Bayesian Prediction-Based Energy-Saving Algorithm for Embedded Intelligent Terminal

The Internet of Things (IoT) has received an increasing attention in recent years. Embedded intelligent terminal (EIT), an indispensable part of IoT, works not only as a sensor but also as a primary processor. Due to the limited power resource of EIT, it is important to study how to improve the efficiency of its power use. To tackle this problem, we propose an energy-saving algorithm, Bayesian idle time prediction (BIP). The basic idea of BIP is to explore historical information and obtain a better estimation of idle time. In this paper, we provide a theoretical analysis of BIP and compare our method with three existing algorithms [weighted idle-time-prediction (IP) algorithm, IP algorithm, and running time fixed threshold in IP algorithm] with respect to energy-saving potential, as well as system delay under a random number of tasks. Both simulation and field experiment results demonstrate the advantages of our algorithm in energy saving.