Wireless sensor network deployment optimization for a smart farming application: comparison of two Multi-Objective Evolutionary Algorithms

As it is one of the main means of insuring food security, agriculture has employed different technologies such as the Internet of Things and wireless sensor networks (WSN), to improve the quality and quantity of agricultural products, while preserving natural resources. But unfortunately, in agricultural plots where we have large surfaces of interest, the optimization of node deployment in a WSN remains among the major problems to be solved. In this work, we proposed a WSN node deployment optimization model for an agricultural application according to classical constraints of coverage, over-coverage, connectivity and nodes number, in addition to the nodes separating distance constraint which affects the quality of physical parameters models. We have applied two variants of Multi-Objective Genetic Algorithms, the Non Sorting Genetic Algorithm II (NS-GA II) and the Strength Pareto Evolutionary Algorithm II (SPEA II). As a result, for a 100 m2 plot, both algorithms ensured a communication rate of 100% while SPEA 2 presented a lower sensor number and over-coverage rates with a smaller separating distance, and the execution time of NSGA II was shorter with 11 s. Besides, both of them were greedy in terms of computation time with the increase in the size of the plots.