Abstract
The literature approaches, devoted to sensor improve network coverage, are deterministic in terms of deployment environment and node configuration parameters. Nevertheless, this type of approaches has not proven to be very successful in uncertain deployment environments. This paper aims to deal with this issue using theories of uncertainty. We consider deployment environment’s imperfections and the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in wireless sensor networks (WSNs) is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to Schedule the Active/Passive State of Sensor nodes Strategy (FP-3SNS). This model helps to plan the scheduling of node states (Active/Passive) based on possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated our model (FP-3SNS) with (a) a running example (that shows the best choice of the active node with a probability of 0.81215); (b) a statistical evaluation (calculation of the confidence interface), where the average coverage reliability at 95% of FP-3SNS use is between (92.94, 96.27); and (c) a comparison with maximum sensing coverage region problem (MSCR), coverage maximization with sleep scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP), Probing Environment and Adaptive Sleeping with Location Information Protocol (PEAS-LI), and Variable Length Particle Swarm Optimization Algorithm with a Weighted Sum Fitness Function (WS-VLPSO). The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem and the proposed model maintained a coverage between 99.99 and 90.00% for a significant period of time.
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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
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Boualem, A., De Runz, C., Ayaida, M. et al. A fuzzy/possibility approach for area coverage in wireless sensor networks. Soft Comput 27, 9367–9382 (2023). https://doi.org/10.1007/s00500-023-08406-3
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DOI: https://doi.org/10.1007/s00500-023-08406-3