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Information-Centric Mobile Edge Computing for Connected Vehicle Environments: Challenges and Research Directions

Authors:

Mayutan Arumaithurai,

Ioannis Psaras, and

Dirk KutscherAuthors Info & Claims

MECOMM '17: Proceedings of the Workshop on Mobile Edge Communications

August 2017

Pages 7 - 12

https://doi.org/10.1145/3098208.3098210

Published: 09 August 2017 Publication History

Abstract

Connected vehicle systems form the basis for future features of functions and applications within the automotive domain. In order to allow resource intensive services, cloud offloading and especially Mobile Edge Computing is a promising approach. In this paper, we present a detailed futuristic vehicular scenario -- Electronic Horizon -- and list the challenges. We argue that the resulting challenges are representative of many of the envisioned use-cases of Mobile Edge Computing. We then present how Information-Centric Networking in combination with Mobile Edge Computing has the potential to support such a futuristic scenario. Finally, we present research directions that could enhance the solution space.

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Cited By

Kishani MBecvar ZNikooroo MAsadi H(2024)Joint Optimization of Communication and Storage Latencies for Vehicular Edge ComputingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.333670425:6(5435-5449)Online publication date: Jun-2024
https://doi.org/10.1109/TITS.2023.3336704
Walia GKumar MGill S(2024)AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future PerspectivesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333801526:1(619-669)Online publication date: Sep-2025
https://doi.org/10.1109/COMST.2023.3338015
Guerrero JMartín AParejo ALarios DMolina FLeón C(2023)A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart GridsSensors10.3390/s2308384523:8(3845)Online publication date: 9-Apr-2023
https://doi.org/10.3390/s23083845
Show More Cited By

Index Terms

Information-Centric Mobile Edge Computing for Connected Vehicle Environments: Challenges and Research Directions
1. Networks
  1. Network architectures
    1. Network design principles
      1. Naming and addressing
  2. Network services
    1. Cloud computing
    2. In-network processing

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cover image ACM Conferences

MECOMM '17: Proceedings of the Workshop on Mobile Edge Communications

August 2017

67 pages

ISBN:9781450350525

DOI:10.1145/3098208

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

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Published: 09 August 2017

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Refereed limited

Funding Sources

EU H2020/NICT ICN2020 Project
and the EPSRC INSP Early Career Fellowship

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SIGCOMM '17

Sponsor:

SIGCOMM

SIGCOMM '17: ACM SIGCOMM 2017 Conference

August 21, 2017

CA, Los Angeles, USA

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Cited By

Kishani MBecvar ZNikooroo MAsadi H(2024)Joint Optimization of Communication and Storage Latencies for Vehicular Edge ComputingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.333670425:6(5435-5449)Online publication date: Jun-2024
https://doi.org/10.1109/TITS.2023.3336704
Walia GKumar MGill S(2024)AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future PerspectivesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333801526:1(619-669)Online publication date: Sep-2025
https://doi.org/10.1109/COMST.2023.3338015
Guerrero JMartín AParejo ALarios DMolina FLeón C(2023)A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart GridsSensors10.3390/s2308384523:8(3845)Online publication date: 9-Apr-2023
https://doi.org/10.3390/s23083845
Joshi AD PGill A(2023)Distributed Analytics in Smart Grids with Edge Computing and Computational Intelligence2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC)10.1109/PEEIC59336.2023.10450996(1289-1294)Online publication date: 19-Dec-2023
https://doi.org/10.1109/PEEIC59336.2023.10450996
Jerang RNayak SMahato GKumar Chakraborty S(2023)Privacy-Preserving of Edge Intelligence using Homomorphic Encryption2023 3rd International Conference on Intelligent Technologies (CONIT)10.1109/CONIT59222.2023.10205745(1-6)Online publication date: 23-Jun-2023
https://doi.org/10.1109/CONIT59222.2023.10205745
Alaya BSellami L(2023)Toward the Design of an Efficient and Secure System Based on the Software-Defined Network Paradigm for Vehicular NetworksIEEE Access10.1109/ACCESS.2023.326480811(43333-43348)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3264808
Arthurs PGillam LKrause PWang NHalder KMouzakitis A(2022)A Taxonomy and Survey of Edge Cloud Computing for Intelligent Transportation Systems and Connected VehiclesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.308439623:7(6206-6221)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/TITS.2021.3084396
Gur GKalla Ade Alwis CPham QNgo KLiyanage MPorambage P(2022)Integration of ICN and MEC in 5G and Beyond Networks: Mutual Benefits, Use Cases, Challenges, Standardization, and Future ResearchIEEE Open Journal of the Communications Society10.1109/OJCOMS.2022.31951253(1382-1412)Online publication date: 2022
https://doi.org/10.1109/OJCOMS.2022.3195125
Sun HZhang BZhang XYu YSha KShi W(2022)FlexEdge: Dynamic Task Scheduling for a UAV-Based On-Demand Mobile Edge ServerIEEE Internet of Things Journal10.1109/JIOT.2022.31524479:17(15983-16005)Online publication date: 1-Sep-2022
https://doi.org/10.1109/JIOT.2022.3152447
Han TOguchi TLyu S(2022)QueueLearner: A Knowledge-Combined Reinforcement Learning to Understand Queuing Evolution in Isolated Traffic Signal Control2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC55140.2022.9922584(1175-1182)Online publication date: 8-Oct-2022
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