In this chapter, the challenges in smart city surveillance are presented. Huge amount of urban data collected in different formats as well as the requirement of real-time information fusion and decision-making are necessitating fresh solutions for smart cities. Cloud computing cannot meet the strict requirements in delay-sensitive surveillance application in mission critical tasks. As the complement of cloud, the fog computing enables data storage and processing much closer to end users and is identified as a more appropriate computing paradigm for urban smart surveillance
applications. A detailed discussion about the difference between fog and cloud is presented as well as the potential applications of fog computing.
A fog-based smart traffic monitoring architecture is discussed as a case study, which shows that fog computing is promising in modern urban surveillance applications. The experimental comparisons between the fog and cloud are con-ducted. With fog paradigm, the tracking result will be sent back to end users in real time with almost ignorable delays. However, using cloud paradigm, the data transmission between the network edge to cloud centers brings significant latencies which could be more severe from further cloud centers. Therefore, the experiments show that for latency-sensitive smart city surveillance applications, the fog com-puting becomes more suitable in terms of latency.
At the end, the research challenges of the fog computing in smart city surveil-lance are discussed. A lot of academic and industrial efforts in the fog computing have been conducted, but there is still a list of open issues to be solved to make the fog computing paradigm more effective and practical for smart city surveillance applications. It is critical for researchers to consider about the boundary between the fog and cloud and how to solve the issues from fog like dynamicity and heterogeneity. We hope this chapter will inspire more active research, discussion, and collaboration in this risingfield.
References
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 203–226
2. UN (2014) World urbanization prospects 2014. https://esa.un.org/unpd/wup/publications/
files/wup2014-highlights.Pdf. Accessed 13 Feb 2017
3. Research Center for Economics and Business (2014) 50% rise in grid lock costs by 2030.
https://www.cebr.com/reports/the-future-economic-and-environmental-costs-of-gridlock/.
Accessed 13 Feb 2017
4. WHO (2015) Global status report on road safety 2015.http://www.who.int/violence_injury_
prevention/road_safety_status/2015/en/. Accessed 13 Feb 2017
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):1–18
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 105–112 7. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv
(CSUR) 41(3):15
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– 580
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-602 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):1501–1517
11. Laxhammar R, Falkman G (2014) Online learning and sequential anomaly detection in trajectories. IEEE Trans Pattern Anal Mach Intell 36(6):1158–1173
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 1–13
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 216–222
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):102–110 15. Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE
Internet Things J 3(5):637–646
16. Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of thefirst edition of the MCC workshop on mobile cloud computing, 2012. ACM, pp 13–16
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 37–42
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 1–6 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):121 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 1–4
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):54–62
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 162–167
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 13:1910
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 32–39
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 1–6
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):16–24
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):27–32
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 938–945
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 2017
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.
http://www.its.dot.gov/press/2016/datasets. Accessed 15 Feb 2017
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 1830–1837
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 2017
Big Energy Data Management for Smart Grids —Issues, Challenges and Recent Developments
Vidyasagar Potdar, Anulipt Chandan, Saima Batool and Naimesh Patel
Abstract Urban areas suffer from tremendous pressure to cope with increasing population in a city. A smart city is a technological solution that integrates engi-neering and information systems to assist in managing these scarce resources.
A smart city comprises several intelligent services such as smart grids, smart education, smart transportation, smart buildings, smart waste management and so on. Among all these, smart grids are the nucleus of all the facilities because these provide sustainable electrical supply for other smart services to operate seamlessly.
Smart grids integrate information and communication technologies (ICT) into tra-ditional energy grids, thereby capturing massive amounts of data from several devices like smart meters, sensors, and other electrical infrastructures. The data collected in smart grids are heterogeneous and require data analytic techniques to extract meaningful information to make informed decisions. We term this enormous amount of data as big energy data. This book chapter discusses progress in thefield of big energy data by enlisting different studies that cover several data management aspects such as data collection, data preprocessing, data integration, data storage, data analytics, data visualisation and decision-making. We also discuss various challenges in data management and report recent progress in thisfield. Finally, we present open research areas in big data management especially in relation to smart grids.
Keywords Smart city
Smart gridBig energy dataData management Smart meterEnergy data management Data lifecycle Data preprocessing Data collectionData integration Data storageData analyticsData visualisation
Decision-makingV. Potdar S. Batool (&)
Curtin Business School, Curtin University, Perth, Australia e-mail: [email protected]
A. Chandan
National Institute of Technology, Agartala, India N. Patel
Safeworld Systems Pvt Ltd, Ahmedabad, India
© Springer International Publishing AG, part of Springer Nature 2018 Z. Mahmood (ed.),Smart Cities, Computer Communications and Networks, https://doi.org/10.1007/978-3-319-76669-0_8
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