Zhang YLiu J.-HWang C.-YHUNG-YU WEI2021-09-022021-09-02202023274662https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092187332&doi=10.1109%2fJIOT.2020.2997091&partnerID=40&md5=b31393738783504c5d5505ea59ded658https://scholars.lib.ntu.edu.tw/handle/123456789/580921With the rapid development of deep learning technology, the modern Internet-of-Things (IoT) cameras have very high demands on communication, computing, and memory resources so as to achieve low latency and high accuracy live video analytics. Thanks to the mobile-edge computing (MEC), intelligent offloading to the MEC nodes can bring a lot of benefits, especially when the decomposable pipeline is adopted in the cloud-edge architecture. In this article, we provide decomposable intelligence on a cloud-edge IoT (DICE-IoT) framework to support joint latency- and accuracy-aware live video analytic services. Specifically, the intelligent framework enables the pipeline-sharing mechanism to reduce MEC resource usage. A Nash bargaining is proposed to incentivize cooperative computing provision between the MEC and the cloud, and a generalized benders decomposition (GBD)-based approach is utilized to optimize the social welfare. The results show that the proposed DICE-IoT framework can achieve a win-win-win solution to the IoT device, the MEC, and the cloud stratum. ? 2014 IEEE.Deep learning; Pipelines; Cooperative computing; EDGE architectures; Generalized benders decompositions; Internet of Things (IOT); Learning technology; Memory resources; Nash bargaining; Sharing mechanism; Internet of things[SDGs]SDG3[SDGs]SDG8[SDGs]SDG11journal article10.1109/JIOT.2020.29970912-s2.0-85092187332