Abstract
Path selection is one of the key technologies of wireless sensor network (WSN). A reasonable choice of coverage path can improve the service quality of WSN and extend the life cycle of WSN. Biogeography-based optimization (BBO) is widely used in the field of cluster intelligent optimization because its search method has a better incentive mechanism for population evolution. In this paper, the move-in and move-out operation and mutation operation of the BBO algorithm enable WSN to find an efficient routing path. In this paper, simulation experiments are carried out in two scenarios of regular deployment and random deployment of WSN nodes. The experimental results show that the quality of the WSN coverage path solution optimized by the BBO algorithm in the two scenarios is better than that of the particle swarm algorithm and genetic algorithm.
Highlights
With the gradual deepening research of quality of service (QoS) issues in WSN, we are paying more attention to the coverage path optimization of wireless sensor network [1, 2]
As the energy of sensor nodes in WSN is limited and cannot be maintained in time, establishing an efficient path connection method can effectively save network energy on the one hand and improve the quality of service of WSN on the other hand. e main goal of WSN coverage path optimization is to establish an efficient and reliable path from the sensor sending node to the receiving node and maximize the network life of the WSN as much as possible [3, 4]
Through the research and improvement of the biogeography-based optimization algorithm, it will make full use of the ability of the biogeography-based optimization algorithm to sense interactive data in multidimensional and high-dimensional problems to achieve the optimal construction of the wireless sensor network coverage path
Summary
With the gradual deepening research of quality of service (QoS) issues in WSN, we are paying more attention to the coverage path optimization of wireless sensor network [1, 2]. As the energy of sensor nodes in WSN is limited and cannot be maintained in time, establishing an efficient path connection method can effectively save network energy on the one hand and improve the quality of service of WSN on the other hand. (2) Explain in detail the main principles, mathematical model, migration, and mutation operation steps of the biogeography-based optimization algorithm, and apply it to the WSN coverage path optimization problem. Rough the comparison with the coverage path optimization experiments of the genetic algorithm and particle swarm optimization (PSO) algorithm, the effectiveness of the biogeography-based optimization algorithm for wireless sensor network coverage path optimization is further verified, and the local convergence problem in the iterative optimization process is solved and the robustness. The mathematical model of WSN coverage path optimization is as follows: nn
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