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Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization

Author

Listed:
  • Alessandro Niccolai

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Francesco Grimaccia

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Marco Mussetta

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Riccardo Zich

    (Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

Abstract

Wireless Sensor Networks (WSN) have been widely adopted for years, but their role is growing significantly currently with the increase of the importance of the Internet of Things paradigm. Moreover, since the computational capability of small-sized devices is also increasing, WSN are now capable of performing relevant operations. An optimal scheduling of these in-network processes can affect both the total computational time and the energy requirements. Evolutionary optimization techniques can address this problem successfully due to their capability to manage non-linear problems with many design variables. In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed.

Suggested Citation

  • Alessandro Niccolai & Francesco Grimaccia & Marco Mussetta & Riccardo Zich, 2019. "Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization," Mathematics, MDPI, vol. 7(4), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:4:p:315-:d:217960
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    References listed on IDEAS

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    1. Emanuele Ogliari & Alessandro Niccolai & Sonia Leva & Riccardo E. Zich, 2018. "Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed," Energies, MDPI, vol. 11(6), pages 1-16, June.
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    Cited by:

    1. Diyin Tang & Xuan Wang & Junwei Di & Guofeng Zheng & Jinsong Yu, 2023. "Joint optimization of inspection and maintenance strategy for complex multi-component systems using a quantum-inspired genetic algorithm," Journal of Risk and Reliability, , vol. 237(5), pages 966-979, October.

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