Solving the problem of maximum coverage in wireless sensor networks using genetic algorithm

Coverage is achieved by using wireless sensor nodes to track areas of interest and collect data. The sensors have the ability to collect, transmit, and receive data. The largest coverage area covers the largest possible area that can be monitored with the least number of sensors. To achieve this goal, many algorithms can be used. Genetic algorithm is a method used to obtain the best results. This algorithm tries to reach the optimum solution by selecting the best data in each generation. These stages are selection, crossover, and mutation stages. In the selection stage, the best data is selected based on the fitness function. These data are represented in the form of chromosomes. In the crossover stage, new chromosomes are created for the next generation. In the mutation stage, random data is entered to prevent the formation of similar chromosomes. These stages are repeated to obtain the best data and new generations are created. This process continues until the optimum solution is reached This thesis examines different stages of genetic algorithms using sensor nodes with different features. Multiple methods are selected and used for each stage. For the selection stage, both the roulette wheel and tournament methods are utilized. Six methods are used for the crossover stage, including single-point, two-point, uniform, arithmetic, Min-Max arithmetic, and BLX-α. The mutation stage employs four methods: random point, inversion, swap, and shuffle. The results of this study indicate that the random point mutation method produced the best results with the BLX-α and arithmetic methods. The roulette wheel and tournament methods had similar performance during the selection stage. The algorithm produced similar results during the applications and increased coverage by 13%. The worst result was obtained when using the roulette wheel selection method, Min-Max arithmetic crossover method, and shuffle mutation, with no change in coverage area.