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<p>A typical directional sensor network (DSN) includes numerous directional sensors. Each sensor in a DSN can be configured in one of the possible directions with one of the possible adjustable ranges. Based on the sensor’s configuration, the battery usage pattern differs. Although some battery harvesting methodologies are adopted, either energy management or network lifespan maximization is still a challenge. This paper formulates the expansion of the network lifespan in DSNs as a discrete NP-Hard optimization problem along with the coverage of all targets at the same time. To solve this combinatorial problem, a discrete hybrid shuffled frog-leaping (D-HSFL) algorithm is developed. The advanced shuffled frog-based algorithm takes benefit of several plus points. Firstly, it utilizes the exploration operator for global search. Secondly, it randomly calls exploitation operator for strengthen the local search by running Hill-Climbing procedure. Thirdly, it engages a new fitness function which considers both lowest energy consumption for found cover set and also the cover set having the lowest overlap in observing targets in the observing field. To verify the proposed D-HSFL in solving targets coverage and network lifespan maximization, several scenarios are defined. Then, the proposed algorithm are tested on the abundant random datasets in the same conditions against existing state-of-the-arts in different scenarios. The simulation results witness that D-HSFL has the amount of 12.28%, 12.59%, 29.70%, and 30.82% averagely enhancement in terms of network lifespan extension in comparison with CGA, DHGWO, Heuristic2, and Heuristic1 algorithms respectively.</p>

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A hybrid shuffled frog-leaping scheduling algorithm for power management of directional sensor networks

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Peyman Mokaripoor,
  • Mirsaeid Hosseini Shirvani,
  • Hamid Reza Ghaffari,
  • Reza Noorian Talouki

摘要

A typical directional sensor network (DSN) includes numerous directional sensors. Each sensor in a DSN can be configured in one of the possible directions with one of the possible adjustable ranges. Based on the sensor’s configuration, the battery usage pattern differs. Although some battery harvesting methodologies are adopted, either energy management or network lifespan maximization is still a challenge. This paper formulates the expansion of the network lifespan in DSNs as a discrete NP-Hard optimization problem along with the coverage of all targets at the same time. To solve this combinatorial problem, a discrete hybrid shuffled frog-leaping (D-HSFL) algorithm is developed. The advanced shuffled frog-based algorithm takes benefit of several plus points. Firstly, it utilizes the exploration operator for global search. Secondly, it randomly calls exploitation operator for strengthen the local search by running Hill-Climbing procedure. Thirdly, it engages a new fitness function which considers both lowest energy consumption for found cover set and also the cover set having the lowest overlap in observing targets in the observing field. To verify the proposed D-HSFL in solving targets coverage and network lifespan maximization, several scenarios are defined. Then, the proposed algorithm are tested on the abundant random datasets in the same conditions against existing state-of-the-arts in different scenarios. The simulation results witness that D-HSFL has the amount of 12.28%, 12.59%, 29.70%, and 30.82% averagely enhancement in terms of network lifespan extension in comparison with CGA, DHGWO, Heuristic2, and Heuristic1 algorithms respectively.