GPS-free geolocation using LoRa in low-power WANs

Abstract

Abstract-Internet of Things (IoT) has been growing over the last few years in multiple applications and due to the high need GPS for tracking capabilities, an innovative opportunity arises. This paper reports on a design and implementation of a LoRaWAN tracking system which is capable exploiting transmitted packages to calculate the current position without the use of GPS or GSM. This is done using the low power technology LoRa where the geolocation is calculated applying a multilateration algorithm on the gateways timestamps from received packages. The whole system consisted of an end-node, four gateways, a server and a java application to store the obtained data in a MySQL database.

Joint range extension and localization for low‐power wide‐area network

“…This additional information may be combined with various information from other sources such as the Time delay of arrival (TDoA) to improve the accuracy of the localization of LoRa devices. Indeed, traditional LoRa localization techniques based on TDoA lack in precision when multipath is present and rely on complex fine‐grained synchronization …”

“…Indeed, traditional LoRa localization techniques based on TDoA lack in precision when multipath is present and rely on complex fine-grained synchronization. 4 Regarding leveraging spatial diversity in LPWANs, previous work has proposed combining signals from an end device arriving at several gateways in the cloud in order to improve the SNR and thus increase the range of the uplink. The approach described in this paper, supporting a second antenna at the gateway allows to improve the range in the uplink direction, while saving bandwidth between gateways and the cloud infrastructure.…”

Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm

“…Although their study reported positioning error exceeding 300 m, it was meaningful in that it showed that TDoA-based positioning was possible with LoRa signals. Fargas et al reduced the positioning error to ~100 m by adopting an iterative algorithm in their method [3].…”

“…We adopted a Maximum Likelihood Estimation approach to estimate the locations of the end-device [11,12,22]. We can define a posterior probability Pr(l i |o * ) and likelihood probability function Pr(o * |l i ) as given by Equation (3). Pr(l i ) is the prior probability, and we set Pr(l i ) as a uniform distribution for experiments.…”

“…However, GPS chips are rather expensive and have high power requirements, making them unsuitable for long-range and lowpower IoT devices. For example, Fargas et al found that a device equipped with GPS and Global System for Mobile communication (GSM) chips consumed around 30-40 times the power consumed by a device equipped with only a long-range (LoRa) chip [3]. Furthermore, GPS functions and communication functions need to be provided separately, making it difficult to miniaturize devices and greatly increasing their production costs.…”