基于二元数据的WSNs多目标容错定位算法

传感节点的感测数据易受到污染,导致大量传感节点的观察数据出错.为此,提出基于二元数据的多目标容错定位BMSFTL算法.BMSFTL算法考虑传感节点的差错概率的情况,利用传感节点的二元观察数据对目标源进行识别及定位.在目标源识别阶段,采用分布式竞争领导者(DCL)算法产生领导者(leader)节点,在理想状态下,leader节点数等于目标源的个数.然后,再利用基于网格投票(GBV)机制对目标源进行定位.仿真结果表明,提出的BMSFTL算法在噪声和差错情况,保持高的定位性能,在差错概率为0.25的环境,均方根误差小于8 m,远优于最大似然估计ML.

Binary Data-Based Multiple Sources Fault Tolerant Localization in WSNs

Sensing of sensor nodes can be tampered,which results in a large number of sensor nodes observation data reporting erroneous.Therefore,the binary data-based multiple sources fault tolerant localization (BMSFTL) algorithm is proposed.BMSFTL algorithm uses the binary observation data of the sensors for identifying and localizing multiple sources in the presence of faults.In the identification phase,the distributed contented leader(DCL) algorithm is used to compute the number of leader node.During the ideal condition,the number of computed leader nodes corresponds to the number of sources.After,grid-based voter mechanism is run to localize the sources.Simulation results show that the BMSFTL algorithm retains its good localizing performance in the presence of faults,even when fault probability is up to 0.25,the root mean square error is less than 8 m,which is far superior to the maximum likelihood estimator.