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An Energy-Saving Unbiased Gray-Prediction Algorithm of Internet of Things

Chuiwei Lu, Yunfang Feng, Zhiyuan Liu, Hao Xiong


Internet of Things (IOT) is usually powered by battery, so its energy is limited, which will reduce the lifetime of IOT. The paper proposes a sleep/activation scheduling and unbiased gray-prediction algorithm, it schedule the nodes in IOT to take turns sleep/activation according to the residual energy of nodes, so as to save the energy of nodes highly and extend the lifetime of IOT. In addition, the algorithm comprises time- correlated and space- correlated data prediction strategies, which can predict the missing data of the sleeping nodes more accurately, so as to avoid the incomplete perception data resulting from the node sleeping. Experiments indicate that the algorithm performs high accuracy in data prediction and excellent energy-saving character. Its performance is significantly better than many energy-saving prediction algorithms with single function.


Internet of Things, Sleeping schedule, Gray data prediction, Energy saving.

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