Abstract
As a novel technology, Internet of Vehicles (IoV) is employed to gather real-time traffic information for drivers from sensors and video surveillance devices with image processing, circumstances analysis and events recognition. In spite of multiple advantages of IoV, preprocessing the huge data may demand abundant computation resources for video surveillance devices. Migrating tasks to remote servers for performing is efficient to solve this problem, but it needs high network bandwidth, which causes traffic congestion and delay. Edge computing has capability to enhance processing performance, which complements video surveillance device and addresses numerous shortcomings. Nevertheless, edge computing for video surveillance remains a challenge to achieve low-latency and load balance through limited amount of edge servers. To handle this challenge, an Edge computing-enabled Resource Provisioning Method (ERPM) for Video Surveillance in IoV is proposed in this paper. Technically, SPEA2 (improving the Strength Pare to Evolutionary Algorithm) is picked to solve the multi-objective optimization problem aiming at minimizing the time consumption and optimizing load balance. Finally, experimental simulation for Evolution algorithm demonstrate the appropriation and efficiency of ERPM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Xu, Z., et al.: An IoT-oriented offloading method with privacy preservation for cloudlet-enabled wireless metropolitan area networks. Sensors 18(9), 3030 (2018)
Kumar, N., Rodrigues, J.J., Chilamkurti, N.: Bayesian coalition game as-a-service for content distribution in internet of vehicles. IEEE Internet Things J. 1(6), 544–555 (2014)
Puvvadi, U.L., Di Benedetto, K., Patil, A., Kang, K.D., Park, Y.: Cost-effective security support in real-time video surveillance. IEEE Trans. Ind. Inform. 11(6), 1457–1465 (2015)
Long, C., Cao, Y.: Edge computing framework for cooperative video processing in multimedia IoT systems. IEEE Trans. Multimedia 20(5), 1126–1139 (2018)
Lopez, P., et al.: Edge-centric computing: vision and challenges. ACMSIGCOMM Comput. Commun. Rev. 45(5), 37–42 (2015)
Eriksson, E., Dán, G.: Predictive distributed visual analysis for video in wireless sensor networks. IEEE Trans. Mob. Comput. 15(7), 1743–1756 (2016)
Zhang, J., et al.: Hybrid computation offloading for smart home automation in mobile cloud computing. Pers. Ubiquitous Comput. 22(1), 121–134 (2018)
Zhang, J., Qi, L., Yuan, Y., Xu, X., Dou, W.: A workflow scheduling method for cloudlet management in mobile cloud. In: 2018 IEEE SmartWorld. https://doi.org/10.1109/SmartWorld.2018.00167
Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)
Qi, L., Chen, Y., Yuan, Y., Fu, S., Zhang, X., Xu, X.: A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web J. (2019). https://doi.org/10.1007/s11280-019-00684-y
Qi, L., et al.: Finding all you need: web APIs recommendation in web of things through keywords search. IEEE Trans. Comput. Soc. Syst. (2019). https://doi.org/10.1109/TCSS.2019.2906925
Wu, P.-H., Huang, C.-W., Hwang, J.-N.: Video-quality-driven resource allocation for real-time surveillance video uplinking over OFDMA-based wireless networks. IEEE Trans. Veh. Technol. 64(7), 3233–3246 (2015)
Chen, J., Li, K.: Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Ind. Inform. https://doi.org/10.1109/TII.2019.2909473
Xu, X., et al.: An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks. J. Netw. Comput. Appl. 133, 75–85 (2019)
Al-Nadwi, M.M.K., Refat, N., Zaman, N., Rahman, M.A., Bhuiyan, M.Z.A., Razali, R.B.: Cloud enabled e-glossary system: a smart campus perspective. In: Wang, G., Chen, J., Yang, L.T. (eds.) SpaCCS 2018. LNCS, vol. 11342, pp. 251–260. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05345-1_21
Yang, J., Wang, H., Wang, Z., Long, J., Du, B.: BDCP: a framework for big data copyright protection based on digital watermarking. In: Wang, G., Chen, J., Yang, L.T. (eds.) SpaCCS 2018. LNCS, vol. 11342, pp. 351–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05345-1_30
Acknowledgment
This research is supported by the National Science Foundation of China under grant no. 61702277 and 61872219.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xu, X., Wu, Q., He, C., Wan, S., Qi, L., Wang, H. (2019). Edge Computing-Enabled Resource Provisioning for Video Surveillance in Internet of Vehicles. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-1301-5_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1300-8
Online ISBN: 978-981-15-1301-5
eBook Packages: Computer ScienceComputer Science (R0)