An Ant Colony-Based Clustering Algorithm For Wireless Sensor Networks

Wireless sensor network is self-organizing network, which is made up of a large of number of sensor nodes with wireless communication and data processing capability. It serves as a new information acquisition mode and processing method, and can be applied to multiple areas such as environmental monitoring, medical care, industrial production and military reconnaissance. With the development of "Internet of Things", wireless sensor network becomes one of the focuses of current research.In the wireless sensor network architecture, the routing technology of the network layer has an important impact on the performance for the wireless sensor network. This thesis focuses on the research in cluster routing technology for wireless sensor network. To solve problems such as the uneven energy consumption of sensor nodes, random cluster head selection and irrational cluster structure in the traditional clustering routing algorithm, a clustering algorithm for wireless sensor network based on ant colony algorithm is proposed. In the cluster head selection phase, the energy and distance pheromones based on the residual energy and aggregation of sensor nodes is provided. Based on this, a new cluster head selection algorithm is proposed based on ant colony algorithm. In the cluster forming phase, the cluster structure is optimized according to the defined cost formula for clusters. The sensor nodes join the cluster head to form the cluster according to the value of cost formula. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the life cycle of the network.In this paper, the wireless sensor network architecture, feature and their application are introduced; followed by clustering routing protocol for wireless sensor network and the improved algorithm is described in detail; after the review of ant colony algorithm, the wireless sensor network routing protocol based on ant colony algorithm is present; On this basis, the algorithm is finally elaborated and priority performance is proved by simulation.