K
Ke Zhang
Researcher at University of Electronic Science and Technology of China
Publications - 97
Citations - 6392
Ke Zhang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Edge computing & Mobile edge computing. The author has an hindex of 27, co-authored 92 publications receiving 3395 citations. Previous affiliations of Ke Zhang include University of Pittsburgh & Florida State University College of Arts and Sciences.
Papers
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Journal ArticleDOI
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
Ke Zhang,Yuming Mao,Supeng Leng,Quanxin Zhao,Longjiang Li,Xin Peng,Li Pan,Sabita Maharjan,Yan Zhang +8 more
TL;DR: An optimization problem is formulated to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration, and an EECO scheme is designed, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints.
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Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading
TL;DR: A cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks is proposed, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions, which greatly reduces the cost of computation and improves task transmission efficiency.
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Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks
TL;DR: The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.
Journal ArticleDOI
Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles
TL;DR: A new architecture based on federated learning to relieve transmission load and address privacy concerns of providers is proposed and the reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification.
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Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics
TL;DR: This work adopts a deep Q-learning approach for designing optimal offloading schemes and proposes an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure and evaluates the proposed schemes based on real traffic data.