The Improvement of DV-Hop Model and Its Application in the Security Performance of Smart Campus

Aimin Yang, Qunwei Zhang, Yikai Liu and Ji Zhao
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Aimin Yang: Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
Qunwei Zhang: Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
Yikai Liu: Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
Ji Zhao: Modern Technology and Education Center, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2022, vol. 10, issue 15, 1-16

Abstract: In the smart campus, sensors are the basic units in the whole the Internet of Things structure, which play the role of collecting information and transmitting it. How to transmits more information at a certain power level has attracted the attention of many researchers. In this paper, the DV-Hop algorithm is optimized by combining simulated annealing-interference particle swarm optimization algorithm to improve the node localization of wireless sensor networks and enhance the security performance of smart campus. To address the problem that particle swarm optimization easily falls into local optimum, a perturbation mechanism is introduced in the basic particle swarm optimization algorithm. Meanwhile, the acceptance probability P is introduced in the simulated annealing algorithm to determine whether a particle is accepted when it “flies” to a new position, which improves the probability of finding a global optimal solution. Comparing the average localization error and optimization rate of the DV-Hop algorithm, PSO-DV-Hop algorithm, and the optimized algorithm. The results show a greater advantage of the algorithm. This will greatly enhance the safety performance and efficiency of the smart campus.

Keywords: improved DV-Hop model; smart campus; wireless sensor network; partial least squares regression; perturbed particle swarm optimization; simulated annealing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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