Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading

By Ke Zhang, Yuming Mao, Supeng Leng, Yejun He, Yan Zhang. IEEE Vehicular Technology Magazine, 2017

561 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dbd4efb3ca106116ef917c266210f52bb93fdb15

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In Automotive Engineering

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Citations in the corpus

Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution (2020, background): “For instance, the authors in [8] proposed a network architecture with predictive offloading to maximum the services provider’s benefits in vehicular networks.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “Offloading such computation-intensive tasks to the proximate MEC servers will enable powerful machine learning methods to assist key tasks of intelligent vehicles [46], [47].”
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks (2019, background): “Vehicular edge computing (VEC) is a promising new paradigm that has received much attention lately, as it can extend the computation capability to vehicular network edge [5], [6].”
Vehicular Edge Computing and Networking: A Survey (2019, methodology): “ed network system can improve the QoE of users while satisfying the QoS of users [57] Utility Propose a task allocation optimization problem assisted by static and mobile fog nodes Mobility Awareness [58] Cost The tasks can be adaptively ofﬂoaded to edge servers through two methods:directly ofﬂoadin”
Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities (2019, unknown): “In [9], a VFC-enabled offloading framework is investigated to reduce latency and delivery cost.”
Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution (2021, background): “Nowadays, edge intelligence, integrating Artiﬁcial Intelligence (AI) with edge computing, enables appropriate edge service deployment and ﬂexible resource scheduling in IoVs [2].”
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “Owing to its proximity to the end-users and geographically distributed deployment, MEC can address the limitations of cloud computing to support the applications demanding delay-sensitive, computation-intensive, and high QoS requirements [154], [155].”
Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues (2018, background): “In the vehicular networks, Vehicles can connect with infrastructure (V2I) and other vehicular terminals (V2V) by the Road Side Units (RSUs), the RSUs need to provide the real-time vehicular services for a large number of mobility vehicles through distributing computation tasks [37].”
Computation Resource Allocation and Task Assignment Optimization in Vehicular Fog Computing: A Contract-Matching Approach (2019, background): “, matching theory [1], [27], coalitional game [28], Stackelberg game [29], and multi-player noncooperative game [30].”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “To liberate mobile devices from the limited computation capabilities and energy supply, both industry and academia take computation offloading as a promising solution to efficiently harmo-nize the computing resources in the end-edge-cloud orchestrated computing paradigms [168].”
Permissioned Blockchain and Edge Computing Empowered Privacy-Preserving Smart Grid Networks (2019, background): “[28] explored the reduction of the complexity by providing predictive offloading.”