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IoT data collection networks have recently become one of the importantresearch areas due to their fundamental role and wide application in manydomains. The establishment of networks of objects is based essentially on thedeployment of connected objects to process the collected data and transmit themto the various locations. Subsequently, a large number of nodes must beadequately deployed to achieve complete coverage. This manuscript introduces adistributed approach, which combines the Voronoi Diagram and the Geneticalgorithm(VD-GA), to maximize the coverage of a region of interest. The Voronoidiagram is used to divide region into cells and generate initial solutions thatpresent the positions of the deployed IoT objects. Then, a genetic algorithm isexecuted in parallel in several nodes to improve these positions. The developedVD-GA approach was evaluated on an experimental environment by prototyping on areal testbed utilizing M5StickC nodes equipped with ESP32 processor. Theexperiments show that the distributed approach provided better degree ofcoverage, RSSI, lifetime and number of neighboring objects than those given bythe original algorithms in terms of the suggested distributed Genetic-Voronoialgorithm outperforms the centralized one in terms of speed of computing. .

Distributed approach for the indoor deployment of wireless connected objects by the hybridization of the Voronoi diagram and the Genetic Algorithm