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Multi Objective Optimization Approach for WSN Based on Reinforcement Learning

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Social Networks Analysis and Mining (ASONAM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15214))

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Abstract

Wireless sensor networks (WSNs) are gradually invading our daily lives, offering us new services every day. They can be found in applications that affect us more and more. First used to monitor the environment and urban areas, they then provided support for first-aid and military surveillance activities. Now they are appearing in applications even closer to home to improve our lifestyle, such as guiding us to available parking spaces or informing us about air quality. The wide range of applications for wireless sensor networks has prompted several researchers to work towards a WSN with lower deployment costs while maximizing network lifetime and coverage. In this paper, an optimization approach-based Q-learning algorithm for optimal coverage of heterogeneous sensor networks is proposed. The findings of the simulation prove that the proposed approach maintains network coverage while using the minimum amount of energy, compared with other approaches.

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Authors and Affiliations

  1. Research Team in Intelligent Machines, Gabes University, Gabes, Tunisia

    Faten Hajjej, Monia Hamdi & Mourad Zaied

  2. Gabes University Hospital, Public Health Ministry, Gabes, Tunisia

    Faten Hajjej

Authors
  1. Faten Hajjej
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  2. Monia Hamdi
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  3. Mourad Zaied
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Corresponding author

Correspondence to Faten Hajjej .

Editor information

Editors and Affiliations

  1. IT University of Copenhagen, Copenhagen, Denmark

    Luca Maria Aiello

  2. Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Tanmoy Chakraborty

  3. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

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Hajjej, F., Hamdi, M., Zaied, M. (2025). Multi Objective Optimization Approach for WSN Based on Reinforcement Learning. In: Aiello, L.M., Chakraborty, T., Gaito, S. (eds) Social Networks Analysis and Mining. ASONAM 2024. Lecture Notes in Computer Science, vol 15214. Springer, Cham. https://doi.org/10.1007/978-3-031-78554-2_6

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  • DOI: https://doi.org/10.1007/978-3-031-78554-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78553-5

  • Online ISBN: 978-3-031-78554-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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