Genetic Algorithm-based Crowdsensing for Cognitive Radio Networks

Abstract

One of the main challenges in cognitive radios is spectrum sensing. Cooperative spectrum sensing scheme among mobile users can be used to determine the usage profile of wide spectrum bands in a large geographical region. In a large mobile crowdsensing environment, the key step is to assign the sensing task among mobile users to maximize the spectrum sensing performance while reducing the cost incurred by the mobile users during the sensing process. In this paper, we propose two genetic algorithm-based approaches to solve the NP-hard problem of spectrum sensing task assignment among mobile users. The first algorithm uses a centralized genetic algorithm scheme to maximize the spectrum sensing utility function. The second algorithm uses an island genetic algorithm to assign the sensing task among mobile users in a distributive way. Simulation results show that both algorithms achieve comparable spectrum utility measure to the one obtained by running recently proposed particle swarm optimization and greedy approximation algorithms while reducing the running time of the algorithm by a significant factor. In addition, the island algorithm massively outperforms both algorithms in the running time by running the algorithm independently at each sensing location and exchanging the necessary information for the overlapping locations, removing the bottleneck of having a central spectrum profiling unit to assign the sensing tasks among mobile users.