Simulation of Weighted Centroid Algorithm for Self-Localization in Wireless Sensor Networks

Localization represents a critical functionality in wireless sensor networks. The weighted centroid self-localization algorithm has gained widespread practical adoption due to its effectiveness. The core methodology involves calculating weighted centroid coordinates through inter-node communication data to determine node positions. In this simulation, we implement the weighted centroid algorithm by modeling information exchange between nodes, subsequently validating both feasibility and precision metrics. The algorithm typically computes node coordinates using RSSI (Received Signal Strength Indicator) values as weights, where stronger signals contribute more significantly to position determination.

Furthermore, applications of the weighted centroid self-localization algorithm extend beyond sensor networks. For instance, indoor positioning systems can leverage this algorithm for accurate location tracking within enclosed environments. The implementation often involves matrix operations for coordinate calculations and neighbor discovery protocols for establishing communication links. This versatility underscores the algorithm's broad research prospects and practical significance across multiple domains.