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Bhagvan Kommadi
Bhagvan Kommadi

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IOT in the enterprise

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.

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