Research On Energy Consumption Of Wireless Sensor Network Based On Ant Colony Algorithm

In the modern world, the wireless sensor networks (WSN) applications got more and more attention. The most prominent of wireless sensor networks is limited energy and the environment frequency makes the energy of sensor nodes can’t be replenished. Therefore, the wireless sensor network protocol is the most important thing and the rational use of limited energy, so that the network can last longer.Aiming at energy and information quality in wireless sensor networks have been studied and optimized, specifically including following:1) This paper describes the ant colony algorithm Algorithm (ACA) of the basic principles of the existing AC A, based on the energy balance between the factors added to the ACA node metastasis probability formula(ACAWSN), local pheromone and global pheromone updating, proposed an improved energy consumption for the network for wireless sensor network routing protocol ACA (ACAEBN). The results show that the improved ACAEBN on maximum energy consumption of nodes, the total energy consumption, the success rate of information transfer, information coverage is better than basic ant colony algorithm and references cited algorithms.2) Based on ACAEBN algorithms were enhanced for the information quality problem, determine the network quality is an important criterion, namely to deliver more effective information within a limited network lifecycle. And as a target for ACAEBN is improved by adding the perception of the area coincident node processing, Raised Ant colony optimization algorithm based on energy balance in network(ACAEBN) the region of overlap nodes in turn work to implement in order to achieve lower overhead and prolong survival time for the purpose; to increase the processing path failure, between nodes on the path to failure pheromone set; the implementation of different density region clustering strategies to reduce unnecessary energy loss; using pre-selection mode and sharing a double selection. Simulation results show that ACAEBN network on the indicators improved compared to before there is much improvement.3) For the algorithm under the effect of different network types, the paper set up sink node in the network edge, the density of the different network nodes, the network node type uneven, and in these three network types for basic ant colony algorithm, algorithm from document, ACAEBN and RACAEBN simulation and analysis. Simulation results show that in the case of sink node network edge network index of four ant colony algorithm has decreased; outstanding common network node density in a larger network than the four algorithms index density, large ants next hop node selection of more advantage at ant colony algorithm can be better reflected; final paper selected by the center to the edge of the network node density decreases four algorithms, simulation results show that such a network conditions the indicators for the three networks optimal network, this network characteristics suited to the ant colony algorithm best. RACAEBN network conditions are optimal algorithm in three of the four algorithms.