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引用次数: 0
摘要
无线传感器网络(WSN)在医疗保健、环境监测和目标跟踪方面有着广泛的应用,它依赖于合作连接的传感器节点。这项研究探讨了 WSN 中目标和节点的定位算法,以提高精确度。通过实施差分进化算法,提出了一种创新的定位算法,其特点是异步到达时间(TOA)目标。与现有方法不同的是,所提出的算法采用最小二乘法准则,将信号发送时间表示为目标位置的函数。目标节点的坐标是通过采用反向学习和自适应重定向的差分进化算法估算出来的。引入了一种混合接收信号强度(RSS)-TOA 目标定位算法,以应对未知传输参数的挑战。该算法利用 RSS 和 TOA 测量同时估算传输功率、路径损耗指数和目标位置。这些建议的算法提高了无线传感器定位的准确性和效率,提升了各种 WSN 应用的性能。
Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm
Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node’s coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.
期刊介绍:
PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.