Abstract
Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.
Highlights
Heterogeneous wireless sensor networks (HWSNs) are a network technology that integrates wireless communication, sensors, embedded computing, and distributed information processing
A coverage optimization algorithm based on an improved whale optimization algorithm (WOA) for HWSNs is proposed in this article, increases the coverage of the network, reduces the network energy consumption, and prolongs the lifetime of the network
The maximum value of the objective function of the coverage optimization of the HWSN is solved, based on the coverage rate optimized by the Sine and Cosine Algorithm (SCA)-WOA algorithm, and the distribution position of all the sensor nodes in the area to be tested after the optimized deployment is obtained.[28]
Summary
Heterogeneous wireless sensor networks (HWSNs) are a network technology that integrates wireless communication, sensors, embedded computing, and distributed information processing. Traditional methods are still innovating, such as the energy efficiency and coordination mechanism of k-fold coverage hole detection in sensor networks, and the sleep mechanism of network nodes based on learning.[7] At the same time, emerging algorithms based on biological heuristics and evolutionary algorithms have received a lot of research, and a series of results have been achieved Such as artificial bee colony algorithm based on the Voronoi diagram, artificial fish swarm algorithm, firefly optimization algorithm, particle swarm optimization (PSO) algorithm, genetic algorithm, and other methods of heterogeneous wireless sensor coverage deployment strategies.[8] Based on this, a coverage optimization algorithm based on an improved whale optimization algorithm (WOA) for HWSNs is proposed in this article, increases the coverage of the network, reduces the network energy consumption, and prolongs the lifetime of the network
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