传感器网络中健壮数据聚集算法

节约能量以提高网络寿命是传感器网络研究面临的重要挑战.网内聚集查询在中间节点对数据进行预处理,可以减少消息传送的数量或者大小,从而实现能量的有效利用,但是,目前的聚集查询研究假设采样数据都是正确的.而目前的异常检测算法以检测率作为首要目标,不考虑能量的消耗,也不考虑查询的特点.所以将两方面的研究成果简单地结合在一起并不能产生很好的效果.分析了错误和异常数据可能对聚集结果造成的影响,提出了健壮聚集算法RAA(robust aggregation algorithm).RAA 对传统聚集查询进行了改进,在聚集的同时利用读向量相似性判断数据是否发生了错误或异常,删除错误数据,聚集正常数据并报告异常,使用户可以对网络目前状况有清晰的理解.最后,比较了RAA 和TAGVoting(在使用TAG(tiny aggregation)算法聚集的同时利用Voting算法进行异常检测),实验结果表明,RAA 算法在能量消耗和异常检测率方面都优于TAGVoting.

Robust Aggregation Algorithm in Sensor Networks

Saving energy to prolong network life is a big challenge for WSNs (wireless sensor networks) research.In-Network query can reduce the number or size of packets through processing data in intermediate nodes so as to consume energy effectively. Present aggregation algorithms suppose all the sample data are correct. The existing outlier detection algorithms regard detection rate as the primary object and do not consider energy consumption and query characteristic. So the simple combination of the two aspects can not bring good performance. By analyzing the influence of faulty and outlier readings to aggregation results, this paper puts forward a robust aggregation algorithm RAA (robust aggregation algorithm). RAA improves traditional aggregation query using reading vector to judge whether a faulty or outlier has happened. RAA deletes faulty readings, aggregates normal readings and reports outliers. Thus, customers can know the networks condition clearly. Finally, this paper compares RAA and TAGVoting which uses tiny aggregation algorithm to complete aggregation and the Voting algorithm to realize outlier detection at the same time. Experimental results show that RAA outperforms TAGVoting in terms of both energy consumption and detection rate.