# Indoor Vehicles Geolocalization Using LoRaWAN
## 2 Related Works  
A detailed overview of different indoor localisation techniques, such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF) and Received Signal Strength (RSS), is offered in [8]. Proposals based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature are described. The authors of [9] study the performance and prospect of LoRa in indoor localisation. The authorsobserved that LoRa is more stable than WiFi and Bluetooth Low Energy (BLE) and is more resilient to environmental change. In [10], the authors present an IoT tracking system using LoRa technology designed and implemented in order to obtain some accurate results. The two algorithms that are described show that it is feasible to locate a device in a static spot with an accuracy of around 100 m. In his thesis [11], [PERSON] investigates whether the received signal strength from LoRaWAN networks can be used to determine the position of a connected end-device. The study is done by simulating a large scale network with real-world measurements to study how the number of anchor nodes impacts the positioning accuracy. Results show that the measurements vary too much to give a precise position estimation. [PERSON] and [PERSON] focus in [12] on comparing WiFi, BLE, Zigbee, and LoRaWAN for use in an indoor localisation system. By using three transmitting nodes broadcasting information, along with a single receiver, trilateration could be performed to determine an approximate receiver location. The most interesting results of this work are those for transmission range--LoRaWAN had the furthest transmission range when running at its maximum transmission power. [PERSON] et al. [13] address the problem of estimating the location of LoRaWAN devices from the time of arrival differences measured at gateways. An Extended Kalman Filter (EKF) based approach is considered to aggregate the measurements obtained at different time instants. Particular attention is paid to the processing of outliers. Based on experimental data obtained from field measurements conducted on a real LoRaWAN TM network and an insight into the realistic localisation accuracy of the considered localisation approach is provided. [PERSON] et al. [14] analyse the performance of LoRa geolocation, although for outdoor tracking purposes. Time Difference of Arrival (TDOA) localisation accuracy, probability and update frequency were evaluated for different trajectories (walking, cycling, driving) and LoRa spreading factors. A median accuracy of 200 m was obtained and in 90% of the cases the error was less than 480 m.  
[PERSON] et al. [15] develop and benchmark four RSS localisation algorithms where different a priori knowledge is required. The selection of the best algorithm depends on the availability of additional information on the path loss exponent and/or transmit power. The authors perform experiments and simulations with Bluetooth Low Energy and LoRaWAN technologies and select BLE as the best technology for localisation in large open industrial environments. In [16], the authors present an RSSI-based localisation solution that relies only on the LoRa technology. This paper mainly focuses on the design for large used car dealerships. Cars to be tracked are fitted with battery-powered tags that send each other plain LoRa messages and then report over LoRaWAN the observed RSSI to the server. The server estimates tag coordinates using multi-dimensional scaling and a novel corrective transformation based on tag clustering. In conclusion, using TDoA for geolocalization is problematic since not all devices nor all gateways can provide a proper estimation of these values. Still, the use of the RSSI is considered a unique solution. In this sense, existing works fail to present results on the stability of the RSSI values over time and on the impact of the spreading factor on the quality of the obtained values.