Abstract
Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.
- [1] . 2010. Hybrid techniques for large-scale IP traffic matrix estimation. In 2010 IEEE International Conference on Communications. IEEE, 1–6.Google Scholar
Cross Ref
- [2] . 2020. Low-cost security for next-generation IoT networks. ACM Trans. Internet Technol. 20, 3, Article
30 (Sept. 2020), 31 pages. Google ScholarDigital Library
- [3] . 2008. VHF general urban path loss model for short range ground-to-ground communications. IEEE Transactions on Antennas and Propagation 56, 10 (2008), 3302–3310.Google Scholar
Cross Ref
- [4] . 2018. Internet of vehicles: Architecture, protocols, and security. IEEE Internet of Things Journal 5, 5 (2018), 3701–3709.Google Scholar
Cross Ref
- [5] . 2020. Fog in the clouds: UAVs to provide edge computing to IoT devices. ACM Trans. Internet Technol. 20, 3, Article
26 (Aug. 2020), 26 pages. Google ScholarDigital Library
- [6] . 2019. TIFIM: A two-stage iterative framework for influence maximization in social networks. Appl. Math. Comput. 354 (2019), 338-352. Google Scholar
Digital Library
- [7] . 2018. ATME: Accurate traffic matrix estimation in both public and private datacenter networks. IEEE Transactions on Cloud Computing 6, 1 (2018), 60–73.Google Scholar
Cross Ref
- [8] . 2020. Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks. IEEE Transactions on Industrial Informatics 16, 8 (2020), 4986–4996.Google Scholar
Cross Ref
- [9] . 2020. Named data networking in vehicular ad hoc networks: State-of-the-art and challenges. IEEE Communications Surveys & Tutorials 22, 1 (2020), 320–351.Google Scholar
Digital Library
- [10] . 2018. Network throughput optimization for random access narrowband cognitive radio internet of things (NB-CR-IoT). IEEE Internet of Things Journal 5, 3 (
June 2018), 1436–1448. Google ScholarCross Ref
- [11] . 2020. Signal estimation in underlay cognitive networks for industrial Internet of Things. IEEE Transactions on Industrial Informatics 16, 8 (2020), 5478–5488.Google Scholar
Cross Ref
- [12] . 2020. Power system intra-interval operational security under false data injection attacks. IEEE Transactions on Industrial Informatics 16, 8 (2020), 4997–5008.Google Scholar
Cross Ref
- [13] . 2020. IoT architecture for urban data-centric services and applications. ACM Trans. Internet Technol. 20, 3, Article
29 (July 2020), 30 pages. Google ScholarDigital Library
- [14] . 2017. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 55, 2 (2017), 645–657. Google Scholar
Cross Ref
- [15] . 2020. Data-driven intrusion detection for intelligent Internet of Vehicles: A deep convolutional neural network-based method. IEEE Transactions on Network Science and Engineering (2020), 1–1.Google Scholar
- [16] . 2020. Intelligent edge computing in Internet of Vehicles: A joint computation offloading and caching solution. IEEE Transactions on Intelligent Transportation Systems (2020), 1–14.Google Scholar
- [17] . 2020. Joint computing and caching in 5G-Envisioned internet of vehicles: A deep reinforcement learning-based traffic control system. IEEE Transactions on Intelligent Transportation Systems (2020), 1–12.Google Scholar
Digital Library
- [18] . 2011. Intelligent IP traffic matrix estimation by neural network and genetic algorithm. In 2011 IEEE 7th International Symposium on Intelligent Signal Processing. IEEE, 1–6.Google Scholar
- [19] . 2019. Fog computing for the internet of things: A survey. ACM Trans. Internet Technol. 19, 2, Article
18 (April 2019), 41 pages. Google ScholarDigital Library
- [20] . 2018. Spatio-Temporal wireless traffic prediction with recurrent neural network. IEEE Wireless Communications Letters 7, 4 (
Aug . 2018), 554–557. Google ScholarCross Ref
- [21] . 2012. Spatio-temporal compressive sensing and Internet traffic matrices (extended version). IEEE Transactions on Networking 20, 3 (2012), 662–676.Google Scholar
Digital Library
- [22] . 2018. Machine-learning-based prediction for resource (Re)allocation in optical data center networks. IEEE/OSA Journal of Optical Communications and Networking 10, 10 (
Oct . 2018), D12–D28. Google ScholarCross Ref
- [23] . 2005. Information theoretic approach to traffic adaptive WDM networks. IEEE/ACM Transactions on Networking 13, 4 (2005), 881–894.Google Scholar
Digital Library
- [24] . 2005. Traffic matrices: Balancing measurements, inference and modeling. In Proceedings of SIGMETRICS 2005. IEEE, 362–373.Google Scholar
Digital Library
- [25] . 2020. A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Transactions on Knowledge and Data Engineering (2020), 1–1.Google Scholar
Cross Ref
- [26] . 2019. Stop-and-Wait: Discover aggregation effect based on private car trajectory data. IEEE Transactions on Intelligent Transportation Systems 20, 10 (
Oct. 2019), 3623–3633. Google ScholarCross Ref
- [27] . 2017. Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach. In IEEE Conference on Computer Communications (IEEE INFOCOM 2017). IEEE, 1–9.Google Scholar
Cross Ref
- [28] . 2020. Imitation learning enabled task scheduling for online vehicular edge computing. IEEE Transactions on Mobile Computing (2020), 1–1.Google Scholar
Digital Library
- [29] . 2019. BENBI: Scalable and dynamic access control on the northbound interface of SDN-based VANET. IEEE Transactions on Vehicular Technology 68, 1 (
Jan . 2019), 822–831. Google ScholarCross Ref
- [30] . 2020. LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT. IEEE Transactions on Industrial Informatics 16, 8 (2020), 5244–5253.Google Scholar
Cross Ref
- [31] . 2019. Short-term traffic prediction for edge computing-enhanced autonomous and connected cars. IEEE Transactions on Vehicular Technology 68, 4 (
April 2019), 3140–3153. Google ScholarCross Ref
- [32] . 2018. Citywide cellular traffic prediction based on densely connected convolutional neural networks. IEEE Communications Letters 22, 8 (
Aug . 2018), 1656–1659. Google ScholarCross Ref
- [33] . 2019. Joint spectrum sensing and packet error rate optimization in cognitive IoT. IEEE Internet of Things Journal 6, 5 (
Oct . 2019), 7816–7827. Google ScholarCross Ref
- [34] . 2017. LSTM network: A deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems 11, 2 (2017), 68–75.Google Scholar
Cross Ref
Index Terms
Deep Learning-Based Network Traffic Prediction for Secure Backbone Networks in Internet of Vehicles
Recommendations
Data Dissemination for Industry 4.0 Applications in Internet of Vehicles Based on Short-term Traffic Prediction
As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the ...
VANET Traffic Prediction Using LSTM with Deep Neural Network Learning
Internet of Things, Smart Spaces, and Next Generation Networks and SystemsAbstractVehicular ad hoc networks (VANETs) are a promising technology that enables the communication between vehicles on roads. It becomes an emerging topic that integrates the capabilities of new generation wireless networks for vehicles. Network traffic ...
A dynamic spatial–temporal deep learning framework for traffic speed prediction on large-scale road networks
AbstractTraffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling advanced transportation management and services. In this paper, we address the problem of multi-step traffic speed prediction, including ...
Highlights- Novel traffic deep learning prediction model on large-scale road networks.
- ...






Comments