Abstract
The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.
- Afshin Abadi, Tooraj Rajabioun, and Petros A. Ioannou. 2014. Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transport. Syst. 16, 2 (2014), 653--662.Google Scholar
- Anas Amjad, Fazle Rabby, Shaima Sadia, Mohammad Patwary, and Elhadj Benkhelifa. 2017. Cognitive edge computing based resource allocation framework for Internet of Things. In Proceedings of the 2nd International Conference on Fog and Mobile Edge Computing (FMEC’17). IEEE, 194--200.Google Scholar
Cross Ref
- Kashif Bilal and Aiman Erbad. 2017. Edge computing for interactive media and video streaming. In Proceedings of the 2nd International Conference on Fog and Mobile Edge Computing (FMEC’17). IEEE, 68--73.Google Scholar
Cross Ref
- Daeho Lee, Doug Young, Suh Bokyun Jo, Md. Jalil Piran. 2019. Efficient computation offloading in mobile cloud computing for video streaming over 5G. Comput. Mater. Contin. 61, 2 (2019), 439--463. DOI:https://doi.org/10.32604/cmc.2019.08194Google Scholar
Cross Ref
- Weihong Chen, Jiyao An, Renfa Li, Li Fu, Guoqi Xie, Md Zakirul Alam Bhuiyan, and Keqin Li. 2018. A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features. Fut. Gen. Comput. Syst. 89 (2018), 78--88.Google Scholar
Cross Ref
- Juan Contreras-Castillo, Sherali Zeadally, and Juan Antonio Guerrero-Ibañez. 2017. Internet of vehicles: Architecture, protocols, and security. IEEE Internet Things J. 5, 5 (2017), 3701--3709.Google Scholar
Cross Ref
- Bowen Du, Hao Peng, Senzhang Wang, Md Zakirul Alam Bhuiyan, Lihong Wang, Qiran Gong, Lin Liu, and Jing Li. 2019. Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans. Intell. Transport. Syst. 21, 3 (2019).Google Scholar
- Christoph Feichtenhofer, Axel Pinz, and Richard P. Wildes. 2017. Temporal residual networks for dynamic scene recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4728--4737.Google Scholar
- Juan Antonio Guerrero-Ibanez, Sherali Zeadally, and Juan Contreras-Castillo. 2015. Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wirel. Commun. 22, 6 (2015), 122--128.Google Scholar
Digital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.Google Scholar
Cross Ref
- Chih-Lin Hu, Pin-Chun Chiu, and Sheng-Chih Huang. 2017. CarView: A live media streaming platform among neighbor vehicles on roads. In Proceedings of the International Conference on Applied System Innovation (ICASI’17). IEEE, 610--613.Google Scholar
Cross Ref
- Sang Hyo Lee, Ha Young Kim, Hyun Kyu Shin, Yong Han Ahn. 2019. Digital vision based concrete compressive strength evaluating model using deep convolutional neural network. Comput. Mater. Contin. 61, 3 (2019), 911--928. DOI:https://doi.org/10.32604/cmc.2019.08269Google Scholar
Cross Ref
- Chuanmin Jia, Shiqi Wang, Xinfeng Zhang, Shanshe Wang, and Siwei Ma. 2017. Spatial-temporal residue network based in-loop filter for video coding. In Proceedings of the IEEE Conference on Visual Communications and Image Processing (VCIP’17). IEEE, 1--4.Google Scholar
Cross Ref
- Fanhui Kong, Jian Li, Bin Jiang, and Houbing Song. 2019. Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network. Fut. Gen. Comput. Syst. 93 (2019), 460--472.Google Scholar
Cross Ref
- He Li, Kaoru Ota, and Mianxiong Dong. 2018. Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Netw. 32, 1 (2018), 96--101.Google Scholar
Cross Ref
- Kai Lin, Fuzhen Xia, and Giancarlo Fortino. 2019. Data-driven clustering for multimedia communication in Internet of vehicles. Fut. Gen. Comput. Syst. 94 (2019), 610--619.Google Scholar
Cross Ref
- Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2014. Traffic flow prediction with big data: A deep learning approach. IEEE Trans. Intell. Transport. Syst. 16, 2 (2014), 865--873.Google Scholar
- Yanik Ngoko and Christophe Cérin. 2017. An edge computing platform for the detection of acoustic events. In Proceedings of the IEEE International Conference on Edge Computing (EDGE’17). IEEE, 240--243.Google Scholar
Cross Ref
- Yang Ning, Yang Huang, Jinyang Li, Qi Liu, Disheng Yang, Wei Zheng, and Hengchang Liu. 2018. ST-DRN: Deep residual networks for spatio-temporal metro stations crowd flows forecast. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’18). IEEE, 1--8.Google Scholar
Cross Ref
- Zhaolong Ning, Xiping Hu, Zhikui Chen, MengChu Zhou, Bin Hu, Jun Cheng, and Mohammad S. Obaidat. 2017. A cooperative quality-aware service access system for social Internet of vehicles. IEEE Internet Things J. 5, 4 (2017), 2506--2517.Google Scholar
Cross Ref
- Hao Peng, Hongfei Wang, Bowen Du, Md Zakirul Alam Bhuiyan, Hongyuan Ma, Jianwei Liu, Lihong Wang, Zeyu Yang, Linfeng Du, Senzhang Wang, et al. 2020. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Inf. Sci. 521 (2020), 277--290.Google Scholar
Digital Library
- Lianyong Qi, Qiang He, Feifei Chen, Wanchun Dou, Shaohua Wan, Xuyun Zhang, and Xiaolong Xu. 2019. Finding all you need: Web APIs recommendation in web of things through keywords search. IEEE Trans. Comput. Social Syst. 6, 5 (2019).Google Scholar
Cross Ref
- Pei Ren, Xiuquan Qiao, Junliang Chen, and Schahram Dustdar. 2018. Mobile edge computing—A booster for the practical provisioning approach of web-based augmented reality. In Proceedings of the IEEE/ACM Symposium on Edge Computing (SEC’18). IEEE, 349--350.Google Scholar
Cross Ref
- Mahadev Satyanarayanan. 2017. The emergence of edge computing. Computer 50, 1 (2017), 30--39.Google Scholar
Digital Library
- Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. 2016. Edge computing: Vision and challenges. IEEE Internet Things J. 3, 5 (2016), 637--646.Google Scholar
Cross Ref
- Shaohua Wan, Xiang Li, Yuan Xue, Wenmin Lin, and Xiaolong Xu. 2019. Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks. J. Supercomput. 76, 4 (2019), 1--30.Google Scholar
- Shangguang Wang, Yali Zhao, Jinlinag Xu, Jie Yuan, and Ching-Hsien Hsu. 2019. Edge server placement in mobile edge computing. J. Parallel Distrib. Comput. 127 (2019), 160--168.Google Scholar
Digital Library
- Takayuki Warabino, Kenji Saito, Keizo Sugiyama, Hideyuki Shinonaga, and Tomohiro Nishida. 2005. Adaptive media switching for future vehicle-to-vehicle communication. In Proceedings of the IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications, Vol. 2. IEEE, 795--800.Google Scholar
Cross Ref
- Yifei Wei, Zhaoying Wang, Da Guo, and F. Richard Yu. 2019. Deep Q-learning based computation offloading strategy for mobile edge computing. Comput. Mater. Contin. 59, 1 (2019), 89--104.Google Scholar
Cross Ref
- Zhongyang Xiao, Zhaobin Mo, Kun Jiang, and Diange Yang. 2018. Multimedia fusion at semantic level in vehicle cooperactive perception. In Proceedings of the IEEE International Conference on Multimedia & Expo Workshops (ICMEW’18). IEEE, 1--6.Google Scholar
Cross Ref
- Min Xing, Jianping He, and Lin Cai. 2015. Maximum-utility scheduling for multimedia transmission in drive-thru Internet. IEEE Trans. Vehic. Technol. 65, 4 (2015), 2649--2658.Google Scholar
Cross Ref
- Xiaolong Xu, Yuan Xue, Lianyong Qi, Yuan Yuan, Xuyun Zhang, Tariq Umer, and Shaohua Wan. 2019. An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Fut. Gen. Comput. Syst. 96 (2019), 89--100.Google Scholar
Cross Ref
- Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the 31st AAAI Conference on Artificial Intelligence.Google Scholar
Index Terms
TripRes: Traffic Flow Prediction Driven Resource Reservation for Multimedia IoV with Edge Computing
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