Smart Cities pp 153-176 | Cite as

Smart City Surveillance at the Network Edge in the Era of IoT: Opportunities and Challenges

  • Ning Chen
  • Yu ChenEmail author
Part of the Computer Communications and Networks book series (CCN)


Taking advantages of modern information and communication technologies (ICTs), smart cities aim at providing their residents better services as well as monitoring unexpected changes of city activity patterns. The globally rapid urbanization is proposing various inevitable issues, one of which is smart and efficient surveillance in urban areas. With ubiquitously deployed smart sensors, city mobility can be recorded all the time resulting in tons of urban data in every second. For smart city surveillance, identifying anomaly changes is always of high priority since changes in normal urban patterns may lead to remarkable events or even disasters. However, just like finding a needle in the sea, it is difficult for the surveillance operators to obtain meaningful information from the collected big urban data. Moreover, changes especially in emergent situations require quick decision-making with rather low latency tolerance to prevent a big loss. Therefore, all the issues are propelling researchers to seek new computing paradigms other than cloud computing which is powerful but suffers relatively high latency and bandwidth overconsumption. Connected environments like Internet of Things (IoTs) build a platform for connected smart devices to collaboratively share data and provide plentiful computing resources at the edge of network. Fog computing enables data processing and storage at the network edge which is promising to reduce the bandwidth consumption as well as making smart city surveillance more effective and efficient. This chapter provides a holistic vision about smart city surveillance and fog computing paradigm including the concepts, applications, challenges, and opportunities. A case study of urban traffic surveillance is presented to highlight the concepts through a real-world application example.


Smart city surveillance Internet of things (IoTs)  Fog computing Cloud computing Edge computing Urban surveillance Urbanization Governance 


  1. 1.
    Chen N, Chen Y, Ye X, Ling H, Song S, Huang C-T (2017) Smart city surveillance in fog computing. In: Advances in mobile cloud computing and big data in the 5G Era. Springer, Berlin, pp 203–226Google Scholar
  2. 2.
    UN (2014) World urbanization prospects 2014. Accessed 13 Feb 2017
  3. 3.
    Research Center for Economics and Business (2014) 50% rise in grid lock costs by 2030. Accessed 13 Feb 2017
  4. 4.
    WHO (2015) Global status report on road safety 2015. Accessed 13 Feb 2017
  5. 5.
    Yin C, Xiong Z, Chen H, Wang J, Cooper D, David B (2015) A literature survey on smart cities. Sci China Inf Sci 58(10):1–18CrossRefGoogle Scholar
  6. 6.
    Chen N, Chen Y, You Y, Ling H, Liang P, Zimmermann R (2016) Dynamic urban surveillance video stream processing using fog computing. In: 2016 IEEE second international conference on multimedia big data (BigMM), 2016. IEEE, pp 105–112Google Scholar
  7. 7.
    Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):15CrossRefGoogle Scholar
  8. 8.
    Tian B, Morris BT, Tang M, Liu Y, Yao Y, Gou C, Shen D, Tang S (2015) Hierarchical and networked vehicle surveillance in ITS: a survey. IEEE Trans Intell Transp Syst 16(2):557–580Google Scholar
  9. 9.
    Fu Z, Hu W, Tan T (2005) Similarity based vehicle trajectory clustering and anomaly detection. In: IEEE international conference on image processing, ICIP 2005. IEEE, pp II-602Google Scholar
  10. 10.
    Jeong H, Yoo Y, Yi KM, Choi JY (2014) Two-stage online inference model for traffic pattern analysis and anomaly detection. Mach Vis Appl 25(6):1501–1517CrossRefGoogle Scholar
  11. 11.
    Laxhammar R, Falkman G (2014) Online learning and sequential anomaly detection in trajectories. IEEE Trans Pattern Anal Mach Intell 36(6):1158–1173CrossRefGoogle Scholar
  12. 12.
    Wu R, Liu B, Chen Y, Blasch E, Ling H, Chen G (2015) A container-based elastic cloud architecture for pseudo real-time exploitation of wide area motion imagery (WAMI) stream. J Signal Process Syst 1–13Google Scholar
  13. 13.
    Chen Q, Qiu Q, Wu Q, Bishop M, Barnell M (2014) A confabulation model for abnormal vehicle events detection in wide-area traffic monitoring. In: 2014 IEEE international inter-disciplinary conference on cognitive methods in situation awareness and decision support (CogSIMA), 2014. IEEE, pp 216–222Google Scholar
  14. 14.
    Andersson M, Gustafsson F, St-Laurent L, Prevost D (2013) Recognition of anomalous motion patterns in urban surveillance. IEEE J Sel Topics Signal Process 7(1):102–110CrossRefGoogle Scholar
  15. 15.
    Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646CrossRefGoogle Scholar
  16. 16.
    Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing, 2012. ACM, pp 13–16Google Scholar
  17. 17.
    Yi S, Li C, Li QA (2015) Survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 workshop on mobile big data, 2015. ACM, pp 37–42Google Scholar
  18. 18.
    Okay FY, Ozdemir S (2016) A fog computing based smart grid model. In: 2016 international symposium on networks, computers and communications (ISNCC), 2016. IEEE, pp 1–6Google Scholar
  19. 19.
    Stantchev V, Barnawi A, Ghulam S, Schubert J, Tamm G (2015) Smart items, fog and cloud computing as enablers of servitization in healthcare. Sens Transducers 185(2):121Google Scholar
  20. 20.
    Yan Y, Su WA (2016) fog computing solution for advanced metering infrastructure. In: Transmission and distribution conference and exposition (T&D), IEEE/PES, 2016. IEEE, pp 1–4Google Scholar
  21. 21.
    Nikoloudakis Y, Panagiotakis S, Markakis E, Pallis E, Mastorakis G, Mavromoustakis CX, Dobre C (2016) A fog-based emergency system for smart enhanced living environments. IEEE Cloud Comput 3(6):54–62CrossRefGoogle Scholar
  22. 22.
    Kopetz H, Poledna S (2016) In-vehicle real-time fog computing. In: 2016 46th annual IEEE/IFIP international conference on dependable systems and networks workshop, 2016. IEEE, pp 162–167Google Scholar
  23. 23.
    Hu P, Ning H, Qiu T, Zhang Y, Luo X (2016) Fog computing-based face identification and resolution scheme in internet of things. IEEE Trans Ind Informatics 13:1910CrossRefGoogle Scholar
  24. 24.
    Skarlat O, Schulte S, Borkowski M, Leitner P (2016) Resource provisioning for IoT services in the fog. In: 2016 IEEE 9th international conference on service-oriented computing and applications (SOCA), 2016. IEEE, pp 32–39Google Scholar
  25. 25.
    Zhang H, Xiao Y, Bu S, Niyato D, Yu R, Han Z (2016) Fog computing in multi-tier data center networks: a hierarchical game approach. In: 2016 IEEE international conference on communications (ICC), 2016. IEEE, pp 1–6Google Scholar
  26. 26.
    Wen Z, Yang R, Garraghan P, Lin T, Xu J, Rovatsos M (2017) Fog orchestration for internet of things services. IEEE Internet Comput 21(2):16–24CrossRefGoogle Scholar
  27. 27.
    Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput Commun Rev 44(5):27–32CrossRefGoogle Scholar
  28. 28.
    Mohammad Y, Nishida T (2017) On comparing SSA-based change point discovery algorithms. In: 2011 IEEE/SICE international symposium on system integration (SII), 2017. IEEE, pp 938–945Google Scholar
  29. 29.
    Chen N, Yang Z, Chen Y, Polunchenko A (2017) Online anomalous vehicle detection at the edge using multidimensional SSA. In: The 3rd IEEE INFOCOM workshop on smart cities and urban computing (SmartCity 2017), 1 May 2017Google Scholar
  30. 30.
    Department of Transportation ITS Joint Program Office (2017) New data sets from the next generation simulation (NGSIM) program are now available in the research data exchange. Accessed 15 Feb 2017
  31. 31.
    Bao C, Wu Y, Ling H, Ji H (2012) Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR), 2012. IEEE, pp 1830–1837Google Scholar
  32. 32.
    Gebre-Amlak H, Lee S, Jabbari A, Chen Y, Choi B, Huang C, Song S (2017) MIST: mobility-inspired software-defined fog system. In: The 2017 international conference on consumer electronics (ICCE), cloud computing track, Las Vegas, NV, USA, 8–11 Jan 2017Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computing EngineeringBinghamton UniversityBinghamtonUSA

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