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The Internet of Things (IoT)-Cloud combines the IoT and cloud computing, which not only enhances the IoT's capability but also expands the scope of its applications. However, it exhibits significant security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however, traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.

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Thermoelectric (TE) materials provide a solid-state solution in waste heat recovery and refrigeration. During the past few decades, considerable effort has been devoted towards improving the performance of TE materials, which requires the optimization of multiple interrelated properties. A fundamental understanding of the interaction processes between the various energy carriers, such as electrons and phonons, is critical for advances in the development of TE materials. However, this understanding remains challenging primarily due to the inaccessibility of time scales using standard atomistic simulations. Machine learning methods, well known for their data-analysis capability, have been successfully applied in research on TE materials in recent years. Here, an overview of the machine learning methods used in thermoelectric studies is provided, with the role that each machine learning method plays being systematically discussed. Furthermore, to date, the scale of thermoelectric-related databases is much smaller than those in other fields, such as e-commerce, image identification, and speech recognition. To overcome this limitation, possible strategies to utilize small databases in promoting materials science are also discussed. Finally, a brief conclusion and outlook are presented.