基于概率感知模型的多节点联合网络覆盖算法

针对传感网在对关注目标节点进行覆盖过程中出现的非连续性覆盖,将会产生覆盖盲区以及大量冗余数据迫使通信链路出现瓶颈现象,提出了一种基于概率感知模型的多节点联合覆盖算法(Multi-Node Joint NetworkCoverage Algorithm,MNJC)。该算法借助于概率相关知识,给出了多节点联合时覆盖质量期望值计算方法,以减少覆盖盲区的产生,提高了多节点联合网络覆盖率;利用节点动态转换机制对节点部署进行重新优化,抵制了网络能量的快速消耗,延长网络生存周期;该算法与其他三种算法覆盖率、生存周期以及节点存活数量等方面进行了比对实验,其性能指标平均提升了18.11%,15.57%和8.26%,从而验证该算法的有效性和实效性。

Multi-Node Joint Network Coverage Algorithm Based on Probability Sensing Model

In covering the goal nodes in sensor network, the occurrence of discontinuous coverage leads to the blindness coverage area and the bottleneck of communication link forced by a large number of redundant data. Therefore, this paper proposes a Multi-Node Joint Network Coverage Algorithm（MNJC） based on probability sensing model. First of all, in the light of probability, a method to compute the coverage expected value of the monitoring area is presented to reduce the blindness area and promote the coverage rate of multi-node network. Secondly, deployment of nodes is optimized again by means of the dynamic shift mechanism of nodes to prohibit the rapid consumption of network consumption and prolong the lifetime. Finally, MNJC is compared with the other three algorithms through experiments in regard of coverage rate, lifetime and the number of working nodes. The results show that the performance index is increased by 18.11%, 15.57% and 8. 26% respectively, which verifies the validity and effectiveness of MNJC.