Abstract
The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.
Highlights
With the rapid development of wireless communication, electronic technology, computer network technology, and sensor means, wireless sensor networks (WSNs) came into being [1]. e wireless sensor network, which is composed of a large number of the sensor nodes with limited energy in a selforganizing and multihop manner cooperatively senses, collects and processes the information of the sensed objects in the coverage area of the network, and sends the information to the network owner [2, 3]. erefore, it can be widely used in the modern military and urban construction, such as environmental monitoring, target tracking, battlefield monitoring, smart home, and so on [4]
The sensor nodes are usually randomly scattered in the air, which results in the randomness deployment of the nodes, and it is difficult to meet the monitoring of the entire area. e research of the coverage optimization of WSNs is related to a series of problems, such as the communication quality, the communication speed, and the network life cycle
Many researchers apply swarm intelligence optimization algorithm to the coverage optimization of wireless sensor networks, such as particle swarm, ant colony, artificial bee colony, and so on. These algorithms have the problems of low solution accuracy, slow convergence speed, and easiness to fall into local optimum when optimizing network coverage, which will lead to poor network data transmission accuracy and node redundancy. erefore, in this paper, we propose a grey wolf algorithm optimized by simulated annealing method (SA-grey wolf optimization algorithm (GWO)) to effectively arrange the sensor nodes in the monitoring area to improve coverage, reduce the redundancy of the sensor node, and extend the network life cycle
Summary
With the rapid development of wireless communication, electronic technology, computer network technology, and sensor means, wireless sensor networks (WSNs) came into being [1]. e wireless sensor network, which is composed of a large number of the sensor nodes with limited energy in a selforganizing and multihop manner cooperatively senses, collects and processes the information of the sensed objects in the coverage area of the network, and sends the information to the network owner [2, 3]. erefore, it can be widely used in the modern military and urban construction, such as environmental monitoring, target tracking, battlefield monitoring, smart home, and so on [4]. Erefore, in this paper, we propose a grey wolf algorithm optimized by simulated annealing method (SA-GWO) to effectively arrange the sensor nodes in the monitoring area to improve coverage, reduce the redundancy of the sensor node, and extend the network life cycle. E ant colony algorithm involves too many parameters, forming a complex network model, which in turn causes practical deployment difficulties In response to these problems, in this paper, we use the improved grey wolf optimization algorithm in the previous chapter to optimize the coverage of WSNs, which can effectively solve the problem of the coverage optimization of the sensor nodes for WSNs. On the basis of summarizing the previous studies, in this paper, we analyze the mathematic model of node coverage in wireless sensor networks and propose a novel coverage optimization strategy based on grey wolf algorithm optimized by simulated annealing method
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