基于汉明距离的传感器网络分层拓扑发现算法

针对传感器网络能源有限的特性,提出了一种基于汉明距离的分层拓扑发现(LTIHD)算法.根据在汇聚节点收集到网络内部节点报文接收或丢失的情况,利用汉明距离识别相邻两层节点之间的父子关系,逐层推测网络的拓扑,不增加网络负担.仿真试验表明:推测18节点传感器网络需要60轮数据采集和1.56 s的推测时间;推测120节点传感器需要140轮数据采集和4.12 s的推测时间.该算法可以准确快速地推测传感器网络的拓扑,适合大规模传感网络的拓扑推测.

Sensor network level-topology inference based on Hamming distance

Considering the resource-constrained sensor network,a level-topology identification algorithm,LTIHD(level-topology inference based Hamming distance),was proposed based on Hamming distance According to the sequence of data loss/receive collected in the sink,the nodes with parent-child relationship can be identified in the two adjacent levels based on the Hamming distance.Then the whole sensor network topology can be obtained level by level and the proposed algorithm does not increase any network burden.The simulation results show that in 18-nodes sensor network,the algorithm needs 60 data collection rounds and 1.56 s to infer the network topology,and in the 120-nodes sensor network,it needs 140 data collection rounds and 4.12 s to infer the network topology.The algorithm can identify the sensor network quickly and accurately, and scale to large sensor network.