Genetic-Voronoi algorithm for coverage of IoT data collection networks

Wajih Abdallah (1, 2, 3) , Sami Mnasri (1, 4) , Thierry Val (1, 5)

1 IRIT-RMESS - Réseaux, Mobiles, Embarqués, Sans fil, Satellites
2 Université de Gafsa
3 ISAM G - Institut Supérieur d'arts et métiers de Gafsa
4 University of Tabuk
5 UT2J - Université Toulouse - Jean Jaurès

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.

Keywords: Deployment; Voronoi Diagram; Genetic Algorithm; IoT collection networks; Coverage; Optimization

Domains: Computer Science [cs]; Networking and Internet Architecture [cs.NI]

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⟩