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.