# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 2 Related Work
This approach extends the conventional multi-lateration method by linking each RSSI value to a disk-representative distance interval. The DV-hop and DML techniques are combined in two further algorithms, DV + DML and DMLDV. Simulations on testbeds utilizing actual RSSI measurement data are used to assess these algorithms. RSSI is even used in different environments and with innovative technologies. In [12], an underwater positioning algorithm based on RSSI is proposed to overcome issues related to water interference, unstable positioning, and large errors typically seen in these situations. A threshold technique based on receiver operating characteristic analysis is presented in [13], which is a novel RSSI-based fingerprinting method for room-level localization using small radio devices to enable a proximity approach. The effects of RSSI samples and the number and position of the Anchor nodes have been studied in tests conducted to characterize this technique. It is observed that the path loss model contributes significantly to raising the caliber and dependability of range accuracy. As a result, research into the path loss model in a real-world propagation environment is required. In contrast to the conventional route loss models, the best fitted parametric exponential decay model (OFPEDM) can attain a greater distance estimate accuracy and adaptability to environmental variables, according to our analysis of experiments conducted at a wheat field. A study in wheat fields based in this model has been considered. Another approach relies on cooperation among nodes. Nodes assist one another in locating themselves in these kinds of systems [14] by using signal parameters such received signal strength (RSSI), time of arrival (TOA), or a combination of these. When testing a certain algorithm for particular channel characteristics and WSN topology, a lower bound parameter can be utilized as a benchmark using TOA or RSSI models. This enables one to determine whether the required accuracy for a given application is achievable. The \"disk graph model\", which is typically used to test cooperative localization techniques, can be avoided thanks to this feature. The study in [15] highlights the unusually high errors in RSSI-based distance estimates, which lead to significant errors in RSSI-based localization schemes. It suggests using outlier detection techniques to eliminate the impact of these disproportionately inaccurate distance estimates in RSSI-based location estimation. Three distinct localization strategies that use outlier detection to successfully lower localization errors in darkened situations are suggested, just like in our study. The study in [16] introduces machine learning techniques that use received signal strength indicator (RSSI) data gathered by Bluetooth low-energy (BLE)-based nodes to determine an object's location. A publicly accessible RSSI dataset is used for this experiment. Several BLE beacon nodes placed in a complex indoor setting with labels are the source of the RSSI data. Moving average filters are then used to filter the RSSI data after they have been linearized using the weighted least-squares approach. Additionally, the dataset is trained and tested using machine learning methods to determine the exact location of the objects. Localization can also be achieved via methods other than RSSI [17; 18]. The node localization problem was usually regarded as an NP hard problem in research. A number of metaheuristic approaches that significantly reduce the localization error are used to solve the localization problem in WSNs. An efficient metaheuristic-based Group Teaching Optimization Algorithm for Node Localization (GTOA-NL) method for WSNs is developed in [19].