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在无线传感器网络中优化能源受约束的目标定位和跟踪,使用辐射偏差和搜索器优化算法

S Yazhinian¹, S Famila¹, P Jose¹

¹Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India.

|April 25, 2025

概括

本研究介绍了一种结合辐射偏差和搜索器优化算法的新方法,用于在无线传感器网络中精确,节能地定位目标. 这种方法提高了准确性,并延长了网络运行时间.

科学领域:

无线传感器网络无线传感器网络
定位和跟踪定位和跟踪
优化算法优化算法

背景情况:

标准的本地化方法使用固定的结位置,限制了适应部署环境的适应性.
能源限制给无线传感器网络 (WSN) 中的目标定位和跟踪带来了重大挑战.

研究的目的:

通过结合辐射偏差 (RB) 和搜索器优化算法 (SOA) 来提出一种新的能源受限目标定位和跟踪方法.
为了提高本地化准确度,并优化传感器部署和数据传输,以最小的能源消耗.

主要方法:

整合辐射偏差 (RB) 来改进目标位置估计.
搜索器优化算法 (SOA) 的应用,以优化传感器部署和数据传输路径.
在各种粒子计数 (5,10,15,20,25) 上对拟议的辐射偏差寻找器优化算法 (RadB_SOA) 的评估.

主要成果:

RadB_SOA方法在跟踪精度和能源效率之间取得了平衡.
实现的指标包括12.4%的传输错误,14.6%的距离错误,96.3%的本地化覆盖率,98.65%的数据包交付比率 (PDR) 和21.56%的能源消耗.
减少了算法中的粒子数量,加速了定位和跟踪,同时保持了准确性.

结论:

拟议的RadB_SOA显著提高了目标定位精度,并优化了WSN中的能源使用.
与现有方法相比,模拟结果显示出更高的跟踪精度,更少的错误和更大的本地化覆盖率.
该研究证实,优化粒子计数可以提高网络效率,定位速度和定位精度.

关键词:

覆盖范围覆盖范围覆盖范围能源效率能源效率是指能源的使用效率.优化算法优化算法在RadB_SOAA中使用.接收到的信号强度.目标本地化目标本地化追踪跟踪追踪跟踪

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