Cefai Edward-Joseph, Coombes Matthew, O'Boy Daniel
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK.
Sensors (Basel). 2025 Jan 11;25(2):402. doi: 10.3390/s25020402.
Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, which result in the collection of redundant information, which can deteriorate performance while also increasing the associated network costs. Therefore, this work combines a predictive posterior distribution methodology with a novel simplified objective function for optimally identifying and forming smaller sensor clusters before activation and measurement collection. The goal of the proposed objective function is to reduce network communication and computation costs while still maintaining the tracking performance of using far more sensors. The developed optimisation algorithm results in reducing the size of selected sensor clusters by an average of 50% while still maintaining the tracking performance of general traditional techniques.
传感器选择是无线传感器网络（WSN）管理的重要组成部分。在考虑使用低成本、仅测角的传感器节点来跟踪无人机（UAV）时，这一点变得尤为重要。然而，传统技术通常会形成过大的传感器集群，这会导致收集到冗余信息，从而降低性能，同时还会增加相关的网络成本。因此，这项工作将预测后验分布方法与一种新颖的简化目标函数相结合，以便在激活和测量收集之前，最佳地识别并形成较小的传感器集群。所提出的目标函数的目的是降低网络通信和计算成本，同时仍保持使用更多传感器时的跟踪性能。所开发的优化算法使得所选传感器集群的规模平均减小50%，同时仍保持一般传统技术的跟踪性能。
