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

TL;DR

This paper presents a distributed method combining Voronoi diagrams and genetic algorithms to optimize the deployment of IoT objects for better coverage, efficiency, and network lifetime in indoor environments.

Contribution

It introduces a novel hybrid distributed approach (VD-GA) for IoT deployment that outperforms centralized algorithms in coverage and computational speed.

Findings

Improved coverage and RSSI in experiments

Enhanced network lifetime and neighbor connectivity

Faster computation compared to centralized methods

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. .