一种基于能耗度量的融合树构建算法

基于传感器节点能耗情况对数据压缩以及数据融合进行了分析，针对在非完全融合情况下，贪婪增长树(GIT)算法构建融合树时并不能很好选择最优路由的问题，提出了一种基于能耗度量的融合树构建算法，通过融合节点反馈能耗以及到达Sink节点的跳数信息，对多个路由的能耗进行评估，进而选择低能耗路由.同时提出了一种由信息源节点进行路径加强的策略，减小了路径加强信息量以及多路径记录带来的负担.模拟实验数据表明，该算法在数据融合压缩比较小的情况下节能效果优于贪婪增长树GIT算法，并且随着信息源与Sink节点距离的增大，路径加强信息的数量也有很大降低.

An Aggregation Tree Constructing Algorithm Based on Energy Consumption Assessment

This paper first gives an analysis of data aggregation and data compression based on energy consumption of sensor nodes,after which an approach is proposed to construct an aggregation tree in the case of non-perfect aggregation,since GIT considers only the case of perfect aggregation and it does not work well if the aggregation is non-perfect.An assessment scheme that can get the information of hops from the aggregation point to the sink and the hops from the aggregation point to the source node is used to construct such an aggregation tree.Moreover,the energy consumption of the aggregation is also considered.This scheme can be used when perfect aggregation cannot be performed.In this paper,an approach to reduce the cost of reinforcement is also proposed,in which the reinforcement work is done by the source nodes themselves,not by the sink node.Simulation result shows that this approach can save more energy than GIT when the aggregation ratio is small.This result also provides a theoretical limit of aggregation to tell when GIT will lose its superiority and thus gives a direction to choose among the aggregation algorithms.Another result shows that the further the sources are away from the sink,the less reinforcement messages are needed. Finally a guidance to tell when to use the ECA(energy consumption assessment) scheme is given.