Geolocation analysis for Search And Rescue systems using LoRaWAN

Abstract

Summary Low‐power wide‐area network (LPWAN) technologies are aiming to provide power‐efficient solutions to the field of Internet of Things (IoT). Over the last years, we have seen a significant development within the area of IoT applications. For many applications, the problem of localization (i.e., determine the physical location of nodes) is critical. An area study of such use case is also the rescue monitor systems. In this study, we start by describing a solution designed for the long range wide area network (LoRaWAN) to localize position of IoT modules such as wearables used from vulnerable groups. Through performance study of the behavior of a LoRaWAN channel and using trilateration and RSSI information, the localization of an IoT wearable can be acquired within a small range. Routing people in need is one of the use cases the above mechanism could be integrated so as to be able to be tracked by familiar people. After that, we evaluate the usage of mathematical model of multilateration algorithms using time difference of arrival (TDoA) as a solution for positioning over LoRaWAN. The research is carried out using simulations in Python by configuring the constant positions of the Gateways inside an outdoor area. The proposed algorithms can be integrated in application for tracking people at any time and especially routing people from vulnerable groups. Through multilateration and algorithm's prediction, we can have an accuracy of 40–60 m in location positioning, ideal for search and rescue use cases. We finally summarize the above algorithms' estimation and general behavior in a SAR system.

A Comparative Study of Machine Learning Models for Spreading Factor Selection in LoRa Networks

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