## 3 GNSS-Free IoT Localization Methods
### Localization Accuracy Improvement Methods  
There is a large amount of literature that review different aspect of LPWAN technologies and Localization methods [52; 53; 41; 54]. The authors in [52] review the performance of LPWAN from various aspects (signal propagation, coverage, and energy conservation). The authors in [41] characterized and implemented an outdoor LoRa-based localization system. The authors in [53] compares the location estimation accuracy of TDoA-based and RSSI-based methods in a public outdoor LoRa network. The authors in [54] investigate the efficiency of fingerprinting using RSSI for location estimation. During the work with RSSI, data preprocessing and the hyperparameter tuning steps can optimize the performance of a localization system. The authors in [55] present a detailed study of four alternative methods of preprocessing (positive values, normalized  
Figure 4: Fingerprint-based localization phases. data, exponential, and powered) and discuss the process of selecting the most appropriate preprocessing methodologies and performing hyperparameter tuning. RSSI-based methods provide acceptable location estimation for the indoor environment. However, they are negatively affected by unknown propagation in outdoor environments. Therefore, This leads to errors up to hundreds of meters. Different methods are proposed to improve the performance of RSSI-based localization by filtering out noisy measurements (filtering techniques e.g., Particle, Bayesian, and Kalman [43; 56]) to improve accuracy. The authors in [57] try to improve the distance estimation accuracy using a Wiener filter. The proposed Wiener algorithm combines the RSSI values received from different LoRaWAN modulation (10 different modes) configurations and formulate the distance logarithm as a linear combination of RSSI measurements. In addition, the authors mentioned that using only one mode, the proposed method has better estimation yet. In addition, the authors in [58] proposed the Gaussian Signal Attenuation Model (GSAM) to decrease the interference effect in dynamic environments for vehicle localization. They combine the motion of vehicles to satisfy environmental constraints to improve the accuracy of RSSI-based localization. Blocking or multipath fading in RSSI-based methods can lead to non-Gaussian noise. This non-Gaussian noise leads to an attenuation signal. The weak signal received by the gateway leads to location estimation error. Many different studies try to improve the location estimation by selecting the GWs that are not affected by non-Gaussian noise [59; 60; 61; 62]. The authors in [59] proposed six new RSSI-based localization algorithms to reduce the effect of non-Gaussian noise in LoRaWAN networks. The first two algorithms eliminate GWs that are highly affected by non-Gaussian noise (RSSI-based LoRaWAN Localization with K-mean Clustering, RSSI-based LoRaWAN Localization with Iterative Elimination), the next two algorithms are proposed to select GWs that are not or are only slightly affected by non-Gaussian noise (RSSI-based LoRaWAN Localization with Minimum Mean Backward RSSI (MBRE) Error, RSSI-based LoRaWAN Localization with Density-based Clustering), and the last two algorithms make use of the previous algorithms to select a set of GWs that are not or are only slightly affected by non-Gaussian noise (RSSI-based LoRaWAN Localization with Minimum MBREs of Selected anchor nodes, RSSI-based LoRaWAN Localization with the Centroid of Selected estimated target locations). For location estimation using RSSI, different ML methods are proposed. ML methods are divided into three categories: supervised (learning phase uses from labeled data), semi-supervised (learning phase uses from a small amount of labeled data and a large amount of unlabeled data), and unsupervised learning (learning phase uses from unlabeled data). In [63], the authors adopt Neural Network (NN) to estimate the location of an object (supervised learning). The authors use from provided data by [23] for simulation.