Abstract
Recently, many related algorithms have been proposed to find an efficient wireless sensor network with good sustainability, a stable connection, and a high covering rate. To further improve the coverage rate of movable wireless sensor networks under the condition of guaranteed connectivity, this paper proposes an adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network (DSO-WPOA). Firstly, a strategy of adaptive expansion based on a minimum overlapping full-coverage model is designed to achieve minimum overlap and no-gap coverage for the monitoring area. Moreover, the adaptive shrinking grid search wolf pack optimization algorithm (ASGS-CWOA) is improved to optimize the movable wireless sensor network, which is a discrete space oriented problem. This improvement includes the usage of a target–node probability matrix and the design of an adaptive step size method, both of which work together to enhance the convergence speed and global optimization ability of the algorithm. Theoretical research and experimental results indicate that compared with the coverage algorithm based on particle swarm optimization (PSO-WSN) and classical virtual force algorithm, the newly proposed algorithm possesses the best coverage rate, better stability, acceptable performance in terms of time, advantages in energy savings, and no gaps.
Highlights
The wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effectiveness, and deployable nature [1]
To solve the problems of large-scale wireless networks, with their wide variety, high volume, real-time velocity data and huge value, which leads to unique research challenges that are different from existing computing systems, in [3], researchers presented a survey of state-of-the-art big data analytics (BDA) approaches for large-scale wireless networks and the technical solutions to challenges in BDA for large-scale wireless networks
In [13], Chen et al proposed a novel wireless sensor network with energy-efficient coverage that achieved a good balance between target coverage and energy consumption by fusing the genetic algorithm (GA) and WSN
Summary
The wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effectiveness, and deployable nature [1]. In [13], Chen et al proposed a novel wireless sensor network with energy-efficient coverage that achieved a good balance between target coverage and energy consumption by fusing the genetic algorithm (GA) and WSN. In [22], Wu et al proposed a discrete wolf pack algorithm by redesigning the position of intelligent behaviors to solve the traveling salesman problem. Indiscrete [24], a new the algorithm was proposed to maximize the number of satisfied passengers, the total number of transfers, discrete wolf pack search (DWPS) algorithm was proposed to maximize the number of satisfied and the total travel timenumber of all served passengers for total a transit network problem. Passengers, the total of transfers, and the travel time ofdesign all served passengers for a the low coverage of WSN and good performance of the wolf pack optimization transit network design problem. Find the shortest path to minimize the energy consumption of the whole sensor network
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