Distributed Approach for the Indoor Deployment of Wireless Connected Objects by the Hybridization of the Voronoi Diagram and the Genetic Algorithm
by Wajih Abdallah 1,2,* 
, Sami Mnasri 1,3 and Thierry Val 1
1 UT2J, CNRS-IRIT (RMESS), University of Toulouse, Toulouse, France
2 ISAM Gafsa, Dept. of Design, University of Gafsa, Tunisia
3 University of Tabuk, Community College, Dept. of computer sciences, Tabuk, KSA
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 2, Page # 10-23, 2022; DOI: 10.55708/js0102002
Keywords: Coverage, Distributed Deployment, Genetic Algorithm, IoT collection networks, Optimization, Voronoi Diagram
Received: 18 January 2022, Revised: 26 February 2022, Accepted: 27 February 2022, Published Online: 14 March 2022
AMA Style
Abdallah W, Mnasri S, Val T. Distributed approach for the indoor deployment of wireless connected objects by the hybridization of the Voronoi diagram and the genetic algorithm. Journal of Engineering Research and Sciences. 2022;1(2):10-23. doi:10.55708/js0102002
Chicago/Turabian Style
Abdallah, Wajih, Sami Mnasri, and Thierry Val. “Distributed Approach for the Indoor Deployment of Wireless Connected Objects by the Hybridization of the Voronoi Diagram and the Genetic Algorithm.” Journal of Engineering Research and Sciences 1, no. 2 (2022): 10–23. https://doi.org/10.55708/js0102002.
IEEE Style
W. Abdallah, S. Mnasri, and T. Val, “Distributed approach for the indoor deployment of wireless connected objects by the hybridization of the Voronoi diagram and the genetic algorithm,” Journal of Engineering Research and Sciences, vol. 1, no. 2, pp. 10–23, 2022.
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Abstract
IoT data collection networks have recently become one of the important research areas due to their fundamental role and wide application in many domains. The establishment of networks of objects is based essentially on the deployment of connected objects to process the collected data and transmit them to the various locations. Subsequently, a large number of nodes must be adequately deployed to achieve complete coverage. This manuscript introduces a distributed approach, which combines the Voronoi Diagram and the Genetic algorithm (VD-GA), to maximize the coverage of a region of interest. The Voronoi diagram is used to divide region into cells and generate initial solutions that present the positions of the deployed IoT objects. Then, a genetic algorithm is executed in parallel in several nodes to improve these positions. The developed VD-GA approach was evaluated on an experimental environment by prototyping on a real testbed utilizing M5StickC nodes equipped with ESP32 processor. The experiments show that the distributed approach provided better degree of coverage, RSSI, lifetime and number of neighboring objects than those given by the original algorithms in terms of the suggested distributed Genetic-Voronoi algorithm outperforms the centralized one in terms of speed of computing.
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