M.A. Thesis:

Use imperial improved optimization algorithm to optimizer energy consumption in wireless sensor networks

Wireless sensor network, consists of many cheap sensor nodes with limited energy. Node tasks is to gather data from surrounding environment, analysis, process and send sensed information to other nodes or base stations. One of the most fundamental challenges that these networks face is the limited energy of the nodes, which reduces network lifetime. Clustering and routing are two ways for facing this problem. In this thesis we used the capabilities of imperialist competition algorithm and chaos theory for reducing energy consumption and also for choosing proper cluster head in wireless sensor network. The proposed method identifies the cluster head nodes based on parameters like the amount of energy and dispersion of nodes and cluster heads with higher energy than normal nodes create symmetrical clusters. The most prominent characteristics of the proposed algorithm are the using of the chaos theory to form an empire at the initial phase of algorithm and happening of revolution, determining threshold in every step of algorithm operation to accomplish revolution or collapsing in the imperialist, and also run the absorption policy using the gradual collapsing of empires and justify the number of colonies of each empire, based on the power of that empire in every iteration of algorithm. Determining threshold in proposed algorithm will be lead to proper load balancing in network. Simulations results show superiority of the proposed method than other traditional methods. The proposed method has meaningful effect in reducing power consumption, enhancing network lifetime, increasing the amount of transferred data and increasing the number of active nodes in different cycles of algorithms run.

Keywords: Imperialist competition algorithm,chaos theory,clustering,clusterhead,wireless sensitive network.