## 3 GNSS-Free IoT Localization Methods
### Signal-Based Localization Methods  
In this category, LPWAN signal features of IoT are being used for end-node localization. Power-based (RSSI), time-based (ToA, TDoA), and angle-based (AoA) methods are the most important signal characteristics [40]. * Received Signal Strength Indicator (RSSI): RSSI is the simplest and widely used method that uses received signal power for end-node localization. For doing this, a path-loss model should be developed for the system. The path-loss model specifies the signal propagation model of the system. The signal propagation model determines the signal strength at increasing distances [41]. There are different kinds of propagation models including Line Of Sight (LOS), Two-Ray (2R), Nakagami-m, gamma distribution, and log-normal, etc. In the simplest model, a free space between transmitting and receiving antennas is considered. Therefore, only a LOS propagation path is present between transmitter and receiver antennas. In this system, the only disturbance is atmospheric effects and path loss is a function of distance between them. In more usual systems, communication between transmitter and receiver takes place closer to the earth surface. Therefore, the LOS model is extended to incorporate the effect of the ground-reflected signal. That means, in the 2R model, the path-loss characteristics in LOS environments are dominated by interference between the direct path and the ground-reflected path [18]. In a more realistic system, the transmitted signal is obstructed by buildings, mountains, and foliage several times and a combination of these signals is received. Received signal strength in multipath propagation at the receiver can be characterized based on two different scales (large scale and small scale). Large-scale fading is due to motion in a large area, and small-scale fading is due to small changes in position or to changes in the environment [42]. To model these two different scales of multipath propagation, different probability distributions are presented (e.g., Nakagami-m, gamma distribution, log-normal). As most of the IoT systems have small changes in position or in the environment (smart healthcare, smart manufacturing, and most applications in smart city), in this paper we focused on small-scale fading to model the signal propagation. The log-normal model or log-distance path-loss model is the most commonly used method to model small-scale fading. In this model, when the LoRaWAN network server receives a signal from the end node, it can measure transmitter distance by Equation (1). \[RSSI=-10 nlog_{10}(d)+A \tag{1}\]  
where \(n\) is the path-loss exponent and \(A\) is the RSSI value at a reference distance (1 m) from the receiver. The ground reflection model is another commonly used propagationmodel. The ground reflection model considers the ground reflection propagation path between transmitter and receiver in addition to the direct path [43]. RSSI is easily affected by the propagation environment, absorption and scattering, and antenna effects including impedance mismatch and polarization mismatch [44]. In addition, RSSI is highly dependent on changes in environmental and weather conditions, which may lead to location accuracy reduction. On the other hand, RSSI is cost-effective and it does not need extra equipment. * Time of Arrival or Time on Air (ToA): ToA is the time that transmitted signal takes to reach the receiver and the ToA of a packet is dependent on CR, SF, and BW. ToA can be used for distance estimation by multiplying the speed of light. There are two categories of ToA: one-way and two-way ToA [45]. In one-way ToA, the distance is computed based on one-way signal transmission between transmitter and receiver. In this case, ToA requires strict synchronization or timestamps to be transmitted with the signal. In two-way ToA, ToA is computed from roundtrip communications between transmitter and receiver. Two-way ToA is an asynchronous procedure, thus involved clocks do not need to be precisely synchronized.