Edge Computing-Enabled Resource Provisioning for Video Surveillance in Internet of Vehicles

  • Xiaolong Xu
  • Qi Wu
  • Chengxun He
  • Shaohua Wan
  • Lianyong QiEmail author
  • Hao Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


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.


Internet of Vehicles Edge computing Resource allocation Video surveillance 



This research is supported by the National Science Foundation of China under grant no. 61702277 and 61872219.


  1. 1.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefGoogle Scholar
  2. 2.
    Xu, Z., et al.: An IoT-oriented offloading method with privacy preservation for cloudlet-enabled wireless metropolitan area networks. Sensors 18(9), 3030 (2018)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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) CrossRefGoogle Scholar
  5. 5.
    Long, C., Cao, Y.: Edge computing framework for cooperative video processing in multimedia IoT systems. IEEE Trans. Multimedia 20(5), 1126–1139 (2018)CrossRefGoogle Scholar
  6. 6.
    Lopez, P., et al.: Edge-centric computing: vision and challenges. ACMSIGCOMM Comput. Commun. Rev. 45(5), 37–42 (2015)CrossRefGoogle Scholar
  7. 7.
    Eriksson, E., Dán, G.: Predictive distributed visual analysis for video in wireless sensor networks. IEEE Trans. Mob. Comput. 15(7), 1743–1756 (2016)CrossRefGoogle Scholar
  8. 8.
    Zhang, J., et al.: Hybrid computation offloading for smart home automation in mobile cloud computing. Pers. Ubiquitous Comput. 22(1), 121–134 (2018)CrossRefGoogle Scholar
  9. 9.
    Zhang, J., Qi, L., Yuan, Y., Xu, X., Dou, W.: A workflow scheduling method for cloudlet management in mobile cloud. In: 2018 IEEE SmartWorld.
  10. 10.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)CrossRefGoogle Scholar
  11. 11.
    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).
  12. 12.
    Qi, L., et al.: Finding all you need: web APIs recommendation in web of things through keywords search. IEEE Trans. Comput. Soc. Syst. (2019). Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Chen, J., Li, K.: Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Ind. Inform.
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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). Scholar
  17. 17.
    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). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xiaolong Xu
    • 1
    • 2
  • Qi Wu
    • 1
  • Chengxun He
    • 1
  • Shaohua Wan
    • 3
  • Lianyong Qi
    • 4
    Email author
  • Hao Wang
    • 5
  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)Nanjing University of Information Science and TechnologyNanjingChina
  3. 3.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  4. 4.School of Information Science and EngineeringQufu Normal UniversityRizhaoChina
  5. 5.Department of Computer ScienceNorwegian University of Science and TechnologyGjøvikNorway

Personalised recommendations