Wireless sensor network energy efficiency optimized clustering method based on whale swarm algorithm

Abstract

The invention discloses a wireless sensor network energy efficiency optimized clustering method based on a whale swarm algorithm. The method includes: a collection node respectively sends initial configuration information to all cluster head nodes and common nodes, and collects and acquires network information; optimal routing schemes and optimal clustering schemes from all the cluster head nodesto the collection node are obtained according to the current network information; clustering routing configuration is performed on a whole wireless sensor network according to the optimal clustering schemes and the optimal routing schemes; and the cluster head nodes perform data fusion on clusters which the cluster head nodes belong to and sends the data to the collection node, and information collection and acquisition can be completed. According to the technical scheme of the method, the improved whale swarm algorithm is introduced to the energy efficiency optimized clustering problem, multimodal optimization of the solution capability of the problem is realized by employing the improved whale swarm algorithm, and the energy efficiency optimized clustering problem of the wireless sensornetwork is solved; besides, a routing algorithm can effectively balance the energy consumption of the cluster head nodes during data forwarding so that the network life cycle is further prolonged.

Description

technical field [0001] The invention belongs to the field of wireless sensor network topology control, and in particular relates to a clustering method for energy efficiency optimization of a wireless sensor network based on a whale swarm algorithm. Background technique [0002] With the rapid development of microelectronics technology, computing technology, wireless communication technology and distributed information processing technology, wireless sensor network has become one of the key technologies in the next generation network and one of the most important emerging technologies in the 21st century. In 1999, the American "Business Weekly" listed WSNs as one of the 21 most influential technologies in the 21st century. In 2003, the MIT "Technology Review" listed WSNs as the top ten new technologies that will change people's lives in the future. . [0003] In most wireless sensor application scenarios, the bottleneck of wireless sensors is that their energy is very limit...

AI Technical Summary

Problems solved by technology

The method of the technical solution of the present invention aims at the situation that the existing whale algorithm is difficult to deal with the clustering problem of the wireless sensor network, based on the improved whale swarm algorithm, the clustering calculation of the wireless sensor network is performed, and the clustering scheme of the entire wireless sensor network is obtained, and the existing wireless sensor network clustering scheme is established. Considering the standard deviation of the life cycle of the cluster head node, and the fitness function of the energy consumption of ordinary sensor nodes and its standard deviation, it solves the problems of unreasonable distribution of member nodes in the wireless sensor network and uneven energy consumption of the cluster head nodes, thus balancing the wireless network. Energy consumption of sensor networks, effectively improving network energy efficiency and prolonging network life cycle

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