M
Mianxiong Dong
Researcher at Muroran Institute of Technology
Publications - 403
Citations - 12069
Mianxiong Dong is an academic researcher from Muroran Institute of Technology. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 49, co-authored 324 publications receiving 8652 citations. Previous affiliations of Mianxiong Dong include National Institute of Information and Communications Technology & Shanghai Jiao Tong University.
Papers
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Journal ArticleDOI
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
He Li,Kaoru Ota,Mianxiong Dong +2 more
TL;DR: This article first introduces deep learning for IoTs into the edge computing environment, and designs a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing.
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Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
TL;DR: This paper proposes a deep learning based classification model, which can find the possible defective products in the manufacture inspection system with higher accuracy, and adapts the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency.
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ActiveTrust: Secure and Trustable Routing in Wireless Sensor Networks
TL;DR: The most important innovation of ActiveTrust is that it avoids black holes through the active creation of a number of detection routes to quickly detect and obtain nodal trust and thus improve the data route security.
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Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks
TL;DR: This paper studies the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN).
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Energy-Efficient Matching for Resource Allocation in D2D Enabled Cellular Networks
TL;DR: This paper employs the Gale–Shapley algorithm to match D2D pairs with cellular UEs, which is proved to be stable and weak Pareto optimal, and extends the algorithm to address scalability issues in large-scale networks by developing tie-breaking and preference-deletion-based matching rules.