Scheduling Optimization Based on Energy Prediction Using ARIMA Model in WSN

Abstract

Wireless sensor networks (WSNs) have attracted great attention because of their applicability in a variety of applications in day-to-day life such as structural monitoring, healthcare, surveillance, etc. Energy conservation is a challenging issue in the context of WSN as these networks are usually deployed in hazardous and remote applications where human intervention is not possible; hence, recharging or replacing the battery of sensor nodes is not feasible often. Apart from energy conservation, target coverage is also a major challenge. Scheduling the nodes to exist in active and sleep modes is an efficient mechanism to address the energy efficiency and coverage problem. The chapter proposes an ARIMA model-based energy consumption prediction approach such that the set cover scheduling may be optimized. The chapter compares the efficiency of several ARIMA-based models, and the results show that the ARIMA (0,1,2) model provides best results for the considered scenario in terms of energy consumption.