# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 2 Related Work  
Although widely investigated, RSSI is still attracting interest as a localization method. The authors in [3] examine a variety of technologies and approaches for both indoor and  
Figure 1: Multipath phenomenon. outdoor localization. Accuracy, cost, complexity, security, scalability, and other issues are covered, along with many applications where location data are essential. Different wireless-based locating systems' accuracy values are compared in urban, rural, indoor, and remote settings. The transmission scale affects the accuracy requirements, which in turn affects the kinds of applications that each localization approach can serve. This emphasizes how crucial localization is to wireless systems, both present and future, in all scenarios and applications. The work in [4] reviews various range-based and range-free localization techniques, with a focus on the latter, and explores localization-based applications where location estimation is critical. It also addresses challenges related to accuracy, cost, complexity, and scalability. An isosceles layout model is introduced in [5] to improve distance estimation and reduce the impact of RSSI variability. By guaranteeing that target nodes are consistently inside Anchor nodes' transmission range, this method reduces interference and enhances localization precision. The isosceles layout can provide improvements when compared to other forms, such as square and equilateral layouts. The Jaya algorithm is used to evaluate the performance of the previous and new layout models, and the outcomes are contrasted with those of the localization methods based on Particle Swarm Optimization (PSO) and the Salp Swarm Algorithm (SSA). The performance of the suggested approach is evaluated in terms of localization accuracy and scalability. A novel equal-arc trilateral localization algorithm based on RSSI is developed in [6] to increase localization accuracy and optimize node resource allocation in a WSN. The algorithm addresses the disruption in RSSI values by processing data from the optimal communication range using a Kalman filter. The ideal communication distance is determined by examining the correlation between RSSI and communication distance. By guaranteeing that the motion pathways of Unknown nodes stay within the ideal communication distance, the suggested equal-arc triangular beacon node structure improves measurement accuracy. However, the path loss plays a major role in improving the quality and reliability of range accuracy. Consequently, studies are conducted on the path loss model in a real-world propagation setting. A decay model is put forth in [7] to evaluate distance accuracy and adaptation to environmental variables, which is different from the traditional route loss methods. A different study using a Kalman filter suggests a better indoor positioning system that combines multi-lateration and a quadratic weighted combination. This makes it easier for Bluetooth low-energy (BLE) wearable devices to send and receive consistent RSSI signals [8]. Errors frequently result from the fluctuating RSSI signal caused by multi-path effects in physical contexts [9]. In [10], an enhanced multi-lateration approach is presented to increase localization accuracy by combining zone selection, boundary consideration, and a position compensation technique based on virtual positions. A ZigBee-based IEEE 802.15.4 wireless sensor network was used for the experiments, which were carried out in a corridor and a lab. The findings demonstrate that, in contrast to the conventional multi-lateration method, which frequently places locations beyond the test region, the suggested method offers estimated positions within the test area. A method that combines the range-based algorithm based on RSSI measurements with the range-free distance vector (DV) hop algorithm is presented in [11]. In order to account for RSSI measurement errors, a technique known as disk-based multi-lateration (DML) is proposed.