Energy-efficient load balancing in wireless sensor network: An application of multinomial regression analysis

Abstract

Wireless sensor networks have become integral components of modern and smart environments. The main challenge for such important data-acquisition tools is the limited amount of available energy. In integrated networks in which cloud systems act as a self-regulatory controller, distributing the computational load among available partitions with rich energy will positively influence the lifetime of the whole network. This article investigates the application of a modified version of multinomial logistic regression model that incorporates spatiotemporal aspects of data collected from smart environments. The contribution of this research is to propose an energy-efficient load balancing strategy based on the proposed prediction model for the purpose of enhancing the lifetime of wireless infrastructure. Our proposed algorithm grows linearly in terms of time complexity. Extensive experiments have been performed to measure the prediction error rate and the energy consumption. The results showed that the proposed model significantly reduces the error rate and distinctly maximizes the lifetime of wireless sensor networks.