Object tracking with knowledge of service quality based on quality criteria in wireless sensor networks environment

Abstract

Object tracking is known as an optimization problem that is proposed by researchers and applied by cloud providers. How to properly track objects in this issue is important in terms of quality and is used to overcome limitations such as reliability and accessibility. But since many articles in tracking objects lead to partial optimizations, evolutionary algorithms should be used to solve the partial optimization problem and create a space to increase the speed of convergence. The use of evolutionary algorithms, due to their random nature, can produce a dynamic space of the problem and guarantee the approach to the solutions of the problem. Therefore, in this article, the chaos-based particle density optimization algorithm has been used. The results of the simulations performed on the network have shown the improvement of the reliability in object tracking.