## 3 GNSS-Free IoT Localization Methods
### Localization Accuracy Improvement Methods
In summary, they proposed to use a trained model to estimate the localization errors at new locations based solely on the observed RSS values at these locations and they used estimated location as an optional additional input feature for different regression methods (ordinary least squares, ridge, lasso, elasticNet, polynomial, k-nearest neighbors, support vector, and random forest). Path-loss parameters are different in various environments. These different parameters lead to distinct RSSI for the same distance. The authors in [72] proposed to use high-resolution satellite images to generate virtual fingerprints for land-cover types and path-loss parameters identification for each gateway. In the training phase, a random forest model for automatically generating a land-cover map according to the satellite image of the area of interest is used. Next, the proposed method produces a virtual fingerprinting map. Then in the localization phase, they combine the fingerprinting maps of multiple gateways for node localization. In [73], authors mentioned that in RSSI signals cannot be measured for every location in the service area. Therefore, they proposed to estimate the locations using probabilistic means based on three different algorithms that use interpolated fingerprint RSSI maps. Time-based approaches in LoRaWAN lead to more accurate location estimation. The authors in [53] assess the RSS positioning performance in a realistic LoRaWAN network, for different spreading factors (SFs), and make the comparison with LoRaWAN TDoA performance. In their experiments, they pointed out that raw location estimates of the TDoA approach result in better accuracy than all investigated RSSI approaches with different SF. Inthe following, the authors improve the performance of RSSI-based methods by taking into account road-mapping filter (the road infrastructure layout and the allowed speed limits). In [74], the authors proposed a TDoA-based method for localization that considers road map and movement speed. They determine the positioning accuracy for three different mobility profiles (walking, cycling, and driving). The authors in [75] divide TDoA localization algorithms into two categories (Non-iterative and iterative algorithms). They explain 5 classic TDoA algorithms and evaluate their performance using simulation. Based on the results, they select the two providing the best accuracy (i.e., [PERSON]'s and [PERSON]'s) and combine them to improve localization accuracy. One of the most important drawbacks of TDoA-based methods is their need for time synchronization. The authors in [76] proposed an indoor localization system based on TDoA for UWB. The proposed method relies on the capability of the IEEE 802.15.4a UWB receiver for accurate time distinctions of the transmitter. In [77], [PERSON] et al. propose a TDoA-based method for centimeter-level indoor localization of smartphones using a WSN infrastructure and non-invasive audio for TDoA estimation. The authors in [78] mentioned that in some harsh environments LoRaWAN cannot work acceptably so it needs an extra assistant. They adopt ultrasonic signals as an aiding approach for TDOA localization-based method for outdoor environments. For localization accuracy improvement, The authors in [79] proposed a fingerprinting method applied to a reference map to perform outdoor geolocation based on machine learning (Random Forest and Neural Networks). The map combines TDOA measurements. There is a category of iterates that uses a combination of two localization methods to improve estimation accuracy [80; 81; 82; 83]. The authors in [80] present a message-passing algorithm for target tracking which exploits AoA and ranges information from RSSI measurements. In the proposed method, the path-loss exponent to each anchor is assumed to be unknown and time-varying to be adaptive to dynamic propagation conditions. In [81], RSSI and ToA are integrated for the regression analysis of the distance between the anchor node and the IoT node. The proposed method is restricted to localizing a single target.