Continuous body temperature measurement (CBTM) is of great significance for human health state monitoring. To avoid interfering with users' daily activities, CBTM is usually achieved using wearable noninvasive thermometers. Current wearable noninvasive thermometers employ steady-state models used in nonwearable thermometers; as a result, the reaction time is long and the measurement can be disturbed by users' activities. However, there is no work to solve these issues. In this paper, first, differences between wearable and nonwearable temperature measurement are analyzed. Second, the relationship among the human body temperature, the skin temperature, and the device temperature is modeled based on artificial neural networks (ANNs). Third, this paper proposes a novel multiple ANNsbased wearable CBTM method. Experiments show that the reaction time of the proposed method is about one-tenth of that of other popular wearable noninvasive CBTM methods, while the accuracy and the robustness are improved.Index Terms-Artificial neural network (ANN), continues body temperature measurement (CBTM), noninvasive, wearable computing.

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To optimize the energy efficiency of edge computing system with energy harvesting, this paper proposes an energy-efficient task offloading method optimized by differential evolution. First, a wireless edge computing network model is established to analyze the energy harvesting, task offloading and task calculation of the system, as well as the total number of calculated bits and total energy consumption of the system. Second, according to the total number of calculated bits and total energy consumption of the system, an objective function is established to optimize the energy efficiency of system, and a differential evolution based optimization method is proposed, with which the optimal energy efficiency of system calculation, offloading time, calculation time and frequency are obtained. Experimental results show that the proposed method can not only achieve better convergence effect, but also can effectively solve the energy shortage problem of the micro-equipment and extend the service life of the equipment.INDEX TERMS Edge computing, task offloading, energy harvesting, differential evolutionary algorithm.PENG ZENG received the Ph.D. degree in mechatronic engineering from the Graduate School of the

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