  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. .
