Exploiting Industrial Big Data Strategy for Load Balancing in Industrial Wireless Mobile Networks

Abstract

In the era of big data, traditional industrial mobile wireless networks cannot effectively handle the new requirements of mobile wireless big data networks arising from the spatio-temporal changes of a nodes traffic load. From the perspective of load balancing and energy efficiency, industrial big data (IBD) brings new transmission challenges to industrial wireless mobile networks (IWMNs). Previous research works have not considered dynamic changes related to the traffic and mobility of IWMNs. In this paper, using an IBD technique, we propose a novel second-deployment and sleep-scheduling strategy (SDSS) for balancing load and increasing energy efficiency, while taking the dynamic nature of the network into consideration. SDSS can be divided into two stages. In the first stage, changes in the traffic of every network grid and its maximum traffic load at different times are calculated using big data analysis techniques. In the second stage, a second-deployment method for the cluster head nodes (CHNs), based on each grids maximum traffic load, is adopted. To save energy, based on their position and traffic states, a sleep-wake scheduling is presented for the CHNs. Simulations results verify the effectiveness of this methodology to save energy and obtain a traffic balance, which is more efficient than obtained through traditional methods.