LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

Abstract

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution.

Remote Pedestrian Localization Systems for Resource-Constrained Environments: A Systematic Review

“…INS systems which commonly integrate sensors such as accelerometers, gyroscopes, magnetometers, and barometers were used in 18% of the studies [62], [64], [66]- [68], [70]. These navigation sensors were used only to complement other technologies such as GNSS [62], [64], [66]- [68], [70] or Wi-Fi [66] as stated in EC3. This is because of the progressive accumulation of errors over a period of time during motion, and it also gives a relative position.…”

“…Even though the authors who used custom devices had an opportunity to redesign and meet the targeted points as identified from different mounting points adopted, only 26% mentioned their mounting points. The most common mounting points for commercial devices were hands [62], [66], pockets [70], bracelets [46], and clothes [47], [64], but in addition to these mounting points, the authors who used custom devices explored different points like a collar [44], [48], [49], [58], animal ear [109] and walking sticks [38]. Not knowing the mounting points makes the adoption of these systems challenging, especially with the group of people and environment being dealt with.…”

Lessons Learned from a Distributed RF-EMF Sensor Network

“…This area was delineated by taking the 98% intervals (i.e., between the 1st and 99th percentiles) of the samples' (x, y)-coordinates. Moreover, because GPS coordinates have an inherent uncertainty, they were mapped (or 'snapped') to the streets using the algorithm described in [16] (example shown in Figure 4). Unfortunately, there were almost no mobile measurements in the area covered by fixed sensors (Figure 3b).…”

LoPATraN: Low Power Asset Tracking by Means of Narrow Band IoT (NB-IoT) Technology

“…Regarding unlicensed technologies, both LoRaWAN and Sigfox have been tested for asset tracking purposes, with different feedbacks though. Indeed, LoRa [ 14 ] and LoRaWAN [ 15 ] proved to be viable solutions to achieve decent tracking: however, the limitation concerning the use of this technology comes from the requirement of having a distributed network infrastructure, whose capillarity has to be the largest possible to localize assets also in remote areas. For this reason, solutions available in literature only focus on asset tracking in limited areas, while the lack of a global infrastructure remains the biggest limit to a large scale use for tracking purposes within supply chain context.…”