Abstract
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 traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.
- Irshad Ahmed Abbasi, Adnan Shahid Khan, and Shahzad Ali. Dec., 2018. A reliable path selection and packet forwarding routing protocol for vehicular ad hoc networks. EURASIP J. Wireless Commun. Netw. 2018, 1 (Dec. 2018), 236.Google ScholarCross Ref
- Irshad A. Abbasi, Babar Nazir, Aftab Abbasi, Sardar M. Bilal, and Sajjad A. Madani. 2014. A traffic flow-oriented routing protocol for VANETs. EURASIP J. Wirel. Commun. Netw. 2014, 1 (2014), 1–14.Google ScholarCross Ref
- Yusor Rafid Bahar Al-Mayouf, Nor Fadzilah Abdullah, Omar Adil Mahdi, Suleman Khan, Mahamod Ismail, Mohsen Guizani, and Syed Hassan Ahmed. 2018. Real-time intersection-based segment aware routing algorithm for urban vehicular networks. IEEE Trans. Intell. Transp. Syst. 19, 7 (2018), 2125–2141.Google ScholarCross Ref
- Khac-Hoai Nam Bui and Jason J. Jung. 2019. ACO-based Dynamic Decision Making for Connected Vehicles in IoT System. IEEE Trans. Ind. Inform. 15, 10 (2019), 5648–5655.Google ScholarCross Ref
- C. Chen, Z. Liu, S. Wan, J. Luan, and Q. Pei. 2020. Traffic flow prediction based on deep learning in internet of vehicles. IEEE Trans. Intell. Transp. Syst. 22, 6 (2020), 3776–3789.Google ScholarCross Ref
- Chen Chen, Cong Wang, Tie Qiu, Mohammed Atiquzzaman, and Dapeng Oliver Wu. 2020. Caching in vehicular named data networking: Architecture, schemes and future directions. IEEE Commun. Surv. Tutor. 22, 4 (2020), 2378–2407.Google ScholarCross Ref
- Okyoung Choi, Seokhyun Kim, Jaeseong Jeong, Hyang-Won Lee, and Song Chong. 2016. Delay-optimal data forwarding in vehicular sensor networks. IEEE Trans. Veh. Technol. 65, 8 (2016), 6389–6402.Google ScholarCross Ref
- Yueyue Dai, Du Xu, Sabita Maharjan, Qiao Guan hua, and Yan Zhang. 2019, accepted. Artificial intelligence empowered edge computing and caching for internet of vehicles. IEEE Wireless Commun. 26, 3 (2019), 12–18. Google ScholarDigital Library
- Tasneem S. J. Darwish, Kamalrulnizam Abu Bakar, and Khalid Haseeb. Jan., 2018. Reliable intersection-based traffic aware routing protocol for urban areas vehicular ad hoc networks. IEEE Intell. Transp. Syst. Mag. 10, 1 (Jan. 2018), 60–73.Google ScholarCross Ref
- Jie Feng, F. Richard Yu, Qingqi Pei, Xiaoli Chu, Jianbo Du, and Li Zhu. 2020. Cooperative computation offloading and resource allocation for blockchain-enabled mobile edge computing: A deep reinforcement learning approach. IEEE Internet of Things J. 7, 7 (2020), 6214–6228.Google ScholarCross Ref
- Jérôme Härri, Fethi Filali, Christian Bonnet, and Marco Fiore. 2006. VanetMobiSim: Generating realistic mobility patterns for VANETs. In Proceedings of ACM SIMUTOOLS. 96–97. Google ScholarDigital Library
- Jianping He, Lin Cai, Jianping Pan, and Peng Cheng. 2016. Delay analysis and routing for two-dimensional VANETs using carry-and-forward mechanism. IEEE Trans. Mobile Comput. 16, 7 (2016), 1830–1841.Google ScholarDigital Library
- N. Li, J. F. Martinez Ortega, V. H. Diaz, and J. A. S. Fernandez. 2018. Probability prediction-based reliable and efficient opportunistic routing algorithm for VANETs. IEEE/ACM Trans. Netw. 26, 4 (2018), 1933–1947. Google ScholarDigital Library
- Lei Liu, Chen Chen, Qingqi Pei, Sabita Maharjan, and Yan Zhang. 2020. Vehicular edge computing and networking: A survey. Mobile Netw. Appl. 2020, 1 (2020), 1–24.Google Scholar
- Lei Liu, Chen Chen, Tie Qiu, Mengyuan Zhang, Siyu Li, and Bin Zhou. 2018. A data dissemination scheme based on clustering and probabilistic broadcasting in VANETs. Vehicular Commun. 13 (2018), 78–88.Google ScholarCross Ref
- Andre B. Reis, Susana Sargento, Filipe Neves, and Ozan K. Tonguz. 2014. Deploying roadside units in sparse vehicular networks: What really works and what does not. IEEE Trans. Veh. Technol. 63, 6 (2014), 2794–2806.Google ScholarCross Ref
- Mohammad Rezaee and Mohammad Hossein Yaghmaee Moghaddam. 2019. SDN-based quality of service networking for wide area measurement system. IEEE Trans. Ind. Info. 16, 5 (2019), 3018–3028.Google ScholarCross Ref
- Andrey Silva, Niaz Reza, and Aurenice Oliveira. 2019. Improvement and Performance evaluation of GPSR-based routing techniques for vehicular ad hoc networks. IEEE Access 7 (2019), 21722–21733.Google ScholarCross Ref
- Y. Song, Y. Fu, F. R. Yu, and L. Zhou. 2020. Blockchain-enabled internet of vehicles with cooperative positioning: A deep neural network approach. IEEE Internet of Things J. 7, 4 (2020), 3485–3498. DOI:https://doi.org/10.1109/JIOT.2020.2972337Google ScholarCross Ref
- Yujie Tang, Nan Cheng, Wen Wu, Miao Wang, Yanpeng Dai, and Xuemin Shen. 2019. Delay-minimization routing for heterogeneous VANETs with machine learning-based mobility prediction. IEEE Trans. Veh. Technol. 68, 4 (2019), 3967–3979.Google ScholarCross Ref
- Shaohua Wan, Xiaolong Xu, Tian Wang, and Zonghua Gu. 2020. An intelligent video analysis method for abnormal event detection in intelligent transportation systems. IEEE Trans. Intell. Transport. Syst. 22, 7 (2021), 4487–4495.Google ScholarCross Ref
- Junhua Wang, Kai Liu, Ke Xiao, Xiumin Wang, Qingwen Han, and Victor C. S. Lee. 2019. Delay-constrained routing via heterogeneous vehicular communications in software defined busnet. IEEE Trans. Veh. Technol. 68, 6 (2019), 5957–5970.Google ScholarCross Ref
- Tinging Yang, Hailong Feng, Chengming Yang, Ying Wang, Jie Dong, and Minghua Xia. 2018. Multivessel computation offloading in maritime mobile edge computing network. IEEE Internet Things J. 6, 3 (2018), 4063–4073.Google ScholarCross Ref
- T. Yang, Z. Jiang, R. Sun, N. Cheng, and H. Feng. 2020. Maritime search and rescue based on group mobile computing for UAVs and USVs. IEEE Trans. Industr. Info. 16, 12 (2020), 7700–7708.Google ScholarCross Ref
Index Terms
- Data Dissemination for Industry 4.0 Applications in Internet of Vehicles Based on Short-term Traffic Prediction
Recommendations
Deep Learning-Based Network Traffic Prediction for Secure Backbone Networks in Internet of Vehicles
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 ...
A Novel Traffic Prediction System based on Floating Car Data and Machine Learning
NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & SecurityIntelligent Transportation Systems have become a necessity with the increasing number of cars running, especially in the urban roads. This paper presents a novel system capable to forecast the traffic in the urban road networks. This study aims to ...
A hybrid prediction approach for road tunnel traffic based on spatial-temporary data fusion
In this paper, we propose a hybrid prediction model based on spatial-temporal data fusion to predict future tunnel traffic. Our approach consists of a local predictor, a global predictor, an outlier predictor, and a prediction integrator. Firstly, the ...
Comments