# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
## 3 Related Work  
Several TDoA-based localization systems using LoRaWAN IoT networks are available in the literature [10; 15]. An acceptable accuracy of less than 100 m in most of the published work was reported considering fixed nodes and gateways as anchor points on a small area. However, the performance degraded significantly when mobile nodes or large areas were considered. [PERSON] et al. in [16], used an iterative TDoA-based algorithm to locate static nodes and obtained a good accuracy with an error of around 100 m. A median error of 200 m was reported by [PERSON] et al. in [7] by using a TDoA-based algorithm that considered map details such as roads. [PERSON]. [PERSON] et al. in [17], via extensive simulations, used two gateways with combined TDoA and Angle of Arrival (AoA) using probabilistic algorithms. Their simulation results reported a mean error of 548 m with TDoA and managed to reduce the mean error to 399 m by combining the TDoA estimate with a single AoA estimate. Furthermore, range-based algorithms do not work in indoor scenarios due to multipath as a result of complicated radio environments. Hence, RSSI fingerprinting localization techniques also explored and proved to be potential candidates for harsh environments, including both indoor and outdoor dense urban areas. Different algorithms using RSSI fingerprint localization are available in the literature using various technologies; nevertheless, most of the present works investigated indoor scenarios because of many data needed for the training phase and tedious work in accumulating enough data for a large area. Wi-Fi has been used in fingerprint localization by different researchers [18; 19; 20; 21], whereby a smartphone may be used to record its RSSI and calculate its location using the web. The authors in [22] compared the performance analyses of different wireless technologies based on RSSI localization. Furthermore, the authors in [23] used satellite images for LoRa-based outdoor fingerprint localization and achieved a median error of 47.1 m.  
Different research works in the literature have evaluated ANN methods for sensor localization and confirmed them to be effective [24]. In addition, the authors investi  
Figure 1: LoRaWAN architecture [13]. gated localization models using ANN for Low-Power Wide Area Networks (LPWAN) in [25], and they confirmed ANN as an objective approach, mainly for dense IoT networks. Furthermore, various studies are available in the literature on using ANN to develop LoRa-based [8; 26; 27] localization models with high accuracy. Different RSSI fingerprint LPWAN-based approaches using machine learning algorithms, particularly LoRaWAN, are also present in the literature [11]. [PERSON] et al. in [17] conducted a comparative performance analysis of various machine learning algorithms for RSS-LPWAN-based localization models. The random forest regression method had the highest accuracy with a mean estimation error of 340 m, and the k-Nearest Neighbour (kNN) method had a similar accuracy with the least computational performance. [PERSON] reported an error lower than 20 m in [28], while analyzing localization in ultra-narrow band IoT networks. Similarly, RSSI fingerprint localization methods based on deep learning algorithms have also been published in the literature [29; 30; 31; 32]. Moreover, [PERSON] et al. in [33] reported a root mean square estimation of error less than 9 m with a Long Short-Term Memory (LSTM) method compared with the accuracy of Random Forest and ANN methods. Furthermore, RNN algorithms have been used in various applications including Heating, Ventilation, and Air Conditioning (HVAC) systems [34; 35; 36]; nonoccupied buildings' energy prediction [37]; image pattern recognition [38]; and intrusion detection systems [12; 39; 40] all with significant results. Moreover, according to the results of [PERSON] et al. [41] and [PERSON] et al. [42], simple encounters can represent neurons in RNN and, hence, easy hardware implementation.