Abstract
The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computing resources, has attracted interest in recent years. The MEC-based task offloading can achieve low latency and low operational cost under the tasks delay constraints. However, most existing research generally focuses on how to divide and migrate these tasks to the other devices. This research ignores delay constraints and offloading node selection for different tasks. In this article, we design the MEC-enabled IoV architecture, in which all vehicles and MEC servers act as offloading nodes. Mobile offloading nodes (i.e., vehicles) and fixed offloading nodes (i.e., MEC servers) provide low latency offloading services cooperatively through roadside units. Then we propose the task offloading scheme that considers task classification and offloading nodes selection (TO-TCONS). Our goal is to minimize the total execution time of tasks. In TO-TCONS Scheme, we divide the task offloading into the same region offloading mode and cross-region offloading mode, which is based on the delay constraints of tasks and the travel time of the target vehicle. Moreover, we propose the mobile offloading nodes selection strategy to select offloading nodes for each task, which evaluates offloading candidates for each task based on computing resources and transmission rates. Simulation results demonstrate that TO-TCONS Scheme is indeed capable of reducing total latency of tasks execution under the delay constraints in MEC-enabled IoV.
- [1] . 2018. Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems 87 (
Oct. 2018), 278–289.Google ScholarDigital Library - [2] . 2018. Mobile edge computing: A survey. IEEE Internet of Things Journal 5, 1 (2018), 450–465.Google ScholarCross Ref
- [3] . 2017. Network service chaining in fog and cloud computing for the 5G environment: Data management and security challenges. IEEE Communications Magazine 55, 11 (2017), 114–122.Google ScholarCross Ref
- [4] . 2018. Task offloading for mobile edge computing in software defined ultra-dense network. IEEE Journal on Selected Areas in Communications 36, 3 (2018), 587–597.Google ScholarCross Ref
- [5] . 2018. Cognitive Internet of Vehicles. Computer Communications 120 (
May 2018), 58–70. Google ScholarDigital Library - [6] . 2014. Decentralized computation offloading game for mobile cloud computing. IEEE Transactions on Parallel & Distributed Systems 26, 4 (2014), 974–983.Google ScholarDigital Library
- [7] . 2016. Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking 24, 5 (2016), 2795–2808. Google ScholarDigital Library
- [8] . 2019. Artificial intelligence empowered edge computing and caching for Internet of Vehicles. IEEE Wireless Communications 26, 3 (2019), 12–18. Google ScholarDigital Library
- [9] . 2017. Internet of Vehicles (IoV) for traffic management. In Proceedings of the 2017 International Conference on Computer, Communication, and Signal Processing
(ICCCSP’17) . IEEE, Los Alamitos, CA, 1–17.Google ScholarCross Ref - [10] . 2018. Efficient computation offloading for multi-access edge computing in 5G HetNets. In Proceedings of the 2018 IEEE International Conference on Communications
(ICC’18) . IEEE, Los Alamitos, CA, 1–7.Google ScholarCross Ref - [11] . 2013. Downlink traffic scheduling in green vehicular roadside infrastructure. IEEE Transactions on Vehicular Technology 62, 3 (2013), 1289–1302.Google ScholarCross Ref
- [12] . 2015. Enhancements of V2X communication in support of cooperative autonomous driving. IEEE Communications Magazine 53, 12 (2015), 64–70.Google ScholarDigital Library
- [13] . 2020. Reliable computation offloading for edge computing-enabled software-defined IoV (IoT-J). IEEE Internet of Things Journal 7 (Aug. 2020), 1–14.Google Scholar
- [14] . 2018. Wireless powered cooperation-assisted mobile edge computing. IEEE Transactions on Wireless Communications 17, 4 (2018), 2375–2388.Google ScholarCross Ref
- [15] . 2020. Energy-efficient offloading decision-making for mobile edge computing in vehicular networks. EURASIP Journal on Wireless Communications and Networking 2020, 1 (2020), Article 35, 16 pages.Google ScholarDigital Library
- [16] . 2017. Internet of Vehicles: Motivation, layered architecture, network model, challenges, and future aspects. IEEE Access 4 (2017), 5356–5373.Google Scholar
- [17] . 2015. Energy-efficient scheduling in green vehicular infrastructure with multiple roadside units. IEEE Transactions on Vehicular Technology 64, 5 (2015), 1942–1957.Google ScholarCross Ref
- [18] . 2021. Intelligent dynamic service pricing strategy for multi-user vehicle-aided MEC networks. Future Generation Computer Systems 114 (2021), 15–22.Google ScholarCross Ref
- [19] . 2018. Selective offloading in mobile edge computing for the Green Internet of Things. IEEE Network 32, 1 (2018), 54–60.Google ScholarCross Ref
- [20] . 2013. When mobile terminals meet the cloud: Computation offloading as the bridge. IEEE Network 27, 5 (2013), 28–33.Google ScholarCross Ref
- [21] . 2015. Managing mobile cloud computing considering objective and subjective perspectives. Computer Networks 93 (
Dec. 2015), 531–542. Google ScholarDigital Library - [22] . 2018. Survey on existing authentication issues for cellular-assisted V2X communication. Vehicular Communications 12 (
April 2018), 50–65.Google ScholarCross Ref - [23] . 2019. Mobile edge computing-enabled Internet of Vehicles: Toward energy-efficient scheduling. IEEE Network 33, 99 (2019), 198–205.Google ScholarDigital Library
- [24] . 2020. Intelligent edge computing in Internet of Vehicles: A joint computation offloading and caching solution. IEEE Transactions on Intelligent Transportation Systems PP, 99 (2020), 1–14. Google ScholarDigital Library
- [25] . 2015. Performance analysis of connectivity probability and connectivity-aware MAC protocol design for platoon-Based VANETs. IEEE Transactions on Vehicular Technology 64, 12 (2015), 5596–5509.Google ScholarCross Ref
- [26] . 2018. Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing. IEEE Transactions on Mobile Computing 18, 2 (2018), 319–333. Google ScholarDigital Library
- [27] . 2020. Multi-user task offloading to heterogeneous processors with communication delay and budget constraints. IEEE Transactions on Cloud Computing 27 (
Aug. 2020), 1–18.Google ScholarCross Ref - [28] . 2010. A standard task graph set for fair evaluation of multiprocessor scheduling algorithms. Journal of Scheduling 5, 5 (2010), 379–394.Google ScholarCross Ref
- [29] . 2018. Internet of Vehicles: Sensing-aided transportation information collection and diffusion. IEEE Transactions on Vehicular Technology 67, 5 (2018), 3813–3825.Google ScholarCross Ref
- [30] . 2018. Vehicular sensing networks in a smart city: Principles, technologies and applications. IEEE Wireless Communications 25, 1 (2018), 122–132. Google ScholarDigital Library
- [31] . 2018. Edge server placement in mobile edge computing. Journal of Parallel and Distributed Computing 127 (
May 2018), 160–168.Google ScholarCross Ref - [32] . 2014. An overview of Internet of Vehicles. China Communications 11, 10 (2014), 1–15.Google ScholarCross Ref
- [33] . 2019. Deployment and dimensioning of fog computing-based Internet of Vehicle infrastructure for autonomous driving. IEEE Internet of Things Journal 6, 1 (2019), 149–160.Google ScholarCross Ref
- [34] . 2020. Task offloading in vehicular edge computing networks: A load-balancing solution. IEEE Transactions on Vehicular Technology 69, 2 (2020), 2092–2104.Google ScholarCross Ref
- [35] . 2017. Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading. IEEE Vehicular Technology Magazine 12, 2 (2017), 36–44.Google ScholarCross Ref
Index Terms
- Task Offloading with Task Classification and Offloading Nodes Selection for MEC-Enabled IoV
Recommendations
Vehicle-Road Cooperative Task Offloading with Task Migration in MEC-Enabled IoV
Wireless Algorithms, Systems, and ApplicationsAbstractMobile edge computing (MEC) is considered as a key technology for addressing computation-intensive and delay-critical applications in the Internet of vehicles (IoV). In MEC-enabled IoV, vehicles lighten their computing load by offloading tasks to ...
Modelling Task Offloading Mobile Edge Computing
ICCDE '22: Proceedings of the 2022 8th International Conference on Computing and Data EngineeringWith the rapid growth of mobile devices (such as smart phones and IoT devices) and the upcoming 5G era, it has been considered that edge computing will play a significant role, which together with the Cloud server forms the Mobile Edge Computing (MEC) ...
Task offloading in fog computing: A survey of algorithms and optimization techniques
AbstractThe exponential growth in Internet of Things (IoT) devices and the limitations of cloud computing in terms of latency and quality of service for time-sensitive applications have led to the unfolding of the efficient middleware ...
Comments