一种面向最小能耗自适应汇聚路由判定算法

Adaptive sink-routing decision algorithm for minimum-energy consumption

在无线传感器网络数据传输过程中,由于大量数据在信道内积累,将会产生频繁数据冲突与碰撞,无故地消耗了过多的网络能量。为此,提出了一种面向最小能耗自适应汇聚路由判定算法。首先,该算法借助于汇聚增益计算了数据融合度介于最小值与最大值之间时,数据融合度与节点距离相关参数之间的比例关系;其次,讨论了3种距离相关系数情况下,节点之间的关联关系,证明了多节点不进行数据融合时,下一跳节点数据融合度存在的条件;然后,通过数据压缩能量比函数关系,讨论了数据在链路上进行压缩与解压以及数据在源节点和Sink节点进行压缩与解压时,全网能量消耗的计算过程;最后,在上述分析基础上,利用能量转换模型计算了任意节点之间欧氏距离满足的必要条件,同时分析了这种算法的实现过程。仿真实验表明,这种算法与其他算法在网络能量开销和网络平均时延上进行对比,其数值分别减少了约10.29%和12.57%,达到了优化汇聚路由的目的,从而证验了这种算法的有效性和实效性。

During the data transmission in wireless sensing networks(WSNs),the data accumulation in the communication link will lead to frequent data collisions,which further consumes the network energy.To address this problem,we propose an adaptive sink-routing decision algorithm for minimum-energy consumption(ASD-MC).First,when the data fusion degree is between the maximum and minimum value,the aggregation gain is exploited to calculate the proportional relationship between the data fusion degree and node distance-related parameters.Then,we discuss the correlation among different nodes with three distance correlation coefficients.When multiple nodes do not conduct data fusion,the condition for the existence of data fusion degree is proved for the next-hop node.In addition,according to the functional relationship of data compression energy ratio,we consider the data compression and decompression process on the link both at the source node and at the sink node.Then the procedure for the calculation of the network energy consumption is provided.Based on the above analysis,we employ the energy conversion model to derive the necessary condition for the Euclidean distance between any two nodes.Furthermore,the procedure for the implementation of our proposed algorithm is also presented.Finally,simulation results show that,compared with existing algorithms,our proposed algorithm could reduce the network energy consumption and the average network delay by 10.29%and 12.57%,respectively,which verifies the effectiveness and validity of the ASD-MC algorithm.