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Genetic-Voronoi algorithm for coverage of IoT data collection networks

Abstract : IoT data collection networks, formerly known as wireless sensor networks, have become one of the most active areas of research in the field of information technology. The deployment of connected objects represents a fundamental phase on the establishment of IoT collection networks. The IoT deployment problem consists in positioning all the sensors constituting the network. This paper proposes an approach maximizing the coverage of a region of interest by the hybridization between the Voronoi diagram and the genetic algorithm. The first algorithm divides the field into cells and generates initial solutions (positions of deployed IoT objects). The latter algorithm is used to improve these positions in order to maximize the overall coverage of the region of interest. Obtained results reveal that the performance of the hybrid algorithm exceeds that of the original algorithms in terms of coverage degree, RSSI, lifetime and number of neighbor of objects.
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https://hal-univ-tlse2.archives-ouvertes.fr/hal-03039800
Contributor : Thierry Val <>
Submitted on : Friday, December 4, 2020 - 8:59:09 AM
Last modification on : Wednesday, June 9, 2021 - 10:00:35 AM
Long-term archiving on: : Friday, March 5, 2021 - 6:17:43 PM

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  • HAL Id : hal-03039800, version 1

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Wajih Abdallah, Sami Mnasri, Thierry Val. Genetic-Voronoi algorithm for coverage of IoT data collection networks. 30th International Conference on Computer Theory and Applications (ICCTA 2020), Arab Academy for Science, Technology & Maritime Transport (AASTMT); Computer Scientific Society (CSS); IEEE Alexandria subsection, Dec 2020, Alexandrie, Egypt. ⟨hal-03039800⟩

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