Localization in IoT by using Fractional Order Chaotic Particle Swarm Algorithm Optimization

The accurate and fast localization in the Internet of Things is an essential requirement in expanding the use of applications for these networks. Due to limited computing power of sensor nodes and the limited power capacity of these nodes, reducing computational complexity and reducing communication overhead in localization algorithms is critical. In this paper, a particle swarm optimization based algorithm is proposed for localization of sensor nodes in IoT. In general, trapping in local optima and the slow convergence rate are two main weaknesses of the classic PSO. In the proposed algorithm, using the chaos theory, trapping in local optima is prevented and convergence of the algorithm is improved. In addition, using the fractional derivatives, the particles convergence rate accelerates to the optimal solution. The performance evaluation of the algorithm is performed by its implementation on specific test functions. Simulation results exhibit the effectiveness of the proposed algorithm in localization of sensor nodes in IoT.