# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
## 1 Introduction  
Localization is a vital research subject that is gaining popularity and being applied in different IoT location-based applications, such as tracking. Moreover, various wireless technologies are being applied in localization. WiFi [1], Bluetooth [2], and Zigbee [3] are primarily applied in localization systems for indoor environments and only limited to a few meters. Furthermore, technologies based on satellites are the pioneers in the accurate localization systems for outdoor applications, with errors less than 4 m for Galileo's Open Service and 10 m using GPS [4]. Satellite-based technologies provide continuous global coverage and play significant roles in a wide range of applications. However, Global Navigation Satellite System (GNSS) modules consume high energy, which rules them out for many low-power IoT end devices. Additionally, incorporating many stand-alone sensor nodes in dense sensor network applications with GPS modules would not be efficient due to high hardware cost, high power consumption, and failures due to non-line-of-sight communications [5]. Time-of-Arrival (ToA), RSSI ranging, and Time-Difference-of-Arrival (TDoA) algorithms are among the most researched range-based localization techniques in IoT and Wireless Sensor Networks (WSN). Trilateration or Triangulation approaches are used to calculate the location coordinates of the end device using multiple anchor end nodes with known location coordinates [6], whereby LoRaWAN gateways may be referredto as anchor points. A propagation model and the RSSI are used to compute the distance between receiver and transmitter in RSSI ranging. However, multipath and shadow fading lead to the loss profile with indoor RSSI ranging. A scheduling algorithm with accurate end devices' time synchronization is required for ToA, whereas accurate gateways time stamps for at least three distant gateways are only required for TDoA and make it more suitable for IoT end devices. The calculation of an end device location is made when at least three distant gateways received the same transmission signal from the same end device as, ideally, the signal trajectories to the gateways intersect at the location of the end device. However, any slight scheduling error may lead to inaccurate location predictions. The traditional Low-power Long-Range (LoRa)-based localization systems use TDoA [7] and fingerprint algorithms [8], all with limitations in dense, large urban areas and still under investigation. TDoA performance is poor in dense urban areas, though it performs well in open areas [9]. Fingerprinting localization uses end devices' RSSI fingerprint values received by gateways to localize any end device in the network accurately. A fingerprint-based approach consists of an offline training phase and an online phase [10]. Data samples are collected from a considered service area where localization is to be predicted and used to train a fingerprint algorithm. The location of any end device can then be predicted using the trained algorithm in an online phase based on end-device RSSI characteristics. However, all existing fingerprint algorithms also have various challenges; among others are changing environments that affect fingerprint maps, fingerprint databases that require manpower to be created, and the complicated infrastructure layout, particularly in urban areas. Additionally, different RSSI fingerprint Low-Power Wide-Area Networks (LPWAN) such as LoRaWAN-based approaches using different machine learning algorithms and Artificial Neural Networks (ANN) are also present in the literature [11]. Furthermore, RNN has been used to develop robust models in different applications with considerable accuracy [12]. However, RNN applied to localization systems is a research topic yet to be fully explored. This research applies RNN to develop a low-power, large-scale localization system using LoRaWAN RSSI values to predict unknown 2D X and Y coordinates.