Collaborative Vehicular Edge Computing Towards Greener ITS

In order to achieve a greener intelligent transport system (ITS), an efficient collaboration between vehicles is required to manage computation task processing with low latency. In this paper, we propose a collaborative edge computing scheme for vehicular Internet-of-things towards a greener ITS. The proposed scheme uses some vehicles as edge nodes, which are responsible for finding task processor nodes on behalf of a task requester node by considering the end-to-end task response time. The proposed scheme employs a two-stage approach where the first stage enables an efficient networking and computing architecture by forming vehicle clusters based on the edge architecture, and the second stage optimizes offloading tasks based on the architecture. We use realistic computer simulations to compare the proposed scheme with existing baselines, and show its superiority in terms of task offloading performance.

[1]  Yusheng Ji,et al.  Vehicular Multi-Access Edge Computing With Licensed Sub-6 GHz, IEEE 802.11p and mmWave , 2018, IEEE Access.

[2]  Qimei Cui,et al.  MDP-Based Task Offloading for Vehicular Edge Computing Under Certain and Uncertain Transition Probabilities , 2020, IEEE Transactions on Vehicular Technology.

[3]  Jianshan Zhou,et al.  A Game-Based Computation Offloading Method in Vehicular Multiaccess Edge Computing Networks , 2020, IEEE Internet of Things Journal.

[4]  Zhou Su,et al.  An Edge Caching Scheme to Distribute Content in Vehicular Networks , 2018, IEEE Transactions on Vehicular Technology.

[5]  Xuemin Shen,et al.  Hierarchical Soft Slicing to Meet Multi-Dimensional QoS Demand in Cache-Enabled Vehicular Networks , 2019, IEEE Transactions on Wireless Communications.

[6]  Kok-Lim Alvin Yau,et al.  Edge Computing in 5G: A Review , 2019, IEEE Access.

[7]  Bin Hu,et al.  Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System , 2020, IEEE Transactions on Intelligent Transportation Systems.

[8]  Yusheng Ji,et al.  An Intelligent Broadcast Protocol for VANETs Based on Transfer Learning , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[9]  Rose Qingyang Hu,et al.  Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning , 2018, IEEE Transactions on Vehicular Technology.

[10]  Ning Zhang,et al.  Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network , 2019, IEEE Internet of Things Journal.

[11]  Zhenyu Zhou,et al.  Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach , 2019, IEEE Transactions on Vehicular Technology.

[12]  Yusheng Ji,et al.  Cluster-Based Content Distribution Integrating LTE and IEEE 802.11p with Fuzzy Logic and Q-Learning , 2018, IEEE Computational Intelligence Magazine.

[13]  Xinlei Chen,et al.  A Survey of Opportunistic Offloading , 2018, IEEE Communications Surveys & Tutorials.

[14]  Yan Zhang,et al.  Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks , 2020, IEEE Internet of Things Journal.

[15]  Qinglin Zhao,et al.  Dependency-Aware Task Scheduling in Vehicular Edge Computing , 2020, IEEE Internet of Things Journal.

[16]  Mehdi Bennis,et al.  Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning , 2018, IEEE Internet of Things Journal.

[17]  Xuemin Shen,et al.  Edge Computing in Autonomous Vehicular Networks , 2019 .

[18]  Mohsen Guizani,et al.  Reliable Task Offloading for Vehicular Fog Computing Under Information Asymmetry and Information Uncertainty , 2019, IEEE Transactions on Vehicular Technology.

[19]  Yusheng Ji,et al.  Integrating Licensed and Unlicensed Spectrum in the Internet of Vehicles with Mobile Edge Computing , 2019, IEEE Network.

[20]  Yusheng Ji,et al.  Mobile Edge Computing for the Internet of Vehicles: Offloading Framework and Job Scheduling , 2019, IEEE Vehicular Technology Magazine.

[21]  Jun Zheng,et al.  Modeling and Analysis of the Uplink Local Delay in MEC-Based VANETs , 2020, IEEE Transactions on Vehicular Technology.

[22]  Lei Liu,et al.  Delay-Aware Grid-Based Geographic Routing in Urban VANETs: A Backbone Approach , 2019, IEEE/ACM Transactions on Networking.

[23]  Yusheng Ji,et al.  How to Utilize Interflow Network Coding in VANETs: A Backbone-Based Approach , 2016, IEEE Transactions on Intelligent Transportation Systems.

[24]  Yusheng Ji,et al.  AVE: Autonomous Vehicular Edge Computing Framework with ACO-Based Scheduling , 2017, IEEE Transactions on Vehicular Technology.

[25]  Yusheng Ji,et al.  Spatial Intelligence toward Trustworthy Vehicular IoT , 2018, IEEE Communications Magazine.

[26]  Lionel Nkenyereye,et al.  Software Defined Network-Based Multi-Access Edge Framework for Vehicular Networks , 2020, IEEE Access.