基于粒子群优化聚类的温室无线传感器网络节能方法

温室传感器网络中,不同区域节点间高相似度数据的传输会浪费通信带宽和增加能量消耗,因此研究相应的节点数据压缩方法对减少数据冗余和提高节点续航能力具有重要意义。该文针对温室无线传感器网络中节点感知数据的特点,同时考虑节点续航能力有限的因素,提出一种温室无线传感器网络方案,系统按轮运行,每轮中利用粒子群(Particle Swarm Optimization)的K-均值聚类算法将节点按监测数据相似性划分到相同的区域,每个数据相同区只允许聚类有效性指标值最高的节点向汇聚节点传输数据,其余节点暂时休眠。试验结果表明,16个节点在10轮试验中归入休眠集合的总次数达到131次,DCAPI平均值为0.1814,每轮降低能耗72.93%以上,该系统能够极大地减少每轮中的工作节点,压缩发送的数据量,降低能耗。

Method of energy saving based on particle swarm optimization clustering for greenhouse wireless sensor networks

In greenhouse sensor network, high similarity data transmission of nodes in different areas may lead to communication bandwidth waste and energy cost increase. Therefore, the study of node data compression method is of great significance to reduce data redundancy and improve the node life ability. Based on the characters of data and the factor of endurance capability, a kind of greenhouse wireless sensor network solution was proposed. The system adopted round operation mode, in each round, nodes of monitoring similarity are put into same area by particle swarm optimization (PSO) K-means clustering algorithm. Each area with same data only allows node with highest clustering validity to transfer data into sink node, the rest data nodes need temporarily dormancy. The experimental results showed that the total number of sixteen nodes subsumed into Sleep was 131 in 10 collection rounds, the mean value of DCAPI was 0.1814 and energy consumption reduced by 72.93% or more. So the greenhouse wireless sensor network solution can greatly reduce the working nodes number per round and compress the data quantity in network.