能量高效的WSNs分簇数据融合算法

Energy-efficient WSNs clustering data fusion algorithm

针对无线传感器网络(WSNs)中多跳通信造成的"热区"以及数据冗余问题,提出了一种能量高效的分簇数据融合算法(EECDA)。该算法在分簇阶段综合考虑节点的剩余能量、到基站的距离和邻居节点的数目,周期性地选择簇首和划分不同规模的簇;对簇内数据进行融合,利用辛普森积分法则计算预测接收数据,在保证采集数据实时性和准确性的前提下,降低数据的冗余性,减少通信负载,提高网络的能量利用率。仿真结果表明:该算法能够对数据进行高效预测,减少网络通信量,相较已有的算法,能够有效延长网络的生存周期。

In order to mitigate the＂ hot spot＂ problem in wireless sensor networks（WSNs） ,which is caused by the multi-hop transmission mode, an energy-efficient clustering data aggregation algorithm （EECDA） is proposed. The clustering algorithm（EECDA）for WSNs considering the residual energy of nodes, distance to base station and number of neighbor nodes periodically select cluster heads and divide into different size clusters ; data in cluster are fused,using Simpson＇ s rule to predict receiving data premise of ensuring real-time and accuracy of data acquisition, reduce redundant of data, reduce traffic load and improve energy efficiency of the network. The simulation results show that the algorithm can efficiently predict the data to reduce the traffic data, compared to existing algorithms, it can effectively extend lifetime of the network.