription based news. Extremism and Terrorism prediction and prevention can be done using data mining of various sources related to online radicalisation, recruitment tools for violent extremist groups, extremism related network, sentiment and opinions. IOT (Internet of Things) helps in security amp; surveillance related to management of assets, traffic, building, emergency systems, drones, airport, seaport, bus stations, sea container, border security, transport, real estate asset and utilities. Digital Security strategy consists of storing and archiving information related to crime, identity history, common frauds, driving records, criminal justice, laboratory, firearms enrolment, biometrics, security clearances and sex offence.

“Indian Root Bridge” - Integration Approach to Connect Systems

Bhagvan Kommadi — Thu, 30 Jan 2025 07:37:02 +0000

Traditional Approach

A typical Integration approach has been to map two interfaces. The interfaces can be system-level, system to datastore, and system to service (asynchronous - protocol agnostic).

Inference-based Mapping - Semantic Services
Semantic web services are web services which have metadata about the service definition. The service request and response fields are described in the metadata. The field information can be used to infer about the services. Inference-based mapping is an approach which can map two interfaces given the metadata about the services. The relationships across the fields can be described using rules, ontological concepts and terms. Service definitions or schemas which describe the interfaces can evolve. Automated Discovery Selection, Composition and web-based execution of services
Semantically Enabled SOA (SESA)
WSMO Web service Modelling ontology has ontology and Rule language for semantic web with support for WSMO. Semantic Execution environments have the capability in deploying and executing semantic web services.
WSML Top Down Process Design Bottom Up

web service description and annotation.
Semantic BPM Semantic Business Process Analysis
Semantic Business Process Modelling
Semantic Business Process Design
Semantic Business Process Execution
Code Generation based Mapping- Standard-based Integration Templates for Interface

Mapping Out-of-the-box connectors can be the templates which evolved from inference engines for mapping two standard interfaces. The templates are stored in a repository for standard interfaces with version information. In Memory Inference In scenarios of translation between two languages, the traditional method is to automate the translation word by word and workflow will be triggered for review. In the new approach, in memory changes during workflow are stored. In memory, inference will help speed translation and bring down the language translation's manual review.

Recommendations

In the data integrations, the fields which describe the customer transaction can be used for loyalty, offers and recommendations. In e-commerce (B2C, B2B, C2C), system interfaces can be integrated using a new approach of inferring the data and using them as recommendations. ETL Extraction of the data and transforming them for loading the data repository is the traditional approach. The new approach evolving is to store the transformations evolved from the inference engines for mapping two interfaces. The transformations are stored in the repository. Transformations can be clustering the data into groups, correlated data transformations, rules, logic and sorting/filtering. Data Relationships The Discovery of structured and unstructured data relationships need to be identified and stored in the repository. The data can be graphs, spatial, text, sensors and location.

Applications

Intelligence
Bio Informatics
Health Care informatics
Finance
Media

Opensource Project Launch : digital-administratione-iura

Bhagvan Kommadi — Wed, 29 Jan 2025 05:33:52 +0000

please check out: https://github.com/bhagvank/digital-administratione-iura

digital rights management using windows media

IOT in the enterprise

Bhagvan Kommadi — Tue, 28 Jan 2025 11:36:50 +0000

IOT in the Enterprise Smart Energy (voltage and power sensors, meters and breakers, and fault detection for the Electric Grid), Smart Transport (ITS, HEVs, electric vehicles) Smart Cities (lighting, water management, monitoring amp; security, and traffic control for Connected Communities) Smart Living (independence through technology, information-when-you-need-it, and connected-when-you-need-it for Entertaining and Leisure) Smart Health (people monitoring, bio-sensors and probes, and remote health for the Healthcare System) Smart Buildings and Homes (thermostats, HVAC, lighting, presence sensors, lockers, actuators, meters, smart-plugs) Smart Planet (environmental sensors, water and power leak detection, and pollution and weather monitoring for a Green Environment) Drones (Integrating artificial intelligence and augmented reality into drones) Hillary Clinton - use of drones for human rights initiative in Arab. A military equipment which is used for destruction is being used for humanitarian purposes. In my life time, it is a rare occurrence that military weapon budget is used to deliver food and medicines to women and children. Other benefits to humanity include search and rescue operations, fire and wildfire control, ecological monitoring, deep ocean surveillance, medical first responders, medical supply transportation, transporting food and water to impoverished areas, disaster relief, Archaelogy, oil, gas and mineral exploration.

Agriculture: Drones will be used in agriculture for targeted weed killing, watering, harvesting, and transportation – resulting in less pesticide use, less water waste, and fresher food. The ability to fly over crops, houses and cities make the drones the perfect platform to gather data that was once too expensive to gather. Indeed, you can count on the local tax assessor to use this technology to spot unreported pools, boats and other items that can be taxed in backyards. Insurance: Commercial and personal-lines insurers that cover property risks are likely to be early adopters of drone technology. For example, a property adjuster or risk engineer could use a drone to capture details of a location or building, and obtain useful insights during claims processing or risk assessments. Drones could also be deployed to enable faster and more effective resolution of claims during catastrophes.Drones hold vast potential for streamlining and reducing the cost of insurance-related processes — from claims adjustment and risk-engineering, to post-catastrophe claims settlements for customers, to weeding out fraudulent agricultural claims Drones can also be used for entertainment. Unmanned objects like vacuums, window washers and robots are a tr end to keep an eye on. IOT Hub : a machine to machine (M2M) system for drones to talk to each other. IoT Hub which allows the user to connect smart devices with and without IP addresses directly to IOT Cloud including: Nest, Phillips Hue lightbulbs, Belkin Wemos, Insteons, and other not-so-smart devices such as serial port devices and RF (radio frequency) devices. Not only does this allow any device to be connected to the Internet but it also allows people to message smart devices without going through the manufacturers’ clouds and apps.

Trends in Enterprise Value Creation with IOT Information of everything. This enables detailed insight into what customers want, and how to connect their needs with the journey planned by an organization for its customers. Shift from the thing to composition. While individual things are perhaps not incredibly interesting, using their connectivity to group them together to measure certain data, or to form new business models and uses, creates almost unlimited potential.

Convergence. Smart, connected technologies dont just bring together things they link and combine people, places, and information: creating new opportunities. Next-level business. The IoT will intrude on established procedures by revealing better ways to measure and operate the organization through new and powerful analytics. REDUCTION IN IOT COMPONENT COSTS A key trend improving enterprise opportunities for IoT is falling system costs at both the device and network levels. Because of architectural similarities, IoT stands to significantly benefit from ongoing advancements in the smartphone market whose large volumes and increasing competition are dramatically reducing prices throughout the value chain. This includes key component parts such as sensors, embedded processors, memory and cellular modems whose steady price declines will continue to drive cost reductions for developing new IoT endpoint applications and thus ease capital requirements for enterprise deployment. Similarly, price reductions continue to occur in cellular communications that support IoT device connectivity. As demand for IoT devices increases, prices for both 3G and 4G networks will continue to decline, which will drive reductions in recurring operational expenses for enterprise solutions. Additionally, expanded use of cloud services in IoT and the benefits of their elastic pricing models will enable cost-effective computational resources that will further reduce cost of ownership and improve the value proposition for enterprise clients. END-TO-END IOT PLATFORMS While component price reductions will play an important role in the growth of user adoption, reducing the complexity of IoT application development and deployment will also be a major driver in the market. Unlike IT, the IoT market is a highly fragmented ecosystem where requirements for enterprise solutions can vary significantly from client to client even in the same market vertical. This includes variability in requirements related to device functionality, communications and applications logic, which require integrating a host of different hardware and software elements. Moreover, added complexities are introduced when integration is required with client systems such as local area networks or core enterprise software, which inevitably will require upgrades that will further complicate the IoT offering. As such, these integration, installation and maintenance complexities establish significant barriers to entry for most enterprise IoT solutions, especially when large numbers of device deployments are required over large geographic regions. As such, the expense associated with development and deployment quickly erodes the solution’s value proposition and return on investment. To address these issues, new forms of IoT platforms will emerge that will drive out the complexity of application and enterprise solution development and radically decrease cost-of-ownership. These will include what I would describe as configurable, end-to-end IoT platforms that will integrate functionality from the edge of the network into to the cloud. Inherent in the architectural design will be built-in features and functions for sensing, communications and business logic, which can be easily configured for new applications, making implementation very simple and cost effective. Moreover, reducing barriers for the delivery of sensor information to the cloud will enable 3rd party application developers an entirely new framework from which to build advanced solutions based on cloud-to-cloud data exchange. Configurable IoT system approaches will continue to tightly integrate new features throughout the network stack with the aim of reducing time to market and cost of ownership for new enterprise solutions. These features will also include advancements in analytics both at the device and cloud levels, which is the basis for the 3rd trend as described below. SENSOR ANALYTICS AND FUSION The business value of IoT at its basic level takes the form of improved situational awareness. This includes a better understanding of the status of remote physical systems in terms of their condition, which drives improved operational decisions, actions and efficiencies. OIL Industry: IOT Data from differential pressure sensors mounted on used oil tanks are processed using analytics at the device level to generate volume status and alert messages, which are communicated wirelessly to the cloud for storage, additional processing and reporting to desktop and mobile devices. In this case, Operations personnel have actionable information regarding a store’s past, current and forecasted volumes of used oil, which enables informed decisions regarding the routing of service vehicles dispatched over large geographic locations. Specifically, this includes the ability to now service automotive clients based on need as opposed to fixed routing schedules, an operational change enabled by IoT that drives significant improvements in oil collection efficiency. Asset Performance : IOT Enabled Data To further eliminate service issues, organizations can analyze and review asset performance data and make necessary design and quality adjustments to ensure that issues dont arise in the first place. Data can also be used to isolate issues to a specific component or supply chain partner in order to improve the speed of resolution and to aid future occurrence. As organizations mature in their use of IoT- enabled data for improved service, they uncover opportunities to leverage the data across other business functions. In our research, nearly 50% of organizations state that remotely captured performance data is shared with engineering and design teams. Other teams such as operations, sales and marketing, and supply chain, are beginning to look at this information to identify opportunities for improvement. Supply Chain Management Manage inventory replenishment levels (currently done minimally) Evaluate partner performance and isolate quality issues to supply chain link (currently done minimally) Disruptive Innovation During the summit, Geoffrey Moore, an expert on the market dynamics of disruptive technologies, noted how IoT rep resents the third in a series of waves of disruptive innovation. “ The first wave – called “Systems of Record,” occurred when we hooked PCs up to the Internet, releasing value in the global supply chain. The second wave – “Systems of Engagement” – unlocks value from mobile devices. Moore believes that we’re now at the very beginning of the third wave, in which IoT enables us to reap value from “Systems of Intelligence.” ——————- IOT Chris Brauer, co‐director of CAST at Goldsmiths, University of London The rich data created by wearable tech will drive the rise of the human cloud of personal data, With this comes countless opportunities to tap into this data; whether its connecting with third parties to provide more tailored and personalized services or working closer with health care institutions to get a better understanding of their patients.” “the public sector will also embrace wearable technology to manage and oversee public health and smart city programs.” Long Term IOT RoadMap Don’t forget the customer value case. Garner executive support for IoT to improve cross-organization buy in. Build the business case internally on enterprise and customer value Start with a small cross-functional team that includes sales, marketing, IT, product design, engineering, and service to evaluate stages of IoT project. Identify immediate customer and quality problems to initiate IoT program Sell the success of remote monitoring to customers and make them aware of the investment Connect organization IT with customer IT in the pre-sale process to allay established data fears. Analyze results against established objectives and scale across business functions IOT - Enterprise Value Framework Business Drivers Accelerating Pace of Innovation Satisfying Customer Demand for new ways of interaction Automation of Business Processes Faster Self Service Faster Problem resolution Quality amp; Reliability improvements Uptime Improvements Technology Drivers New Types of Devices Volume of Data generated Cloud Based Technology Challenges New Threats to data/physical security Inability of IT systems to keep in pace with the change Regulatory or Compliance challenges Need to invest in new technology infrastructure Ability to integrate new technologies with legacy IT environments Ability to update Processes to absorb new technologies System Development/Integration Cost Customers not Willing to Pay Unable to make Internal Business Case Concerns from customers around connectivity Business Benefits Operational Efficiency Asset Utilisation Supply Chain Employee Productivity Customer Service Collaboration within Company Innovation Employee Productivity Business Functions Asset Utilisation Innovation Employee Productivity Supply Chain Customer Experience Internet/Cloud of Things Internet of Nano-Things (i.e. nanomachines endowed with communication capabilities and interconnected with micro- and macro-devices) Pertino CMO Todd Krautkremer believes the Internet of Things should be called the Cloud of Things, since you can only derive its full benefit through cloud computing and storage. Cloud of Things — the reason being that the cloud is really this idea of compute that is reachable across the internet. The really powerful aspect of the whole Internet of Things is the fact that one can harvest all this data. A person can analyze across a lot of data points that are relevant to his/her business through cloud computing and cloud storage, and can get brilliant answers. The Cloud of Things platform enables businesses to develop self-branded Internet of Things solutions quickly and easily. The platform delivers the complete set of elements that constitute an IoT solution – SDKs for endpoint devices, an insight-driven big-data cloud backend and an engine that automatically generates source-code for mobile control applications. The Cloud of Things platform was built from the ground-up to support public-cloud, private-cloud and on-premise deployment models. Thus, in addition to consumer-oriented solutions, the platform enables companies to white-label IoT solutions that were originally built for consumers and use them securely within the enterprise.

Embedded “The Internet of Things is rendering many incumbent embedded engineering technologies and design processes insufficient and antiquated,” Chris Rommel, vice president of M2M and embedded technology at VDC, wrote in a research report accompanying the firm’s survey findings. “Engineering organizations now need new solutions that address these evolving requirements and speed development and time to revenue.”

Data from Embedded Systems Will Account for 10% of the Digital Universe by 2020 The grid of embedded devices that are now interconnected into discrete networks, like industrial control subnets are similar to early networks of the sixties and seventies. These clusters of embedded devices are starting to be equipped with TCP/IP gateways, being driven mainly by connection to the factory IT back-end infrastructure. In this industrial application space, an already intensive M2M connectivity exists, centered in specific control-oriented applications. There is increase in industrial TCP/IP based field-bus protocols, like Ethernet/IP and EtherCAT, driving universal adoption of TCP/IP connectivity all the way to the sensors and actuator control networks. In a few years, the promise of pervasive computing will have reached those sensor networks, allowing sensor information to feed more general applications. From the embedded devices themselves, little more than reliable transmission of their sensor information will be necessary. But from the grid perspective, a much larger cloud of data points will be available, making new applications possible that use that data and run in other nodes on the network. To make this pervasiveness happen, one main requirement is the availability of gateways to these proprietary sub-networks from the larger Internet. This is happening at an accelerated pace, as wireless and wired TCP/IP points are added to these grids, communicating PLCs (Programmable Logic Controllers) with Ethernet and WiFi, and direct connections of end points to the TCP/IP grid, as is the case with the weighing instruments my company manufactures. New applications will emerge: For example, monitoring of micro-weather and seismic activity from data extracted from temperature, humidity, wind speed, and weighing sensors distributed in a number of industrial grids. And real-time monitoring of damages for emergency response team management during large natural disasters, or even war scenarios. Two of the most common uses of an embedded TCP/IP stack are for messaging and web page display. For example, a simple SMTP-based messaging system allows a microcontroller to take sensor readings and, at a pre-determined interval, email or message those readings to a centralized repository for logging and analysis. Such an application in the home might be to email the temperature of the house to residents every hour. Of course, this example can be extended much further. The embedded microcontroller might also monitor whether the doors and windows are locked and the lights are off. The system monitors this information throughout the day. If the resident is going to be home late from work, he or she can use HTTP to request that the micro send a web page with all of this information. The resident could then turn lights on in certain rooms, bump up the temperature, and perform other tasks by changing a few fields on the web page. There are multiple ways to implement an Ethernet subsystem that matches each system’s requirements. Wearable Technologies Alex Choi, Chief Technology Officer at SK Telecom. The 5G future Intel wants can turn us into half-humans, half-Terminators. This means that rather than wearing wearables, the sensors could someday live in or on your body instead Jack Higgins, a digital media lawyer at Sheridans in London Wearable technologies face the same challenges as any other new product that represents a change in consumer activities, albeit that in the digital age the challenges can seem more daunting,. On the one hand, some wearable technologies do not represent any privacy or any other rights issues as they interact solely with the user. On the other hand, when wearable technologies interact with other people and the environment around the user, then potential privacy or copyright issues can arise, although its important to remember that these issues already exist, but the new technology typically makes it easier to happen.” Beyond providing users with real-time data about their health or an augmented view of the world, wearable technologies will form an integral part of the Internet of things, the logical evolution of the cloud and big data. The idea is to enable sensor-equipped things to communicate with one another in meaningful, actionable ways. For that to happen, though, companies need to take care not to scare off would-be users by failing to address their privacy concerns. Wearable technologies like Google Glasses, the Nike+ FuelBand, and Autographer are still in their infancy, but theyve managed to pique the interest of organizations and users alike. The full range of this new form factor for mobile devices is very wide and I would like to define wearables as electronic systems located on the body that mediate their user and their environment. From activity trackers like FitBit and Up by JawBone and other quantified self applications, to more advanced information devices like Google Glass and Samsung Smartgear, these first generation devices are always on and always connected. Next generation devices will also be contextual and intelligent thanks to the Internet of Everything’s convergence of people, devices, data and the web. Advances in inertial sensors, touch and in-air gesture control, gaze control for eyeglasses, speech recognition or natural language processing and more advanced, predictive data analytics are just some examples of what’s possible. As the Internet of Everything drives more connections, predictive data analytics is an important part to making wearable devices more intelligent. And if wearables can speak our language or predict our behavior, they can be of much more help to us, especially since all currently conceivable wearable devices involve limited screen space. With no space for haptic input, we need to be able to have a ‘conversation’ with these devices. Voice and artificial intelligence together should be the main ingredients for wearable computing, and as of now, they are not yet advanced enough. Context Wearables appeal to the idea of human-centric design, but what we need to aim for is human needs-centric interaction. For now, it has mostly been shifting notifications over to another screen. For a glimpse of how contextual information-interaction might work, just look at Google Now. It’s still in its early stages, but is already impressive. It uses my data to get to know my context. For example, it calculates the ideal travel schedule based on person agenda and sends up soccer scores Google knows. With advancement in sensors, the ever-growing amount of data in our ‘personal’ cloud, wearable devices have the potential to be so contextual. There are dozens of useful applications to be made. But we have to stop replacing the smartphone. Instead, we need to make something better. Think services, not devices With the explosion of new types of data, better personalisation practices and more contextual ways to interact with information, the opportunity to create better and new services is here. It’s one thing that a wristband can track activity, but it becomes a smart service. It’s also one thing if a smart watch can display how many messages you received, but it becomes a smart service if it can prioritise those messages and highlight what one need to know based on your context. The hybrid of mobile, social, personal and analytics technologies can offer anyone a more anticipatory and intelligent relationship with their devices. But value and great services are not created in devices, but in the systems in which they exist. In the end it’s not so much about smart things and devices, but about smarter services and people.

Appendix

IOT : Protocol Overview Devices must communicate with each other (D2D). Device data then must be collected and sent to the server infrastructure (D2S). The server infrastructure has to share device data (S2S), possibly providing it back to devices, to analysis programs, or to people. From 30,000 feet, the protocols can be described in this framework as:

• MQTT: a protocol for collecting device data and communicating it to servers (D2S)

• XMPP: a protocol best for connecting devices to people, a special case of the D2S pattern, since people are connected to the servers
• DDS: a fast bus for integrating intelligent machines (D2D)
• AMQP: a queuing system designed to connect servers to each other (S2S)

Each of these protocols is widely adopted. There are at least 10 implementations of each. Confusion is understandable, because the high-level positioning is similar. In fact, all four claim to be real-time publish-subscribe IoT protocols that can connect thousands of devices. And it’s true, depending on how you define “real time,” “things,” and “devices.” Nonetheless, they are very different indeed! Today’s Internet supports hundreds of protocols. The IoT will support hundreds more. It’s important to understand the class of use that each of these important protocols addresses.

The Bottom Line : Key Dimensions of IOT The IoT needs many protocols. The four outlined here differ markedly. Perhaps it’s easiest to categorize them along a few key dimensions: QoS, location, security, privacy and application. QoS (Quality of Service) QoScontrol is a much better metric than the overloaded “real-time” term. QoS control refers to the flexibility of data delivery. A system with complex QoS control may be harder to understand and program, but it can build much more demanding applications. For example, consider the reliability QoS. Most protocols run on top of TCP, which delivers strict, simple reliability. Every byte put into the pipe must be delivered to the other end, even if it takes many retries. This is simple and handles many common cases, but it doesn’t allow timing control. TCP’s single-lane traffic backs up if there’s a slow consumer. Because it targets device-to-device communications, DDS differs markedly from the other protocols in QoS control. In addition to reliability, DDS offers QoS control of “liveliness” (when you discover problems), resource usage, discovery, and even timing. Next, finding the data needle in the huge IoT haystack is a fundamental challenge. XMPP shines here for “single item” discovery. Its “user@domain” addressing leverages the Internet’s well-established conventions. However, XMPP doesn’t easily handle large data sets connected to one server. With its collection-to-a-server design, MQTT handles that case well. If you can connect to the server, you’re on the network. AMQP queues act similarly to servers, but for S2S systems. Again, DDS is an outlier. Instead of a server, it uses a background “discovery” protocol that automatically finds data. DDS systems are typically more contained. Discovery across the wide-area network (WAN) or huge device sets requires special consideration. Perhaps the most critical distinction comes down to the intended applications. Inter-device data use is a fundamentally different use case from device data collection. For example, turning on your light switch (best for XMPP) is worlds apart from generating that power (DDS), monitoring the transmission lines (MQTT), or analyzing the power usage back at the data center (AMQP). Application Of course, there is overlap. For instance, DDS can serve and receive data from the cloud, and MQTT can send information back out to devices. Nonetheless, the fundamental goals of all four protocols differ, the architectures differ, and the capabilities differ. All of these protocols are critical to the (rapid) evolution of the IoT. The Internet of Things is a big place, with room for many protocols. Choose the one for your application carefully and without prejudice of what you know. MQTT MQTT, the Message Queue Telemetry Transport, targets device data collection. As its name states, its main purpose is telemetry, or remote monitoring. Its goal is to collect data from many devices and transport that data to the IT infrastructure. It targets large networks of small devices that need to be monitored or controlled from the cloud.Message Queue Telemetry Transport (MQTT) implements a hub-and-spoke system. MQTT makes little attempt to enable device-to-device transfer, nor to “fan out” the data to many recipients. Since it has a clear, compelling single application, MQTT is simple, offering few control options. It also doesn’t need to be particularly fast. In this context, “real time” is typically measured in seconds. A hub-and-spoke architecture is natural for MQTT. All the devices connect to a data concentrator server, like IBM’s new MessageSight appliance. You don’t want to lose data, so the protocol works on top of TCP, which provides a simple, reliable stream. Since the IT infrastructure uses the data, the entire system is designed to easily transport data into enterprise technologies like ActiveMQ and enterprise service buses (ESBs). MQTT enables applications like monitoring a huge oil pipeline for leaks or vandalism. Those thousands of sensors must be concentrated into a single location for analysis. When the system finds a problem, it can take action to correct that problem. Other applications for MQTT include power usage monitoring, lighting control, and even intelligent gardening. They share a need for collecting data from many sources and making it available to the IT infrastructure. XMPP XMPP was originally called “Jabber.” It was developed for instant messaging (IM) to connect people to other people via text messages (Fig. 4). XMPP stands for Extensible Messaging and Presence Protocol. Again, the name belies the targeted use: presence, meaning people are intimately involved.The Extensible Messaging and Presence Protocol (XMPP) provides text communication between points. XMPP uses the XML text format as its native type, making person-to-person communications natural. Like MQTT, it runs over TCP, or perhaps over HTTP on top of TCP. Its key strength is a name@domain.com addressing scheme that helps connect the needles in the huge Internet haystack. In the IoT context, XMPP offers an easy way to address a device. This is especially handy if that data is going between distant, mostly unrelated points, just like the person-to-person case. It’s not designed to be fast. In fact, most implementations use polling, or checking for updates only on demand. A protocol called BOSH (Bidirectional streams over Synchronous HTTP) lets severs push messages. But “real time” to XMPP is on human scales, measured in seconds. XMPP provides a great way, for instance, to connect your home thermostat to a Web server so you can access it from your phone. Its strengths in addressing, security, and scalability make it ideal for consumer-oriented IoT applications. DDS In contrast to MQTT and XMPP, the Data Distribution Service (DDS) targets devices that directly use device data. It distributes data to other devices. While interfacing with the IT infrastructure is supported, DDS’s main purpose is to connect devices to other devices. It is a data-centric middleware standard with roots in high-performance defense, industrial, and embedded applications. DDS can efficiently deliver millions of messages per second to many simultaneous receivers.Data Distribution Service (DDS) implements a publish/subscribe architecture. Devices demand data very differently than the IT infrastructure demands data. First, devices are fast. “Real time” is often measured in microseconds. Devices need to communicate with many other devices in complex ways, so TCP’s simple and reliable point-to-point streams are far too restrictive. Instead, DDS offers detailed quality-of-service (QoS) control, multicast, configurable reliability, and pervasive redundancy. In addition, fan-out is a key strength. DDS offers powerful ways to filter and select exactly which data goes where, and “where” can be thousands of simultaneous destinations. Some devices are small, so there are lightweight versions of DDS that run in constrained environments. Hub-and-spoke is completely inappropriate for device data use. Rather, DDS implements direct device-to-device “bus” communication with a relational data model. RTI calls this a “DataBus” because it is the networking analog to a database. Similar to the way a database controls access to stored data, a data bus controls data access and updates by many simultaneous users. This is exactly what many high-performance devices need to work together as a single system. High-performance integrated device systems use DDS. It is the only technology that delivers the flexibility, reliability, and speed necessary to build complex, real-time applications. Applications include military systems, wind farms, hospital integration, medical imaging, asset-tracking systems, and automotive test and safety. DDS connects devices together into working, distributed applications at physics speeds. AMQP Finally, the Advanced Message Queuing Protocol (AMQP) is sometimes considered an IoT protocol. AMQP is all about queues (Fig. 6). It sends transactional messages between servers. As a message-centric middleware that arose from the banking industry, it can process thousands of reliable queued transactions.The Advanced Message Queuing Protocol (AMQP) is messages-centric middleware that arose from the banking industry. AMQP is focused on not losing messages. Communications from the publishers to exchanges and from queues to subscribers use TCP, which provides strictly reliable point-to-point connection. Further, endpoints must acknowledge acceptance of each message. The standard also describes an optional transaction mode with a formal multiphase commit sequence. True to its origins in the banking industry, AMQP middleware focuses on tracking all messages and ensuring each is delivered as intended, regardless of failures or reboots. AMQP is mostly used in business messaging. It usually defines “devices” as mobile handsets communicating with back-office data centers. In the IoT context, AMQP is most appropriate for the control plane or server-based analysis functions.

Enterprise Messaging Platform

Bhagvan Kommadi — Fri, 24 Jan 2025 15:48:16 +0000

Messaging Platform Introduction Messaging Product is envisioned as a messaging platform for communication between consumers. It can be extended to B2C and B2B depending on the context. The messaging platform can be white labelled to be embedded in another product or standalone for specific brands or influencer promotions. The product can be used as a secured messaging platform for verticals which are concerned with customer privacy like Hospitals, Clinics, Health Insurance. Inter practice communication in those verticals can happen on the platform and integrated with Electronic Health Record system. The professionals in a vertical can use this platform for sharing information. Products The Product can be used to sell products like games, emotions, tones and coupons. It can be used in businesses for recruiting and conducting interviews. It can be a collaboration tool for daily updates of tasks in enterprise business teams. Consumer Identity The consumer identity can be avatar, mobile phone number and QR code. The personalisation features will be platform settings, defaults, profile and favorites. The person’s mood, attitude and preferences are part of the context of the messaging platform. The message channel can be voice, video, content, mobile and web. The messaging platform will have features for chatting, voice calling and video conferencing. Message Context The message context can be private amp; secure. It can be a conversation or sharing a topic. The context can be extended to a brand, offer, poll, events and interviews. A temporary discussion which is created dynamically with contacts (not stored) can be the context of the messages. The messages can be shared by a person to socialise with another person alone or in a group. The conversation can be hidden to a third party or current engaged business that the person is in. Groups Groups can be created on the platform. Groups can be students, parents related to a school. A social network can be a school, fan club, non profit groups, women safety and academia. The location based messages or subscribed topics can be configured in the platform. The location can be used for picking the contacts to send messages. Circle of Influence The sender and the receiver’s distance is defined in a social network on the circle of influence. On the circle of influence from a sender, the distance can be the the radius of the circle drawn from the sender to the receiver. The person in the conversation can be distant from the other person on the circumference of the circle. The effectivity of the message sender or the conversation is measured by how far the network is spread and how many are in the network. Highest on the social network is termed as Influencer measured by the number of followers. The number of following in the social network is a measure of how much you are influenced by the others. The competition between the persons in the network is to start a live conversation to attract people in the network. The goal of a person is to become an influencer. Topics A Topic can be subscribed by the user on the messaging platform. Topics can be fashion trends, politics, elections, lifestyle, tourism, ecommerce portals, fan clubs, fitness, movies, entertainment amp; event calendars, spiritual and religion. Music news amp; releases, sports commentary, celebrity gossips can be topics started by the specific publishing businesses. Cost amp; Profit The social distance is on the circle of influence from the best influencer in your network. The cost of sending messages is the content creation and the profit is the branding of the product in the network. Tools will be provided in the messaging platform for content creation. Add ons can be provided free or based on cost for content creation in the platform. The engagement of the persons in a topic or a conversation is the measure of the effectivity of the content topic. The cost of a topic and topic content will be the content creation cost for the publishing business. The profit is measured by the brand building and the social network engaged in the topic. The engagement is measured by the number of people in the topic network and the number of influencers listening to the topic. Consumer Behaviour Consumer expects data usage need to be optimal when the person is messaging for a topic or a conversation. Privacy will be a major concern. The person will be concerned for battery of the device. The archival process of the messages need to factor in privacy. The person sending messages is measured by the frequency of messaging. The person’s influence in the social network is determined by the probability of the message being read by the receiver. The probability of the topic being conversed upon is measured by the number of messages subscribed in that topic by the person and frequency of his/her sending messages. Shake of the device can be interpreted by the intensity of the shake. The intensity can decide the alerts for police, security or known family contacts nearby. Consumer now days wants to move out of social media platforms into the messaging platform. The person is also interested in posting moments to be shared through social media platforms. Social media platform can be a channel for the messaging platform. Smart Home and Smart Cities The messaging platforms are evolving from conversational tools to Smart Living and Lifestyle Remote control devices. The platforms are enhanced to control a hotel room for lighting, temperature and security. Smart cities have features on these platforms for weather check, traffic, coastal monitoring, building maps, augmented reality apps for airports, seaports, railway stations and bus stations.

Self Aware & Self Defending Networks

Bhagvan Kommadi — Thu, 23 Jan 2025 14:09:22 +0000

Self-Aware, Self-Defending Adaptive Network is a network that protects itself from security attacks in smart cities implementation.An intelligent agent that learns and understands the threat level posed by attack across a smart city network. The AI system uses a new form of machine learning to monitor every detail of a network to identify and isolate security threats. The threats are such as malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. An autonomic system is composed of ensemble of autonomic components which are dynamically added and removed. Nodes within such an ensemble should cooperate to achieve system goals. These systems are expected to self-adapt with little or no human-interaction. Self-adaptive network modifies its own behavior in response to changes in its operating environment. By operating environment, we mean anything observable by the network, such as user interaction, network devices and sensors, or instrumentation.

Introduction

Adaptive network based on 5g with AI is used for automated service provisioning. The network providers automate manual service lifecycle processes. These process are automated using in packet/optical networks. Packet/Optical network are built using an software defined networking based automation platform. The automation platform is multi-layer and multi-vendor based which adopts DevOps processes. For instance, a network provider can automate the delivery of its wavelength services and plans. They can automate to extend this platform to other services using a phased approach.

Proactive network assurance is another area where AI based adaptive network can be used. The network providers want to identify and correct as many network issue that they can foresee and predict. This helps in increasing network reliability and deliver with specified SLAs. AI based adaptive platform improves the customer experience. This platform will have features related to pre-emptive network maintenance across the optical, Ethernet and IP Wide Area Networks. AI based automation platform will have the network health prediction capabilities. Along the same lines, Machine learning based analytics can predict the likelihood of a network node’s failure in a given timeframe for repair.

AI based Adaptive network can help in Fiber capacity analysis and optimization. Policy based matching of channel and wavelength capacity improve the efficiency and adaptive planning of optical networks. Providers can predict signal variability by combining real-time network telemetry data and traffic forecasting with AI based predictive analytics. This helps in improving the system margin utilization and reduce cost-per-bit.

AI-Defined Infrastructure

AI Defined infrastructure can manage planning, build, run and maintain tasks. In Planning tasks, AIDI is used for analyzing the demand trends and predicting the infra requirements. Using the requirements, planning can be done appropriately. We can also ensure the infrastructure is according to the requirements.

In build task, the necessary resources can be deployed as per the workload requirements. Resources can be deallocated when there is no need. The infrastructure components can be configured easily. In the run and maintain tasks, AIDI can be used to analyze the data patterns. The data patterns help in indicating the behavior of the system. The behavior of the system helps in making the model of the system behavior. AI based training helping in building this model with quality parameters. The quality parameters which are used for the model are availability, scalability and storage.

The anomalies in the network can be identified by the AIDI based platform. intrusion detection, fraud points, fault points, infrastructure abuse and failure are the anomalies identified. The platform can detect the threat and act to rectify and fix the problem. It has features to react or proactively act based on the single or group of infrastructure components. Errors can be fixed completely by autonomous actions. AIDI helps in reducing the cost of IT infrastructure. The cost is reduced by using the most optimal components.

Networking Challenges

Network providers are now rethinking about their operations with Artificial Intelligence. AI can be used to achieve the long desired goal of end-to-end automation. Automation might remove humans from the equation. The network providers want their networks and operations to become adaptive. This is to respond to a changing competitive landscape and consumer demands. These demands require a coherent combination of human-controlled and supervised automated operational processes. They might also need analytics-driven intelligence, and a programmable infrastructure.

The evolution to 5G and IOT adoption is putting massive pressure on today’s networks. There is need to increase the capacity by orders of magnitude. On the related front, the networks need to have the ability to respond to unpredictability in traffic patterns.The optical network which is at the heart of communications helps in interconnecting people, data centers, and devices in the network. The network need to meet today’s web-scale demands.

Operators are having challenges in handling bandwidth demands. They are managing the demands by deploying, managing, and sparing different hardware. They are using cost-optimized solutions per specific application. They select the hardware based on the best-guess fiber characterization data. Lack of network visibility and efficiency is forcing operators to operate at suboptimal capacity. These factors are making the operators lose revenue resulting in costly network overbuilds.

Adaptive Networks - 5g

Adaptive Network platform based on AI will have three important components which are software control, programmable infrastructure and analytics driven intelligence. The software control forms the basis of adaptive operations. The basis is supported by the automated creation and deployment of network services. These network services are deployed for scale and speed using software defined network, Network functions virtualization and open APIs.

The programmable infrastructure is a hybrid future generation network. It comprises of open, software defined network enabled physical networks and cloud-native virtual network functions. It will provide advanced telemetry that delivers real-time data on the health of the network. The programmable infrastructure will provide the ability to match the changing capacity needs.

Another component named Analytics-driven intelligence enables intelligent automation. The intelligent automation enhances autonomous decision making and supporting software-control. They can achieved through policy management, rule engines, AI, machine learning and telemetry. We need to have a robust storage repository. The repository will record, process and aggregate real-time and historical large-scale and raw data streams. The data streams such as log files and telemetry data are recorded in the repository. The raw data will be processed, normalized and used for advanced data models and analytics algorithms. These algorithms are used to generate actionable insights.

Network providers will apply different kinds of machine-learning techniques. These techniques are based on the operational use cases and benefits. The techniques used are supervised learning, reinforced learning and unsupervised learning. Supervised machine-learning based algorithms are trained to identify patterns such as degrading network performance. They can also be used to predict an outcome like port failure and trigger remediation actions such as auto-adjust network bandwidth and add new capacity. This technique is commonly used and suitable for use cases in which historical data and outcomes are known. Reinforced learning involves continuous calibration of these algorithms based on previous feedback on actions. Unsupervised learning algorithms use grouping and clustering techniques to organize data. This helps to understand the structures and enable the discovery of patterns. The patterns discovered are related to previously unknown and unnoticed scenarios such as identify new user, service traffic behavior and profiles. These patterns are used to improve forecasting in network planning.

Self-Aware, Self-Defending Adaptive Network

Self-Aware, Self-Defending Adaptive Network system is intelligent agent based and monitors network activity, content and behavior. Network activity, content and behavior based information is used identify and counteract different forms of cyber threats.

The intelligent agent framework learns and understands the threat level posed by node in a network. The adaptive network platform uses machine learning techniques to monitor the network to identify and isolate cyber security threats. The security threats identified are malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. It enables make all assets self-aware, self-protecting and adaptive to any threat which is external or internal. This approach eliminates the chance for zero-day attacks. This is because, the platform can detect anomalous packet behavior and content. The self learning adaptive network system learns through use and becomes intelligent over time.

The self aware, self defending adaptive network system can recognize every packet’s behavior and content. The behavior and content is used to determine if the pattern conforms to expectations or is anomalous. This helps in deciding to check and find if it is a threat. This adaptive and associative network detects the relationship of every byte in the system. It is capable of identifying known threat patterns. It can identify and isolate anomalous patterns. The anomalous patterns which are identified might be related to a zero-day attack, non-compliant use of the network or a sabotage.

To illustrate a self learning adaptive network, we look at human neuronal network of the neocortex. This biologically inspired intelligent framework operates like a human brain. The human brain can learns autonomically by detecting patterns at the moment of stimulation. The adaptive network stores each unique byte pattern like human brain. Each time the pattern is detected, the values such as time of stimulation, place of stimulation, syntax of patterns, packet payload and addressing are stored. The stored data model has an n-dimensional representation of the semiotic value of every pattern.

Intent Based Networks

Intent Based Networks are used to capture the intent related to business. This helps in bridging the gap between business operations and IT. The benefits of intent based networking are related to the ability to automate network management and orchestration in an intelligent manner. They represent a solution set which is the confluence of a key technologies such as machine learning, Artificial Intelligence, Software Defined Networking and Internet of Things technologies.

Conclusion

Cognitive Network Management is the emerging network infrastructure technology. Network operators have deployed and operated network on 5g with existing Self-organizing Network technologies. Artificial Intelligence mixed with Software Defined Networking and advanced analytics, takes the autonomous, intelligent network operation and control to next level. While AI has not yet made extensive inroads into the realm of networks, cellular systems in particular will soon achieve early and extensive success through the combination of Machine Learning, SDN, and advanced data analytics technologies.

The potential economic and social benefits can be achieved because of self-awareness, self-configuration, self-optimization, self-healing, and self-protection. We might realize these benefits during the implementation of 5G networks. The network systems can be integrated with technologies such as Machine Learning, Software defined networking, Network Function Virtualization, Network Slicing, Quality of Service Management, and emerging security methodologies.

Today’s networking challenges

Network providers are now able to rethink their operations with AI to achieve their long-desired goal of end-to-end automation, but most of them do not want to cede control to networks that decide their own direction and remove humans from the equation altogether. Most network providers want their networks and operations to become more ‘adaptive’ to respond to an ever-changing competitive landscape and consumer demands, which requires a coherent combination of human-controlled and -supervised automated operational processes, analytics-driven intelligence, and an underlying programmable infrastructure.

Network operators know it all too well: Streaming video, cloud computing, the Internet of Things (IoT), and the evolution to 5G are putting massive pressure on today’s networks, requiring capacity increases by orders of magnitude and the ability to respond to even greater unpredictability in traffic patterns.

The optical network sits at the heart of communications, interconnecting people, data centers, and an increasing number of devices across any distance, from across the street to across the ocean. Yet, for all the critical functions and agility they need to provide to meet today’s web-scale demands, most networks are weighed down with manual processes and hardware inflexibility.

Operators are working to keep up with bandwidth demands by deploying, managing, and sparing different hardware for different areas of the network using cost-optimized solutions per specific application. They select their hardware based on upfront link engineering determined with best-guess fiber characterization data. Lack of network visibility and little hardware flexibility limit network efficiency, forcing operators to operate at suboptimal capacity, leaving revenue on the table and resulting in costly network overbuilds. With the speed at which technology shifts are occurring in the industry, using this operating model is no longer an option.

New technologies promise to drive discontinuity in both cost and power reduction. Higher baud rate, programmable, coherent technology can reduce transport costs and put operators within reach of their business goals. But if operators cannot get accurate, real-time link data from the network to determine the right channel capacity rate at any point in time, they cannot take advantage of the savings associated with the new technology.

What if the network could self-monitor and adapt to application demands in real time, adjusting capacity across paths as needed based on traffic requirements and system margin? Evolving to such an autonomous network would drive new levels of efficiencies and speed in achieving business goals.

autonomous network

lays the foundation for tomorrow’s self-driving, autonomous networks. With AI, operators can take advantage of improved transport economics, gain new insights and control into the efficiencies of their network, and use new levels of automation to compete and win in the new on-demand economy.

For the operator to be able to optimize network capacity, the system itself must be able to monitor and gather critical information in real time. Using deep, systems-level expertise, platform has embedded unique, real-time link monitoring capabilities, never before available, into Ai. Operators can now understand exactly how much margin is currently present in the network, as well as the optimal capacity they can deploy.

Introducing a new paradigm for optical networks, i provides operators with new visibility into and control over the efficiency of their networks. For the first time in optical networks, users will be able to access real-time link monitoring information to determine the optimal capacity for each channel—across any path, for any network fill—and tune to match that capacity with a single technology that can address any application, from metro Data Center Interconnect (DCI) to trans-Pacific submarine.

Adaptive Network

An adaptive network is a network that can self-configure, self-monitor, self-heal and self-optimize by constantly assessing network pressures and automatically reallocating resources, but is bound by the rules and policies set by the network provider and is under constant human supervision. These programmable networks will use continuous learning and optimization to dynamically adapt to changing service demands and traffic patterns; help network providers to reduce costs by enabling high levels of process automation and enhanced, AI- and machine learning (ML)-led and assisted decision making; and provide a high-quality customer experience with more predictive and proactive operations and differentiated SLAs.

AI-driven autonomous networking is a high-profile topic among network providers and their suppliers. AI/ML will be central to providers’ efforts to create more-agile and leaner network operations, but that alone will not solve providers’ critical networking challenges. An adaptive network embraces AI with human oversight and uses it in combination with a software-control layer and a programmable infrastructure (see section 3). These networks will put providers in control, rather than asking them to relinquish control to AI, by allowing providers to decide the strategic direction; set the constraints on the autonomous decision making with rules and policies based on business and operational objectives; and supervise autonomous processes and intervene as necessary. Overall, they will enable providers to complement human intelligence with artificial intelligence, where the strengths of one will compensate for the weaknesses of the other, and respond quickly and cost-effectively to customer demands and the competitive landscape.

Automating networks and their operations is not a new idea; providers have been pursuing the autonomous network vision for years. Many providers have adopted various tools and technologies to apply software- controlled, automated processes in various operational scenarios to certain degrees, such as for fault/alarm management, traffic management and RAN optimization, and more recently for Layer 1–3 service provisioning with software defined networking (SDN). However, these have typically been tactical solutions that have been implemented as disjointed, fragmented ‘automation islands’ for specific domains and services. Therefore, the overall level of automation in operations remains low, a long way from the desired goal of end-to-end automation and simplification across multiple networks and services.

Network providers are now presented with the opportunity to change this. The main building blocks of an adaptive network, such as SDN/NFV-based software control and automation, enhanced analytics-driven intelligence with AI and machine learning, and more-programmable network infrastructure (see Figure 1 and section 3 for detailed discussion of these components), are now available. They will enable providers to adopt a more strategic, embedded automation approach to make the network truly adaptive.

The Adaptive Network is a new approach that expands on autonomous networking concepts to transform the static network into a dynamic, programmable environment driven by analytics and intelligence.

Since the introduction of the first Public Switched Telephone Network, networks have continually evolved. Through the various stages of development—from fixed endpoints in the early Internet to today’s broadband networks that connect mobile users to massive data centers and bandwidth behemoths like Netflix, Amazon, and Facebook—networks have adjusted to accommodate new demands.

The once-static infrastructure is undergoing a more profound transformation than ever before. The latest incarnation is autonomous networking, which is a trend that has been building for some time. The autonomous network runs without much human intervention. It can configure, monitor and maintain itself independently.

But, even though it’s a significant advance, autonomous networking is still too restrictive and too rigid. So platform has defined a new approach to the evolution of networking—the Adaptive Network—that’s geared toward providing a network that can grow with a company as its business needs and markets change.

The Adaptive Network is remaking the network into a dynamic, programmable infrastructure built on analytics and automation.

The Adaptive Network allows providers to evolve their current infrastructures into more of a communications loop that relays information from network elements, instrumentation, users, and applications to a software layer for review, analysis, and action—rather than bogging down the network itself.

The Adaptive Network includes three important layers:

Programmable infrastructure: This includes the network’s physical and virtual elements, as well as the telemetry gathered from them. The programmable infrastructure layer is highly intelligent and interprets data so the network can make decisions—whether that means routing traffic around a circuit that's down or investigating and correcting an issue with latency or lower-than-expected capacity on a specific link. Programmable infrastructure requires a flexible grid; a reconfigurable photonic layer to give the ability to reroute channels of variable spectral occupancy across any path, and across any optical spectrum in the network; and telemetry from the IP layer correlated with routing data. In addition, a programmable infrastructure needs tunable coherent transponders to efficiently map a flexible number of client signals to the variable line capacity. In turn, that requires a centralized purpose-built Optical Transport Network (OTN) or packet switching architecture.

Analytics and intelligence: The programmable infrastructure produces significant amounts of data. Some of it is big data that indicate trends that the network learns and adjusts for over time. Big data can inform the network on how to adjust in the long term, which traffic patterns to look out for, and which parts of the network could be vulnerable. Then there’s small data—things that are happening at a fairly rapid pace. It could be a flicker on a circuit or an immediate request from a customer. Such events require a speedy response from the network, and those moves will be made by the analytics. But once the decisions have been made, a human operator or pre-defined policies could step in and approve or change things as necessary. In a truly autonomous network, there would be no operator influence at this point.

Software control and automation: Research shows the undisputed number one cause of network outages is human error, with estimates as high as 32 percent, according to Dimension Data's 2014 Network Barometer report. Effective automation of network tasks, such as loading access controllers and provisioning routers, or automated calculation and configuration of TE tunnels to optimize traffic and relieve congestion, can eliminate those errors and keep the network running at peak performance. The ability for automation to work across multiple vendors is critical. Some technologies are good at working with one set of devices from a single vendor, but few networks are built on a single vendor’s gear. Networks have to interoperate, using APIs, to function efficiently and move data efficiently and swiftly from point to point.

The development of the Adaptive Network is a watershed moment for the networking world. It’s a cohesive evolution that supports all aspects of intelligent automation—such as intent-based orchestration, analytics, and programmable domain control. It’s a microservices-based architecture that delivers extensibility and scale. Plus, it takes a DevOps integration approach to provide operational and service agility.

The Adaptive Network is a new approach that expands on autonomous networking concepts to transform the static network into a dynamic, programmable environment driven by analytics and intelligence.

Self-Aware, Self-Defending Adaptive Network Appliance Software (SASDANAS) system that acts as an intelligent agent to monitor network activity, content and behavior to augment the capacity of human analysts to identify and counteract all forms of cyber threats.

SASDANAS is an intelligent agent that learns and understands the threat level posed by every byte-pattern across a network. The software system uses a new form of machine learning to monitor every detail of a network to identify and isolate cyber security threats – including malware, application high-jacking, sabotage and illicit access, hacking and unauthorized use. It enables the Air Force to make all cyber assets self-aware, self-protecting and adaptive to any external or internal threat. The approach eliminates the opportunity for zero-day attacks because it detects all anomalous packet behavior and content. Furthermore, SASDANAS provides the Air Force with a first-mover advantage as the system learns through use and thus becomes more intelligent over time.

SASDANAS is a 64-bit multithread, massively parallel application that is deployable through a REpresentational state transfer (REST) architecture. Each instance of SASDANAS may be deployed in series and/or in parallel. This architecture provides the USAF the greatest degree of flexibility when deploying into field operations. This approach enables the USAF to use SANDANAS in either: a) moving-windows approach to read every packet as it flows across the network; or, b) identifying threats by capturing an image of the topology of network at byte- or packet-level of detail to understand the behavior and content of network. Each instance of SASDANAS will have the capacity to understand up to 18 exabytes of data at a time. Speed of SASDANAS is dependent on available memory and processing capacity. When deployed in parallel, SASDANAS has the theoretical capacity to monitor the activity of the entire Internet.

Unlike current approaches to cyber security, SASDANA uses a new technology called a HoloSemantic DataSpace (HSDS) to detect, classify and store every byte pattern. The HSDS is thus able to recognize every packet’s behavior and content to determine if the byte-pattern conforms to expectations or is anomalous and therefore subject to further scrutiny to determine if it is a threat. The HSDS is an adaptive, associative network that detects the relationship of every byte that is fed into the system. Thus, the HSDS is capable of identifying both known threat patterns while concurrently identifying and isolating anomalous patterns that may signify a zero-day attack or non-compliant use of the network (e.g., sabotage).

The HSDS is a newly discovered form of neuronal network that mimics the neurophysiology of the neocortex. It is commercially trademarked as a “biologically inspired intelligence” and operates similar to a human brain. It learns autonomically by detecting byte-patterns at the moment of stimulation. The HSDS stores each unique byte pattern only once regardless of how many times it encounters that specific pattern. It registers and adjusts the semiotic generic zoloft cost value for each byte pattern each time it is stimulated – adjusting the size of the net automatically. It determines the semiotic value for each byte pattern with the following dimensions, each of which may have many values: time of stimulation, place of stimulation, syntax of surrounding byte patterns, and packet payload and addressing. Thus, the HSDS creates an n-dimensional representation of the semiotic value of every byte-pattern; thereby capturing every detail within the complexity of data.

Cognitive Network Management represents one of the most important emerging network infrastructure opportunities. Network operators have made great strides with existing Self-organizing Network (SON) technologies. However, Artificial Intelligence (AI), in combination with Software-Defined Networking (SDN) and advanced analytics, is poised to take autonomous, intelligent network operation and control to an entirely new level. While AI has not yet made extensive inroads into the realm of networks, cellular systems in particular will soon achieve early and extensive success through the combination of Machine Learning, SDN, and advanced data analytics technologies.

The potential economic and social benefits cannot be overstated as networks will achieve and entirely new level of self-awareness, self-configuration, self-optimization, self-healing, and self-protection. This will be a benefit for existing networks as well as evolved LTE, emerging Internet of Things (IoT) systems, and soon to be launched 5G networks. An interdisciplinary approach will be required for systems integration as many technologies are brought together including Machine Learning, SDN, Network Function Virtualization (NFV), Network Slicing, Quality of Service Management, and advanced security methodologies.

Intent Based Networks capture business intent, and in so doing, bridge the gap between business operations and information technology. The benefits of intent based networking are many and varied, but arguably all improvements stem from the ability for IBN to automate network management and orchestration in a proactive and intelligent manner. Accordingly, intent based networking represents a solution set that is at the confluence of a few key technologies including machine learning (and other forms of AI), SDN, and IoT technologies.

This research evaluates intent based networking including comparison of IBN with traditional networking in terms of architecture, capabilities, and benefits for carrier and enterprise networks. The report analyzes technologies, infrastructure, and the impact of implementing intent based networking in conjunction with other emerging technologies such as Multi-access Edge Computing (MEC), 5G, AI, and IoT. The report also evaluates leading vendors, strategies, and solutions.

.An adaptive network will be based on hybrid, programmable infrastructure comprising physical and virtual network resources across the WAN and provider data centers that are managed and orchestrated by the software control layer.
Traditional provider WANs are highly stable and complex networks with multiple layers, protocols and services. In particular, optical transport networks are typically configured statically and engineered using worst- case, full-fill, end-of-life conditions. However, rapid and unpredictable growth in capacity requirements and competitive concerns require using network assets as fully as possible with minimal stranded capacity. Customer demands, too, push the need for more flexible, on-demand connectivity services enabled by more dynamic transport network architectures. Providers need infrastructure that can self-configure and self-optimize to meet the demands of existing services (cloud services, high-quality video, mission-critical enterprise services) and rapidly adapt to make way to future services (5G, network slicing, IoT/M2M).

Use cases:

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ADI enhances a SDI with the necessary sophisticated algorithms, machine learning and artificial intelligence – fueling the SDI with intelligence. An ADI allows a SDI to build and run self-learning respectively self-healing infrastructure environments. Thus, without human interaction AI-defined IT infrastructure environments are capable of:

deploying the necessary resources depending on the workload requirements as well as de-allocating the resources when they are not needed anymore.

constantly analyzing the ever-changing behavior and status of every single infrastructure component and thus understanding itself.

reacting or proactively acting based on the status of single infrastructure components by autonomously taking actions and thus leading the entire infrastructure into an error-free status.

An AI-defined infrastructure cannot be compared with classic automation software, which typically works with predefined scripts. An ADI utilizes a company’s existing knowledge executing it automatically and independently. However, like every new born organism an ADI needs to be trained but afterwards can work autonomously. Thus, based on the learned knowledge, disturbances can be solved – even proactively for not expected events by connecting appropriate incidents from the past. Therefore, an ADI monitors and analyzes all responding components in real-time to identify and solve a problem based on its existing knowledge. The more incidents are solved the bigger the infrastructure knowledge gets. The core of an ADI is a knowledge-based architecture that can analyze incidents and changes and autonomously develop strategies to solve an issue.

Furthermore, an AI-defined infrastructure embraces communities to:

consume the knowledge from external experts to become more intelligent.

connect with other ADI environments to link, combine and share their knowledge base.

constantly expand the knowledge pool.

optimize the knowledge.

All in all, an ADI is an intelligent system that – initially fueled with external knowledge – can learn and make decisions autonomously without human interaction.

ADI is only a single piece of the entire AI-defined enterprise stack

An ADI is an essential part of today’s IT operations building the foundation for the AI-enabled enterprise. However, first and foremost it enables IT departments changing the infrastructure behavior from a today’s semi-dynamic to a true real-time IT environment.

This autonomous way of planning, building, running and maintaining the entire infrastructure let IT operations and developers deploy IT resources like server, storage, network, databases and other ready services in the most efficient way – by using the knowledge of more than just one expert but the entire IT operations team. Furthermore, IT operations are being transformed from pure consumers of resources to orchestrators respectively managers of a completely automated and intelligent IT stack. The very foundation of an end-to-end AI-ready enterprise.