Delay-Sensitive Task Offloading in the 802.11p-Based Vehicular Fog Computing Systems

Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results.

[1]  Mianxiong Dong,et al.  FCSS: Fog-Computing-based Content-Aware Filtering for Security Services in Information-Centric Social Networks , 2019, IEEE Transactions on Emerging Topics in Computing.

[2]  Shahid Mumtaz,et al.  Computation Resource Allocation and Task Assignment Optimization in Vehicular Fog Computing: A Contract-Matching Approach , 2019, IEEE Transactions on Vehicular Technology.

[3]  Chih-Yu Wang,et al.  Parking Reservation Auction for Parked Vehicle Assistance in Vehicular Fog Computing , 2019, IEEE Transactions on Vehicular Technology.

[4]  Pingyi Fan,et al.  Velocity-Adaptive V2I Fair-Access Scheme Based on IEEE 802.11 DCF for Platooning Vehicles , 2018, Sensors.

[5]  A. Girotra,et al.  Performance Analysis of the IEEE 802 . 11 Distributed Coordination Function , 2005 .

[6]  D. Malone,et al.  Modeling the 802.11 Distributed Coordination Function in Nonsaturated Heterogeneous Conditions , 2007, IEEE/ACM Transactions on Networking.

[7]  Xiaolin Chang,et al.  Reliable and Secure Vehicular Fog Service Provision , 2019, IEEE Internet of Things Journal.

[8]  David Malone,et al.  Modeling the 802.11 distributed coordination function in non-saturated conditions , 2005, IEEE Communications Letters.

[9]  Qianbin Chen,et al.  Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing , 2017, IEEE Transactions on Wireless Communications.

[10]  Jun Zheng,et al.  Performance Modeling and Analysis of the IEEE 802.11p EDCA Mechanism for VANET , 2016, IEEE Transactions on Vehicular Technology.

[11]  Du Xu,et al.  Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks , 2019, IEEE Internet of Things Journal.

[12]  Antti Ylä-Jääski,et al.  Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing , 2019, IEEE Internet of Things Journal.

[13]  Keqiu Li,et al.  Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing , 2017, IEEE Wireless Communications Letters.

[14]  Wenchao Xu,et al.  Big Data Driven Vehicular Networks , 2018, IEEE Network.

[15]  Depeng Jin,et al.  Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures , 2016, IEEE Transactions on Vehicular Technology.

[16]  Mianxiong Dong,et al.  Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing , 2019, ACM Trans. Internet Techn..

[17]  Igor Bisio,et al.  Blind Detection: Advanced Techniques for WiFi-Based Drone Surveillance , 2019, IEEE Transactions on Vehicular Technology.

[18]  Song Guo,et al.  Traffic and Computation Co-Offloading With Reinforcement Learning in Fog Computing for Industrial Applications , 2019, IEEE Transactions on Industrial Informatics.

[19]  Jiannong Cao,et al.  Guest Editorial Emerging Computing Offloading for IoTs: Architectures, Technologies, and Applications , 2019, IEEE Internet Things J..

[20]  Jun Huang,et al.  Vehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities , 2019, IEEE Wireless Communications.

[21]  Der-Jiunn Deng,et al.  Resource Allocation in Vehicular Cloud Computing Systems With Heterogeneous Vehicles and Roadside Units , 2018, IEEE Internet of Things Journal.

[22]  Xiaowei Yang,et al.  Secrecy-Driven Resource Management for Vehicular Computation Offloading Networks , 2018, IEEE Network.

[23]  Xuemin Shen,et al.  An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems , 2015, IEEE Transactions on Industrial Electronics.

[24]  Zhe Wang,et al.  Application-Aware Offloading Policy Using SMDP in Vehicular Fog Computing Systems , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[25]  Qiong Wu,et al.  A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications , 2018, Sensors.

[26]  Lin Gui,et al.  Service-Oriented Dynamic Connection Management for Software-Defined Internet of Vehicles , 2017, IEEE Transactions on Intelligent Transportation Systems.

[27]  Mianxiong Dong,et al.  Foud: Integrating Fog and Cloud for 5G-Enabled V2G Networks , 2017, IEEE Network.

[28]  Lin Gui,et al.  Cooperative Task Scheduling for Computation Offloading in Vehicular Cloud , 2018, IEEE Transactions on Vehicular Technology.

[29]  Khaled Ben Letaief,et al.  Delay-optimal computation task scheduling for mobile-edge computing systems , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[30]  Xiaodong Lin,et al.  Efficient and Privacy-Preserving Carpooling Using Blockchain-Assisted Vehicular Fog Computing , 2019, IEEE Internet of Things Journal.

[31]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[32]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[33]  Victor C. M. Leung,et al.  Distributed Resource Allocation and Computation Offloading in Fog and Cloud Networks With Non-Orthogonal Multiple Access , 2018, IEEE Transactions on Vehicular Technology.

[34]  R. Bellman A Markovian Decision Process , 1957 .

[35]  Xu Chen,et al.  Decentralized Computation Offloading Game for Mobile Cloud Computing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[36]  Wenyu Zhang,et al.  Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management , 2017, IEEE Communications Magazine.

[37]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.