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


The Internet of Things (IoT) has received an increasing attention in recent times. Embedded intelligent terminal (EIT), an imperative half of IoT, works not only as a sensor however conjointly as a primary processor. Due to the restricted power resource of EIT, it's vital to review how to enhance the efficiency of its power use. To tackle this problem, we tend to propose an energy-saving algorithm, Bayesian idle time prediction (BIP). The basic plan of BIP is to explore historical info and acquire a higher estimation of idle time. During this paper, we provide a theoretical analysis of BIP and compare our method with 3 existing algorithms [weighted idle-time-prediction (IP) algorithm, IP algorithm, and running time fastened threshold in IP algorithm] with respect to energy-saving potential, in addition to system delay under a random number of tasks. Each simulation and field experiment results demonstrate the advantages of our algorithm in energy saving.

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