GAMA: Genetic Algorithm for k-Coverage and Connectivity with Minimum Sensor Activation in Wireless Sensor Networks

In wireless sensor networks, ensuring k-coverage and connectivity is crucial in order to efficiently gather data and relay it back to the base station. We propose an algorithm to achieve k-coverage and connectivity in randomly deployed wireless sensor networks while minimizing the number of active sensors. It has been shown that selecting a minimum set of sensors to activate from an already deployed set of sensors is NP-hard. We address this by using a genetic algorithm that efficiently approximates a solution close to the optimal solution. The algorithm works by selecting random solutions and mutating them, retaining only the best solutions for the next generation until it converges to a near-optimal solution. We examine the time complexity of our approach and discuss possible optimizations. Our simulation results show that our approach works consistently across different types of wireless sensor networks and for different degrees of required coverage.