Abstract
Wireless sensor networks (WSNs) is a multi-hop wireless network composed of a group of static or mobile sensor nodes in the form of self-organization. Uneven distribution of nodes often leads to the problem of over coverage and incomplete coverage of monitoring areas. To solve this problem, this paper establishes a network coverage optimization model and proposes a coverage optimization method based on an improved hybrid strategy weed algorithm (LRDE_IWO). The improvement of the weed algorithm includes three steps. Firstly, the standard deviation of normal distribution based on the tangent function is used as the seed’s new step size in the seed diffusion stage to balance the ability of the global search and local search of weed algorithm. Secondly, to avoid the problem of premature convergence, a disturbance mechanism combining enhanced Levy flight and the adaptive random walk strategy is proposed in the process of seed breeding. Finally, in competition of invasive weed stage, the differential evolution strategy is introduced to optimize the competition operation process and speed up convergence. The improved weed algorithm is applied to coverage optimization of WSNs. The simulation results show that the coverage rate of LRDE_IWO is increased by about 1% to 6% compared with the original invade weed algorithm (IWO) and the differential evolution invasive weed optimization algorithm (DE_IWO), and the coverage rate of the LRDE_IWO algorithm is increased by 4.10%, 2.73% and 1.19%, respectively, compared with the antlion optimization algorithm (ALO), the fruit fly optimization algorithm (FOA) and the gauss mutation weed algorithm (IIWO). The results prove the superiority and validity of the improved weed algorithm for coverage optimization of wireless sensor networks.
Highlights
With the development of wireless communication technology, wireless sensor networks (WSNs) have been widely used in many fields of social life because of its advantages of low-power consumption, low cost, wide coverage and diversified integration functions [1]
Inspired by [12], this paper proposes an improved weed algorithm based on a hybrid strategy (LRDE_IWO), to solve the problem of slow convergence speed of the invade weed algorithm (IWO) algorithm and to ensure it falls into the local optimum, it can significantly improve the coverage rate of WSN
The standard deviation of the tangent function strategy is utilized to improve the convergence speed of the algorithm, the disturbance strategy of enhanced Levy flight and Sensors 2021, 21, 5869 the adaptive random walk strategy is used to expand the diversity of the weeds and the differential evolution strategy is used to enhance the ability of the algorithm to jump out of the local optimum
Summary
With the development of wireless communication technology, wireless sensor networks (WSNs) have been widely used in many fields of social life because of its advantages of low-power consumption, low cost, wide coverage and diversified integration functions [1]. Inspired by [12], this paper proposes an improved weed algorithm based on a hybrid strategy (LRDE_IWO), to solve the problem of slow convergence speed of the IWO algorithm and to ensure it falls into the local optimum, it can significantly improve the coverage rate of WSN. The standard deviation of the tangent function strategy is utilized to improve the convergence speed of the algorithm, the disturbance strategy of enhanced Levy flight and Sensors 2021, 21, 5869 the adaptive random walk strategy is used to expand the diversity of the weeds and the differential evolution strategy is used to enhance the ability of the algorithm to jump out of the local optimum. In the section “WSN Coverage Optimization”, the steps and algorithm flow of how the improved weed algorithm is applied to WSN node deployment optimization are described in detail. The work of this paper is summarized in the “Conclusion” section
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