Lightweight APIT with Bat Optimization with Simulated Annealing Localization for Resource-Constrained Sensor Networks

• Published in: Wireless communications and mobile computing 2023, Vol.2023, p.1-11
• Main Authors: Latha, T. Swarna, Rekha, K. Bhanu, Ferede, Alachewn Wubie
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Data processing; Global positioning systems; GPS; Localization; Network latency; Network management systems; Nodes; Optimization; Performance evaluation; Performance measurement; Sensors; Simulated annealing; Surveillance; Tracking systems; Triangulation; Unevenness; Wireless sensor networks
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2023/7982038

In a wireless sensor network, information processing, and information acquisition, localization technology is the key to making it practically possible application. Approximate Point-in-Triangulation (APIT) is the most widely used localization estimation which has high accuracy in localizing the nodes and ease of deployment of nodes in the real-time environment. Though it has numerous key advantages, some of the drawbacks which make it a little setback in preference are the unevenness in the distribution of nodes. Tracking is more appropriate for mobile sensor nodes than tracking is for static sensor nodes. The two main types of localization algorithms are range-based and range-free techniques. In an indoor setting, the projected range (distance) between an anchor and an unknown node is very inaccurate. By utilizing a large number of already existing access points (APs) in the range-free localization approach, this issue can be overcome to a great extent. The utilization of multisensor data, such as magnetic, inertial, compass, gyroscope, ultrasonic, infrared, visual, and/or odometer, is stressed in recent research to further increase localization accuracy. The tracking system also makes location predictions for the future based on historical location data. To overcome this issue, the proposed localization algorithm of APIT with Bat-SA proves its efficiency. Due to its low localization error, the traditional Bat method is more accurate than APIT. The proposed Bat using the SA algorithm is found to perform better than the traditional APIT algorithm in terms of convergence of computing rate and success rate. In order to mimic the suggested APIT method, it is paired with the Bat-SA localization technique. Simulation evaluation proves the performance efficiency of the proposed algorithm. The performance metrics parameters are latency, node distribution map, positioning error map, and neighbor relationship diagram which are used to evaluate the proposed method.