DEV Community: Safreena

RFID versus GPS versus BLE: Which one is the right choice for asset tracking?

Safreena — Fri, 16 Oct 2026 12:40:04 +0000

The decision you make about the kind of asset-tracking technology to buy should be determined by your asset's location and your company's demand for location data.

The tags can be used to identify a tagged object and to verify an inventory in certain checkpoints, for example, when goods are arriving to the warehouse or leaving it. But RFID can't continuously track the location of an object.

GPS (Global Positioning System) is a good method of tracking vehicles and mobile equipment in open areas. Its usefulness will be affected when used indoors, or where the satellite signals are blocked.

Energy) may assist in locating assets within the facility or their general location. Precision of the technology will rely on receiver density, building design, and system setup.

For instance, a company could rely on RFID to check tools as they exit the building, a GPS system to track the transport vehicle, and BLE to assist in the search for the tools when the vehicle arrives at the destination. Using multiple technologies offers greater insight, but also expenses more in hardware and upkeep.

Before choosing a solution, consider three important questions:

Q. Are assets kept inside, outside, or between the two?
Are you after checkpoint based inventory reconciliation or more frequent location updates?
Will the benefits out weigh the costs of tags, readers, gateways, connectivity and maintenance?

Test a core set of representative assets, or a smaller implementation, to realize potential limitations and gaps.

Here is another resource you can use to learn more about asset identification and location tracking. Asset Track Pro https://assettrackpro.com/

What has been most successful in your operation? Is one technology good enough, or do you use multiple solutions for inside and outside?

Building a Digital Thread for Aerospace Manufacturing with AIoT

Safreena — Mon, 28 Sep 2026 17:30:25 +0000

When it comes to aerospace manufacturing, the data conundrum is one that many underestimate.

A single flight component goes through numerous steps in its creation and integration into an aircraft – from fabrication and inspection to environmental testing and storage – and information about the component, equipment, process, inspection, environment, and more can be generated during each.

The issue is not that this information should be captured, but rather, how to connect this data into a reliable digital thread.

What is a digital thread?

A digital thread is a connected information chain about a given physical asset.

By linking information related to production, quality, inventory, and equipment into a shared set of records (rather than having this data siloed in various systems), engineers and operations managers can have a greater degree of situational awareness about what has occurred to a component, when, and which process or system may be involved.

For example, component genealogy – linking a given part to manufacturing, inspection, material, equipment, and other production records – can provide this much-needed situational awareness when questions about a particular component arise months or years after its initial production.

But what role does AIoT play in all of this?

Industrial IoT provides a means of capturing information about the physical environment, while analytics and AI offer a means of deriving operational intelligence.

Depending on the given manufacturing environment, this can take a variety of forms, such as:

• RFID to identify components or assets

• BLE and UWB to determine location

• Environmental sensors to capture conditions in controlled environments

• Telemetry from equipment to derive information about its operation

• WIP tracking to observe production

• PLM, ERP, or MES data for linking manufacturing records

The point is not to create yet another dashboard, but to think at the level of the enterprise: can information derived from physical manufacturing processes be tied to the digital records about them?

For instance, WIP tracking can indicate the progress of a sub-assembly through a production process, while component genealogy can store information about that component’s history. By linking these data sets, operations managers can obtain a greater degree of situational awareness about their production process.

The importance of traceability

Aerospace manufacturing is a complicated affair that requires significant engineering know-how to configure a product correctly and ensure that it meets quality standards. As such, the ability to capture information about a component as it goes through the production process and store that information digitally can be extremely beneficial to situational awareness: if questions arise about a particular aspect of a component (e.g., a non-conformance), connected production records can help identify the root cause much faster by pointing to the relevant process, inspection, equipment or other factors involved.

That is why the digital thread is so interesting: it represents a way to connect the life-cycle of a component digitally.

For a more in-depth look at how aerospace and space manufacturing can employ AIoT technologies, see this manufacturing resource.

The engineering challenge

The challenge that engineers face when trying to implement a digital thread is similar to many other aspects of industrial IoT: there is a need to connect disparate systems, capture relevant data in a standardized manner, and establish a shared understanding of things like component identity or temporal metadata. For instance, when it comes to component genealogy, some pertinent questions include: What information needs to be captured at each stage of the production process? How should component identity be standardized across systems? Where should processing of sensor data occur? How can legacy manufacturing systems be connected to more modern IoT-enabled ones? How can the integrity of data be maintained without creating unnecessary data entry and processing overhead? What information needs to be available in real-time, and what can be processed periodically?

As such, an appropriate architecture for this kind of application would need to address the needs of the physical manufacturing process as well as the digital thread linking these processes.

After all, from the perspective of an aerospace manufacturer, the ability to connect component information from various stages of the production process is not likely to be determined by any one particular sensor, tracking technology or analytics model, but rather the whole.

https://aeromfgai.com/

It makes one wonder what engineers working on manufacturing systems would identify as the most challenging part of building a digital thread: data collection, system integration, ensuring data quality, or maintaining temporal and asset integrity throughout the component’s life-cycle?

Aerospace #AIoT #IndustrialIoT #DigitalThread #Manufacturing #AerospaceEngineering #Traceability

Why Automotive Smart Factories Need More Than Connected Machines

Safreena — Sat, 26 Sep 2026 08:41:27 +0000

Modern automotive manufacturing facilities generate data from PLCs, robots, RFID readers, RTLS devices, MES platforms, machine vision, AGVs, and production stations.

The challenge is not in collecting this data, but rather, in transforming it into meaningful operational context.

This is where AIoT (Artificial Intelligence of Things) comes to play.

From Automotive Data to Operational Context

Let’s take a single vehicle that is being assembled in a factory.

A RTLS system may know its physical location while RFID may identify the part or asset.

The MES may know what production stage the vehicle is in while machine or PLC data may indicate what is happening at this workstation.

Each system may provide a piece of the puzzle, but only by combining these data sets into the single operational context we can truly understand what is happening on the factory floor.

A typical data architecture may leverage OPC UA for connectivity, MQTT for lightweight event and telemetry messaging, RFID and UWB RTLS for seeing where assets are, BLE for connected devices and worker telemetry, Edge for smart data processing and MES/ERP integration to bring production and business events into this context.

The ultimate goal is not just to connect more devices, but rather, is to enable new levels of context-aware operations.

Understanding Context, Not Just Collecting Data

For instance, if we know about a vehicle’s VIN, the location data may enable us to understand where is this particular vehicle or part heading to.

But, when combined with production data, this information can help us answer more interesting questions such as: is the asset where it is supposed to be at this stage of the process?

Do we have the right tools and materials ready for it?

What production stage are we at?

What events have occurred?

Which systems need to be notified of the change?

Here we can see how various technologies such as RTLS, RFID, industrial telemetry, and manufacturing software, can complement each other rather than existing in complete isolation.

Architecting the Solution

For the developers who work on industrial systems, the question is of not just collecting more data, but really, about delivering more context.

This means making decisions about the data architecture, event processing, edge technologies, system integration and interoperability, industry-specific data models and many other considerations.

AIoT can enable the needed level of contextual intelligence, but it is up to developers to ensure that devices, data, applications, and operational workflows form a coherent whole.

In automotive smart factories of the future, the emphasis will be not on simply making more machines connected, but rather, on making sure the data that is being collected is actually useful for operational analysts, managers, and workers.

AI #IoT #AIoT #Manufacturing #Industry40