## 3 GNSS-Free IoT Localization Methods
### Localization Accuracy Improvement Methods
They provide their simulation in three models: unprocessed data, processed data, and processed data using mean of measurements. During the simulation, authors change different factors of NN (number of hidden layers, number of neurons in each layer, optimizer, earning rate, the batch size, and the number of neurons) to achieve the best results. The authors mentioned that NN can gain equally or even better estimation compared to kNN fingerprinting. Deep learning or deep neural network (DNN) is the extended model of artificial neural networks to learn tasks. It contains more hidden layers. There are large category of literature that adopt DNN for location estimation [64; 65; 66]. The authors in [67; 68] mentioned that it is not feasible to work only with labeled samples in an outdoor environment. In addition, the location estimation accuracy of the semi-supervised learning methods is usually better than that of unsupervised learning. Therefore, they proposed a semi-supervised DNN for location estimation in LoRaWAN. By constructing the relationship between labeled and unlabeled data, authors generate several virtual labeled data through learning. RSSI, SNR, and timestamps are used for training and learn grid segmentation knowledge from the source domain and transfer the knowledge to the target domain. RSSI-based outdoor location estimation methods have acceptable performance in certain environments and their error increased in the general environment. The authors in [69] proposed to exploit from UAVs. The proposed method used from a UAV for localization estimation improvement was initially provided by the network. The authors characterize the relevant parameters of the communication system and used it from a quadrotor capable of carrying the required hardware as a mobile gateway. Their result showed that the mobile gateway (between 1 and 3) can lead to localization precision increment. RSS-based fingerprinting approaches can usually achieve better accuracy than RSSI-based methods. This makes fingerprinting methods an interesting option for location estimation. However, Fingerprint-based methods require a wide survey of the environment to build a database and update the database regularly to reflect changes in dynamic environments and complex signal fading of different environment situations (e.g., weather changes). The authors in [54] analyze RSSI fingerprinting in real LOS and non-LOS (NLOS) environments. The authors try to determine the appropriate function for accurate LoRaWAN packet specifications and RSSI mapping. They examine the performance of LoRaWAN at different spreading factors and showed that ML algorithms could use RSSI to predict the number of obstacles. In [70], authors mentioned that location estimation using the traditional path-loss models cannot be determined because of highly variable factors. Therefore, they studied various regression (linear, polynomial, exponential, and Gaussian regression) and machine learning models (support vector machines, spline models, decision trees, and ensemble learning) on received RSSI to accommodate the variability of the ranging function. The authors point out that the dependence of gateways on the environment (e.g., weather conditions and obstacles) greatly influence signal quality and can be accommodated by training different models for each gateway. In addition to the different environment influence on gateways, localization errors can vary in different environmental and different gateways. The authors in [71] mentioned that localization errors are considerably larger at the edges of an environment, in comparison to its center. As a result, the localization error of a new location estimate is associated with the localization error of its nearest ground truth location(s). Therefore, the aggregate statistical metrics are intrinsically not very useful in characterizing the localization error of a single location estimate.