How Real-Time Location Data Elevates IoT to the Next Level



IoT technologies have gained a lot of popularity in the past few years and have become an integral part of many people’s lives. From our houses to our workplaces, everything can be measured, counted, connected, and automated.

Globally, the number of connected devices is expected to exceed 70 billion by 2025. Hardware, software, and service companies are heavily investing in enterprise IoT across a broad spectrum of industries, including healthcare, education, manufacturing, logistics, energy, travel, and automotive. Enterprise IoT is driven by the increasing adoption of mobile devices, as well as the growing availability of high-speed broadband (such as 5G), edge computing for real-time control systems, and cloud capabilities for storage, computing, analysis, and business intelligence applications for distributed enterprise IoT.

As more devices become connected, the volume, diversity, and complexity of available data will also continue to increase. While data generated by IoT devices enables organizations to make informed decisions, it’s usefulness and potential is hindered if the location of the devices is unknown. This is where location data and RTLSs fit into the equation.

Graphic of an AGV/AMR

What is a RTLS?

To effectively understand how location data elevates IoT devices, let us begin with a definition: a real-time location system (RTLS) is a system that accurately determines the location of an object, such as a machine, vehicle, tool, or worker. In addition to accuracy, the other key aspect of a RTLS is that it communicates this information in real-time through automatic and continuous feedback with little to no latency. As such, a RTLS solution can provide enterprises with immediate information on where something is and/or has been, and in some instances, what it has done, via constant communication between the system and the object.

With RTLS, anything being tracked has a tag attached to it that can be located by the system’s infrastructure. From an architectural perspective, RTLS applications consist of a transponder, a receiver, and software.

  • Transponder: Examples include RFID tags, BLE beacons, UWB tags, and smart devices
  • Receiver: Examples include access points, satellites/anchors, readers, and location sensors
  • Software: May include location engine software, a middleware, and application software

Location Data and IoT Devices

Internet of Things devices can be defined in many ways. Fundamentally, they are electronic devices that perform the following four functions: connect, collect, analyze, and transform. Increasingly, IoT devices use AI and machine learning to bring intelligence and autonomy to systems and processes. Several industries and use cases benefit tremendously from the harmony of RTLSs with IoT devices, such as smart manufacturing, warehousing, and healthcare. Consider a large-scale manufacturing facility where mistakes and delays could amount to significant losses in revenue. In order to operate at maximum efficiency, managers need to have an accurate, holistic overview of their facility.

Through their strategic utilization of intelligent IoT devices and sensors, smart factories gather and analyze a wide variety of data, such as temperature, air quality, pressure, machine vibrations, and much more. This data is centralized through IoT gateways, where it is forwarded to applications for further processing.

On its own, this data does not paint the whole picture. Large-scale, chaotic, fast-paced manufacturing facilities equipped with IoT devices and sensors also require the addition of meaningful location data in order to maximize the utility of their IoT devices. In environments where parts frequently move from one station to another, machines (autonomous and human-controlled) navigate across indoor and outdoor areas, and manufacturing processes entail many critical steps, having a RTLS can remove human error, speed up processes, and increase outputs.

Merging IoT devices with RTLS not only enhances operational efficiencies and output, it also promotes increased safety for workers through location awareness. A few other benefits of combining IoT devices with RTLS include:

  • Detecting bottlenecks in processes
  • Automating inventory routines for assets
  • Generating usage metrics for machines, such as forklifts – allowing managers to assess whether machines are being optimally used
  • Automating updates for location-based procedures, such as vehicle maintenance
  • Identifying areas prone to backlogs and/or unplanned downtime
Picture of a woman controlling a drone

The Missing Tool

The location data generated from RTLSs supplements the data generated from IoT devices. However, there is still one missing component – a middleware that aggregates and manages all of the location data from the locating technologies. This is needed in order to make sense of the data and to wield it in a manner whereby it informs and drives decisions. Our DeepHub, the premier omlox hub implementation, achieves this and thereby enables a plethora of diverse use cases, such as asset tracking and tracing, collision control, shop floor security, AGV/AMR navigation and management, etc.

The DeepHub is a high performance, lightweight middleware that is capable of being deployed on the edge and/or in cloud environments. It can run on a wide spectrum of devices, including IoT gateways, large data centers, low-level PCs, and devices on the edge (such as AGVs/AMRs, handheld sensors and readers). This robustness and flexibility is critical to maximizing the value and efficacy of the data generated by RTLSs and IoT devices.

By harnessing the interoperability of the omlox standard, an IoT platform or solution powered by the DeepHub is guaranteed to be technology and vendor-agnostic. A facility manager could connect all of their IoT devices to locating technologies (such as UWB, BLE, Wi-Fi, RFID, 5G, or GPS) from different manufacturers, which can all use the same infrastructure. The DeepHub ingests location data from locating technologies deployed within indoor and outdoor areas and transforms it into standardized, georeferenced coordinates. Furthermore, through the creation of zones and geofences, events can be triggered for actions such as entry/exits, motions, and collisions – enabling a highly-granular degree of insight into the location and behaviour of devices.

Final Remarks

While the prevalence of IoT in smart homes and workplaces continues to rise, organizations are realizing the benefits of combining their IoT devices with RTLSs. This opens the door for new use cases in several verticals, such as asset tracking in healthcare, space allocation and utilization in smart buildings, and route optimization for AGVs/AMRs in warehouses.

Even though the focus of this article was predominantly on real-time scenarios, the points presented are also highly relevant for applications that do not necessarily require real-time location data. For example, combining IoT devices with Auto ID technologies, such as RFID, enables several key use cases, including asset identification and tracking, access control, material flow, etc.

Managing all of this data in a centralized and standardized manner is paramount in order to effectively make strategic decisions based on findings from the data. The DeepHub, utilizing the omlox standard, makes this a reality.

Get in touch with us to learn more about the DeepHub and to schedule a demo!