A particle swarm optimization based load scheduling algorithm in cloud platform for wireless sensor networks

Abstract

Integration of wireless sensor network into cloud computing is a growing paradigm that supports a massive amount of applications in cloud computing, optimization of resources required in the machines. This integration requires the optimization of resources to efficiently complete the different tasks in the devices at cloud platform. This optimization can be done using load scheduling algorithms. These algorithms reduce overload and achieve higher throughput by maximizing the machine utilization concerning cost stabilization. There are lots of methods like First Come First Serve, Min-Min, Particle Swarm Optimization (PSO) for optimizing the load but we use Particle Swarm Optimization as it obtains the motivation from the social behavior of the flock of birds and analyses various approaches for load scheduling. In this paper, we propose the load scheduling algorithm based on PSO in wireless sensor networks for cloud computing to minimize total transfer time and cost stabilization. The proposed method is compared with the existing approaches used for load scheduling in Cloudlets. It is clear from the simulation results that the proposed method is more efficient because it minimizes the transfer time and cost than the conventional algorithms thereby making a system for cost stable.