Energy-based intelligent routing for wireless sensor networks

Power consumption in wireless sensor networks is the most important challenge in this type of network. Optimal node location and routing are two well-known solutions for reducing energy consumption in wireless sensor networks that have been extensively studied by researchers. Researchers have always been looking for a way to reduce energy consumption and thus extend the life of a wireless network. Various techniques and methods have been proposed for this purpose, each of which has its own strengths and weaknesses. In this paper, we increase the life of the network and optimize energy consumption using a combination of particle swarm optimization algorithm and bee algorithm. This study covers ۱۰۰ to ۱۵۰۰ replicates. The evaluation results show that the hybrid algorithm significantly increases the life of the network to the previous level and reduces the energy consumption, and even in some special conditions, the energy consumption is improved compared to the particle swarm method. The number of failed nodes is often about less than the PSO method. A wireless sensor network is a set of sensor nodes located in an area to collect data from the environment. In order to collect more and more of this data, nodes need longer life in the network, in other words, they must operate optimally in terms of energy consumption. For this purpose, the nodes are divided into clusters that send the collected data to the clustered node. Sarkhosheh node is responsible for collecting all data from its member nodes and sending it to the base station. In this paper, an energy-aware routing method in wireless sensor networks will be presented. In the proposed method, fuzzy logic and genetic algorithm are used to improve the AOMDV routing protocol and LEACH clustering protocol and a new routing scheme with minimum energy consumption is proposed. In the proposed diagram, the energy consumption of the nodes is greatly reduced and the life of the network is increased. The simulation results indicate the high efficiency of the proposed schema compared to some other methods.

Keywords:

Energy-based intelligent , wireless sensor networks