Relying purely on the cellular network using 4G LTE and 5G device-based positioning can eliminate the hardware and software complexity in IoT device tracking and location.

The billions of IoT devices deployed in our homes, vehicles, factories, office buildings, public spaces, and smart city infrastructure are driving the need for tracking and location services on a massive scale. Determining the location of countless things, such as smart meters, medical wearables, shipping containers, and industrial robots, with pinpoint accuracy is a necessity in today’s era of IoT connectivity. The IoT use cases for end-to-end location services are expansive and mission-critical, ranging from logistics and transportation to manufacturing and energy.

Today’s location services typically rely on global navigation satellite systems (GNSS) and are sometimes assisted by Wi-Fi, Bluetooth, Cell-ID and enhanced Cell-ID, as well as other cellular-based technologies. However, to fully leverage the enormous potential of IoT tracking and location services, we need a better solution that reduces the cost and complexity of the hardware and software needed to achieve accurate positioning while minimizing the amount of data transmitted from device to cloud to ease security risks.

The traditional methods for location-based services have significant limitations in certain scenarios. For example, GNSS typically only works outdoors, Wi-Fi is often unreliable or unavailable, and Cell-ID and enhanced Cell-ID and other cellular-based services can be complex and costly to deploy. Some or all of these solutions are required to provide accurate positioning.

For example, a typical wireless-enabled IoT device might use an LTE modem for data, a global positioning service (GPS) sensor for outdoor location, and a Wi-Fi or Bluetooth connection for indoor location. This resulting complexity of multiple wireless devices and technologies requires dedicated hardware and drives up power consumption, which contradicts what IoT is designed to achieve: design simplicity, ultra-low power consumption, tiny form factors, and very low cost.

A better approach is to eliminate this hardware and software complexity by relying purely on the cellular network itself using 4G LTE and 5G device-based positioning.

Device-based positioning does not rely on cloud, GPS, Wi-Fi, or Bluetooth

LTE/5G device-based positioning relies on the same basic principles governing cloud-based location, but since the end-user device determines the location, the technology does not rely on the cloud and virtually eliminates latency and security/privacy concerns associated with device-to-cloud connectivity. The reduced amount of data transmitted to/from the network is also greatly reduced, saving significant power and reducing data usage costs for the end user. There is no need for GPS, Wi-Fi, or Bluetooth connectivity because device-based positioning uses existing LTE signals, which reduces the bill of materials, system cost, device size, and power consumption while improving battery life (see figure 1).

For example, a device-based positioning technology called hellaPHY from PHY Wireless uses location-specific signals standardized in 3GPP Release 9. Known as positioning reference signals (PRS), this standards-based technology is designed to deliver the highest possible levels of accuracy and coverage while reducing interference. These terrestrial signals are typically 50 dB higher in signal strength than satellite-based GPS signals.

Wireless carriers can broadcast PRS signals infrequently to maximize their spectrum use for data and other services. These signals are typically used to meet FCC requirements for Enhanced 911 (E911) services. It is a common belief that low-bandwidth IoT devices can provide good accuracy if the frequency of PRS signals is increased significantly beyond the very low density used for wideband use cases such as E911.

Device-based positioning technology requires only a very limited amount of PRS signal bandwidth, approximately 0.625 percent, to achieve near-GPS performance. Because the solution is a service enabled and provided by network operators, device-based positioning allows them to fully exploit the potential of location information and extract increased value from their network assets. Rather than simply using PRS for applications such as E911, operators can address the large and growing number of commercial opportunities that require both very low power and high position accuracy.

While GNSS has potentially greater accuracy than device-based positioning, this advantage is often moot because accurate indoor coverage can only be achieved when combined with another location technology. In addition, not every scenario requires the highest level of location precision, so the 50-meter accuracy of LTE-M technology is usually adequate for many use cases. As the wireless industry transitions to 5G and as small cells proliferate, particularly in private wireless deployments, device-based positioning solutions have been demonstrated to achieve location accuracy as precise as one meter.

Device-based positioning is based on the premise that avoiding interactions with the network saves power, so by shifting decisions to the network edge and allowing low-power edge processors to make decisions autonomously, the device can decide what needs to be transmitted immediately and what data can be stored and forwarded later.

By minimizing network and cloud interaction, this approach enables massive IoT scaling with 300 times less data required than possible with other cellular positioning technologies. This approach also opens the door for a wide array of applications that may require geofencing or breadcrumbing, which would be power prohibitive to achieve with cloud-based technology.

A device-based positioning system consists of multiple functions, as shown in figure 2. The network operator’s base station almanac (BSA) database contains the cell parameters defining the network layout, and each cell in the database is characterized by a unique cell identifier (ECGI), which includes the latitude and longitude of the cell transmission point, a physical cell index, antenna information, transmission power, and other parameters. A cloud assist server interacts with the BSA database to provide the end-user equipment with a small subset of the operator BSA based on the proximity to the serving cell of the end-user device.

Multiple location fixes can be obtained with a single micro-BSA, and once the data is downloaded, the device requires minimal additional interaction with the network. The host modem provides the onboard hellaPHY software with the necessary information to fully calculate the device location. To enable the maximum extended battery life, the PRS capture is performed during 3GPP low power extended discontinuous reception (eDRX) idle mode or power save mode (PSM).

The software determines cells from the micro-BSA to perform measurements for optimal location accuracy, and reference signal time difference (RSTD) measurements are performed using advanced time-of-arrival (TOA) and filtering algorithms. The hellaPHY LOC consists of position estimation algorithms tailored to the challenging multipath cellular environment that process the TOA measurements and various quality metrics to arrive at an estimate of the user location. These components are tightly coupled for fast and efficient derivation of accurate position estimates.

To illustrate this process, figure 3 compares three positioning solutions for low-power wide-area network (LPWAN) IoT applications. On the left is the assisted-GPS approach (Device A), in the middle is the cloud-based cellular approach (Device B), and on the right is the device-based location solution (Device C). Each device in this analysis employs LTE-M baseband for data connectivity on a cellular network. LTE-M low power features include PSM and RRC Idle discontinuous reception (DRX). The analysis assumes that the PSM nominally draws 0.01 mA and RRC Idle DRX draws 2 mA. When connected to the LTE network exchanging data in RRC Connected mode, the LTE-M modem is assumed to draw 150 mA.

Device A performs measurements on satellite transmissions, and Devices B and C perform measurements on terrestrial LTE cellular signals while also using 1 ms of PRS transmitted every 160 ms. The 3GPP specification allows higher density PRS, but it is assumed that the mobile network operator is deploying low-density PRS to prioritize data capacity.

Device A performs the position estimate on the device using the GPS receiver, which is optimized for precise timing measurements, position calculation updates and filtering. This tight coupling between algorithms results in accurate position accuracy. Device B performs measurements on the device and uploads these measurements to a cloud

server where the position estimate is performed.

There are some fundamental issues with this approach. For example, uploading the position measurements to the cloud consumes power that reduces battery life, and separating the position measurements on the device from the position calculation in the cloud can degrade performance. Finally, storing the location information from millions or even billions of devices in the cloud invites intrusion by hackers.

The Device C solution overcomes the issues with Device B by using device-based positioning. The interplay between the measurements, the position calculation, and filtering efficiently improves location accuracy. In terms of expected location accuracy, A-GPS remains the gold standard for outdoor locations where the device has clear visibility of the sky (e.g., satellites) with an accuracy of about 5 m. But as noted earlier, indoor coverage is limited and often unavailable.

Device B is expected to have indoor and outdoor position accuracy of more than 100 m, which is not as accurate as outdoor A-GPS but nevertheless useable for many IoT applications and has the benefit of indoor coverage. Device C will have better performance than Device B, with an accuracy of 50 m, based on trial results from a Tier-1 network operator that compared hellaPHY on LTE-M. Device C also has the advantage of supporting both indoor and outdoor coverage and offers a significant advantage in terms of longer battery life.

Although the history of positioning through cellular networks dates back more than three decades, it has typically been used to meet regulatory requirements for 911 applications. However, the emergence of the internet of things and Industry 4.0 has introduced a broad range of performance requirements for accuracy, low latency, availability, reliability, security, and many other factors.

Traditional solutions for achieving accurate positioning require the use of GNSS, Wi-Fi, and even Bluetooth beacons. These multiple technologies result in higher levels of network complexity, system cost, and power consumption that cannot be easily accommodated by tiny, battery-powered IoT devices. The optimal solution is to eliminate design complexity by leveraging only the signals used for network communications on LTE in 5G networks to enable device-based positioning.

Performing positioning entirely on the device consumes the least amount of DC power to extend battery life (figure 4) and employs the host device’s inherent high levels of security while offering the highest accuracy of all location technologies relying on LTE/5G signals. hellaPHY device-based positioning solutions are 60 times more power-efficient than GNSS.

Device-based positioning software also requires significantly less memory than other approaches, is scalable to any type of IoT device, and is very spectrally efficient. Location services powered by device-based positioning not only meet the demands of current IoT use cases but also enable the next generation of LTE/5G-connected devices to offer even greater position accuracy.

Steven Caliguri, founder & vice president of strategy at PHY Wireless, has over 25 years of experience guiding telecom and wireless companies. He was the founding executive for Leap Wireless, a Qualcomm spin-off and has extensive M&A background, transforming strategies into profitable businesses. He holds a BS in Physics from Boston College, MSEE from Northeastern, and MBA from Boston College.

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