Underwater wireless sensor networks (UWSNs) can provide real-time monitoring services for many underwater applications,such as the oceanographic data collection,natural disaster prevention and tactical military surveillance,all these have attracted a prominent attention recently.Especially,the localization technique is a challenging task for underwater applications,and the localization is usually completed with the assistance of beacon nodes.However,due to the sophisticated environmental variations of water current,underwater creature touch,and strong electromagnetic interference,some beacon nodes tend to move or be damaged,which thus results in larger positioning errors of ordinary nodes.In order to solve the error beacon problem,an error beacon filtering algorithm was proposed based on particle swarm optimization (PSO),which can differentiate the beacon accurately.Firstly,several location errors of beacons are calculated by an improved Trilateration,and then the beacon with the maximum positioning error is filtered out through PSO.The remaining beacons are filtered out consecutively until the location error of each beacon does not exceed a preset threshold.Simulation results indicate that the proposed algorithm can detect almost all error beacons effectively with smaller variances.%水下无线传感器网络可以为海洋地理数据收集、预防自然灾害、战术预警等多种水下应用提供实时监控服务.水下定位技术是水下应用中的一大难点.水下定位通常依赖信标节点;但是由于水下洋流环境的复杂变化、水下生物的碰触和强电磁干扰,信标节点往往会移动或损坏,导致许多普通传感器节点定位错误.为了处理错误信标问题,提出一种基于粒子群优化的错误信标过滤算法来精确地找出错误信标.首先通过改进的三边定位法计算出定位错误;然后通过粒子群优化算法把定位错误数量最多的信标节点过滤出来.剩下的信标节点不断进行过滤,直到每一个信标节点的相关定位错误都低于某个预设的阈值.模拟实验证明,算法可以高效地检测出几乎全部错误信标,并且有很好的算法一致性.
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