Abstract
With the prosperity of Industry 4.0, numerous emerging industries continue to gain popularity and their market scales are expanding ceaselessly. The Internet of Vehicles (IoV), one of the thriving intelligent industries, enjoys bright development prospects. However, at the same time, the reliability and availability of IoV applications are confronted with two major bottlenecks of time delay and energy consumption. To make matters worse, massive heterogeneous and multi-dimensional multimedia data generated on the IoV present a huge obstacle to effective data analysis. Fortunately, the advent of edge computing technology enables tasks to be offloaded to edge servers, which significantly reduces total overhead of IoV systems. Deep reinforcement learning (DRL), equipped with its excellent perception and decision-making capability, is undoubtedly a dominant technology to solve task offloading problems. In this article, we first employ an optimized Fuzzy C-means algorithm to cluster vehicles and other edge devices according to their respective service quality requirements. Then, we employ an election algorithm to assist in maintaining the stability of the IoV. Last, we propose a task-offloading algorithm based on the Deep Q Network (DQN) to acquire an optimal task offloading scheme. Massive simulation experiments demonstrate the superiority of our method in minimizing time delay and energy consumption.
- [1] . 2021. Deep-learning-enhanced multitarget detection for end-edge-cloud surveillance in smart IoT. IEEE Internet of Things Journal 8, 16 (2021), 12588–12596.
DOI: Google ScholarCross Ref
- [2] . 2018. The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics 204 (2018), 383–394.Google Scholar
Cross Ref
- [3] . 2021. Intelligent traffic network control in the era of Internet of Vehicles. IEEE Transactions on Vehicular Technology 70, 10 (2021), 9787–9802.
DOI: Google ScholarCross Ref
- [4] . 2016. Real-time load reduction in multimedia big data for mobile Internet. ACM Transactions on Multimedia Computing, Communications, and Applications 12, 5s, Article
76 (Oct 2016), 20 pages.DOI: Google ScholarDigital Library
- [5] . 2018. Cluster-oriented device-to-device multimedia communications: Joint power, bandwidth, and link selection optimization. IEEE Transactions on Vehicular Technology 67, 2 (2018), 1570–1581.
DOI: Google ScholarCross Ref
- [6] . 2021. Deep correlation mining based on hierarchical hybrid networks for heterogeneous big data recommendations. IEEE Transactions on Computational Social Systems 8, 1 (2021), 171–178.
DOI: Google ScholarCross Ref
- [7] . 2022. A correlation graph based approach for personalized and compatible web APIs recommendation in mobile APP development. IEEE Transactions on Knowledge and Data Engineering (2022), 1–1.
DOI: Google ScholarCross Ref
- [8] . 2022. An effective forest fire detection framework using heterogeneous wireless multimedia sensor networks. ACM Transactions on Multimedia Computing, Communications, and Applications 18, 2, Article
47 (Feb 2022), 21 pages.DOI: Google ScholarDigital Library
- [9] . 2021. Edge content caching with deep spatiotemporal residual network for IoV in smart city. ACM Transactions on Sensor Networks 17, 3, Article
29 (Jun 2021), 33 pages.DOI: Google ScholarDigital Library
- [10] . 2021. A survey on algorithms for intelligent computing and smart city applications. Big Data Mining and Analytics 4, 3 (2021), 155–172.
DOI: Google ScholarCross Ref
- [11] . 2022. Big data with cloud computing: Discussions and challenges. Big Data Mining and Analytics 5, 1 (2022), 32–40.Google Scholar
Cross Ref
- [12] . 2018. Stochastic analysis of delayed mobile offloading in heterogeneous networks. IEEE Transactions on Mobile Computing 17, 2 (2018), 461–474.
DOI: Google ScholarDigital Library
- [13] . 2016. Edge computing: Vision and challenges. IEEE Internet of Things Journal 3, 5 (2016), 637–646.
DOI: Google ScholarCross Ref
- [14] . 2021. TripRes: Traffic flow prediction driven resource reservation for multimedia IoV with edge computing. ACM Transactions on Multimedia Computing, Communications, and Applications 17, 2, Article
41 (apr 2021), 21 pages.DOI: Google ScholarDigital Library
- [15] . 2021. Utility aware offloading for mobile-edge computing. Tsinghua Science and Technology 26, 2 (2021), 239–250.
DOI: Google ScholarCross Ref
- [16] . 2022. DisCOV: Distributed COVID-19 detection on x-ray images with edge-cloud collaboration. IEEE Transactions on Services Computing (2022), 1–1.
DOI: Google ScholarCross Ref
- [17] . 2022. Task offloading strategy with emergency handling and blockchain security in SDN-empowered and fog-assisted healthcare IoT. Tsinghua Science and Technology 27, 4 (2022), 760–776.
DOI: Google ScholarCross Ref
- [18] . 2022. IDAM: Iteratively trained deep in-loop filter with adaptive model selection. ACM Transactions on Multimedia Computing, Communications, and Applications (
Mar 2022).DOI: Google ScholarDigital Library
- [19] . 2019. Robust load frequency control for smart power grid over open distributed communication network with uncertainty. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). 4341–4346.
DOI: Google ScholarDigital Library
- [20] . 2021. Hierarchical adversarial attacks against graph neural network based IoT network intrusion detection system. IEEE Internet of Things Journal (2021), 1–1.
DOI: Google ScholarCross Ref
- [21] . 1999. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. Vol. 51. John Wiley & Sons. 769–770.Google Scholar
- [22] . 2022. Game theory for distributed IoV task offloading with fuzzy neural network in edge computing. IEEE Transactions on Fuzzy Systems (2022), 1–1.
DOI: Google ScholarCross Ref
- [23] . 2010. Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics 2, 3 (2010), 317–332.Google Scholar
Cross Ref
- [24] . 2021. An intelligent trust cloud management method for secure clustering in 5G enabled Internet of Medical Things. IEEE Transactions on Industrial Informatics (2021), 1–1.
DOI: Google ScholarCross Ref
- [25] . 2018. Deep reinforcement learning based computation offloading and resource allocation for MEC. In 2018 IEEE Wireless Communications and Networking Conference (WCNC’18). 1–6.
DOI: Google ScholarDigital Library
- [26] . 2019. Deep learning-based multimedia analytics: A review. ACM Transactions on Multimedia Computing, Communications and Applications 15, 1s, Article
2 (Jan 2019), 26 pages.DOI: Google ScholarDigital Library
- [27] . 2021. Internet of Things attack detection using hybrid deep learning model. Computer Communications 176 (2021), 146–154.Google Scholar
Digital Library
- [28] . 2022. Classification of Alzheimer’s disease using ensemble of deep neural networks trained through transfer learning. IEEE Journal of Biomedical and Health Informatics 26, 4 (2022), 1453–1463.
DOI: Google ScholarCross Ref
- [29] . 2021. Adaptive compression for online computer vision: An edge reinforcement learning approach. ACM Transactions on Multimedia Computing, Communications, and Applications 17, 4, Article
118 (Nov 2021), 23 pages.DOI: Google ScholarDigital Library
- [30] . 2021. Fast adaptive task offloading in edge computing based on meta reinforcement learning. IEEE Transactions on Parallel and Distributed Systems 32, 1 (2021), 242–253.
DOI: Google ScholarDigital Library
- [31] . 1984. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 10, 2–3 (1984), 191–203.Google Scholar
Cross Ref
- [32] . 2013. Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.Google Scholar
- [33] . 2016. Deep reinforcement learning with double q-learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30.
DOI: Google ScholarCross Ref
- [34] . 2021. Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial IoT. IEEE Transactions on Industrial Informatics (2021), 1–1.
DOI: Google ScholarCross Ref
- [35] . 2020. Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning. IEEE Transactions on Vehicular Technology 69, 12 (2020), 16067–16081.
DOI: Google ScholarCross Ref
- [36] . 2020. Deep reinforcement learning for task offloading in mobile edge computing systems. IEEE Transactions on Mobile Computing (2020), 1–1.
DOI: Google ScholarCross Ref
- [37] . 2021. Computationally efficient energy management for hybrid electric vehicles using model predictive control and vehicle-to-vehicle communication. IEEE Transactions on Vehicular Technology 70, 1 (2021), 237–250.
DOI: Google ScholarCross Ref
- [38] . 2021. Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning. IEEE Transactions on Cognitive Communications and Networking 7, 3 (2021), 881–892.
DOI: Google ScholarCross Ref
- [39] . 2021. Deadline-aware task offloading with partially-observable deep reinforcement learning for multi-access edge computing. IEEE Transactions on Network Science and Engineering (2021), 1–1.
DOI: Google ScholarCross Ref
- [40] . 2021. Cooperative data sensing and computation offloading in UAV-assisted crowdsensing with multi-agent deep reinforcement learning. IEEE Transactions on Network Science and Engineering (2021), 1–1.
DOI: Google ScholarCross Ref
- [41] . 2021. DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing. IEEE Transactions on Network and Service Management 18, 3 (2021), 3448–3459.
DOI: Google ScholarCross Ref
- [42] . 2020. Deep reinforcement learning-based adaptive computation offloading for MEC in heterogeneous vehicular networks. IEEE Transactions on Vehicular Technology 69, 7 (2020), 7916–7929.
DOI: Google ScholarCross Ref
- [43] . 2019. Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology 68, 5 (2019), 4192–4203.
DOI: Google ScholarCross Ref
- [44] . 2021. An LSH-based offloading method for IoMT services in integrated cloud-edge environment. ACM Transactions on Multimedia Computing, Communications, and Applications 16, 3s, Article
94 (Jan 2021), 19 pages.DOI: Google ScholarDigital Library
- [45] . 2021. Energy efficient smart routing based on link correlation mining for wireless edge computing in IoT. IEEE Internet of Things Journal (2021), 1–1.
DOI: Google ScholarCross Ref
- [46] . 2021. Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network. IEEE Transactions on Intelligent Transportation Systems (2021), 1–13.
DOI: Google ScholarDigital Library
- [47] . 2018. Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning. IEEE Internet of Things Journal 6, 3 (2018), 4005–4018.Google Scholar
Cross Ref
- [48] . 2021. Ensemble deep learning: A review. arXiv preprint arXiv:2104.02395.Google Scholar
Index Terms
Deep Q Network–Driven Task Offloading for Efficient Multimedia Data Analysis in Edge Computing–Assisted IoV
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