Abstract:

An improved sparrow search algorithm led by virtual force is put forward,aiming to address the issues of a low coverage rate,large coverage redundancy,and long node moving distance in the process of monitoring the target region of wireless sensor networks.To begin with,the population is initialized using the Tent chaotic map in order to improve the population’s diversity.Second,a virtual force algorithm is presented to direct the sparrow population’s discoverers to search for better positions.That is to say,interaction forces between nodes,boundaries,and barriers might assist the algorithm in more wide exploration by guiding discoverers into a more beneficial place.To keep the algorithm out of the local optimum dilemma,the Levy flight disturbance approach is then used to optimize the followers' position.Ultimately,the method of random reverse learning is utilized to improve the position of the global optimal individual,thus allowing for local optimization to occur in the nearby area,and improving the algorithm’s population diversity and convergence speed.Experimental findings demonstrate that the proposed algorithm can enhance the coverage rate while reducing the node’s moving distance and achieving a more uniform distribution when compared to other traditional algorithms.Furthermore,the proposed algorithm incorporates the virtual force algorithm’s effective obstacle avoidance capabilities with the sparrow search algorithm’s potent optimization seeking capabilities in the obstacle-filled monitoring area,which makes sure that the nodes successfully avoid impediments while allowing the network to deploy the nodes' placements in a reasonable manner.The approach is hence more useful for real-world applications.

Key words: wireless sensor network, node deployment, multi-objective optimization, sparrow search algorithm, virtual force algorithm, fusion algorithm