survey

A Survey on Big Multimedia Data Processing and Management in Smart Cities

Authors:

Muhammad Usman,

Mian Ahmad Jan,

Jinjun ChenAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 3

Article No.: 54, Pages 1 - 29

https://doi.org/10.1145/3323334

Published: 18 June 2019 Publication History

Abstract

Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time.

References

[1]

Ian F. Akyildiz, Tommaso Melodia, and Kaushik R. Chowdhury. 2007. A survey on wireless multimedia sensor networks. Comput. Netw. 51, 4 (2007), 921--960.

Digital Library

[2]

Ian F. Akyildiz, Tommaso Melodia, and Kaushik R. Chowdhury. 2008. Wireless multimedia sensor networks: Applications and testbeds. Proc. IEEE 96, 10 (2008), 1588--1605.

[3]

Fadi Al-Turjman, Ayman Radwan, Shahid Mumtaz, and Jonathan Rodriguez. 2017. Mobile traffic modelling for wireless multimedia sensor networks in IoT. Comput. Commun. 112, 1 (Nov. 2017), 109--115.

Digital Library

[4]

Islam T. Almalkawi, Manel Guerrero Zapata, Jamal N. Al-Karaki, and Julian Morillo-Pozo. 2010. Wireless multimedia sensor networks: Current trends and future directions. Sensors 10, 7 (2010), 6662--6717.

[5]

Sheeraz A. Alvi, Bilal Afzal, Ghalib A. Shah, Luigi Atzori, and Waqar Mahmood. 2015. Internet of multimedia things: Vision and challenges. Ad Hoc Netw. 33 (2015), 87--111.

Digital Library

[6]

Amazon. 2015. Amazon API. Retrieved from https://aws.amazon.com/documentation/machine-learning/.

[7]

Fabrice Axisa, Pierre Michael Schmitt, Claudine Gehin, Georges Delhomme, Eric McAdams, and André Dittmar. 2005. Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention. IEEE Trans. Inform. Technol. Biomed. 9, 3 (2005), 325--336.

Digital Library

[8]

C. Badii, P. Bellini, D. Cenni, A. Difino, P. Nesi, and M. Paolucci. 2017. Analysis and assessment of a knowledge based smart city architecture providing service APIs. Future Gen. Comput. Syst. 75 (2017), 14--29.

[9]

Asa Ben-Hur, David Horn, Hava T. Siegelmann, and Vladimir Vapnik. 2001. Support vector clustering. J. Machine Learn. Res. 2, Dec. (2001), 125--137.

[10]

Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Machine Intell. 35, 8 (2013), 1798--1828.

Digital Library

[11]

Luca Bertinetto, Jack Valmadre, Joao F. Henriques, Andrea Vedaldi, and Philip H. S. Torr. 2016. Fully-convolutional siamese networks for object tracking. In Proceedings of the European Conference on Computer Vision. Springer, 850--865.

[12]

Simon Elias Bibri and John Krogstie. 2017. Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society 31 (May 2017), 183--212.

[13]

BigML. 2011. BigML API. Retrieved from https://bigml.com/api.

[14]

Igor Bisio, Chiara Garibotto, Fabio Lavagetto, and Andrea Sciarrone. 2018. Performance evaluation of application layer joint coding solutions for video transmissions between mobile devices over the Internet of things. Computer Communications 118 (March 2018), 50--59.

Digital Library

[15]

Leo Breiman, Jerome Friedman, Charles J. Stone, and Richard A. Olshen. 1984. Classification and Regression Trees. CRC Press.

[16]

Manisha Chahal, Sandeep Harit, Krishn K. Mishra, Arun Kumar Sangaiah, and Zhigao Zheng. 2017. A survey on software-defined networking in vehicular ad hoc networks: Challenges, applications and use cases. Sustainable Cities and Society 35 (Nov. 2017), 830--840.

[17]

Adam Coates, Andrew Ng, and Honglak Lee. 2011. An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 215--223.

[18]

Adam Coates and Andrew Y. Ng. 2012. Learning feature representations with k-means. In Neural Networks: Tricks of the Trade. Springer, 561--580.

[19]

DELL. 2016. Saensuk Pilots Smart City in Collaboration with DELL and Intel. Retrieved from https://www.dell.com/learn/us/en/uscorp1/press-releases/2016-07-26-saensuk-smart-city-pilots-first-healthcare-iot-project-with-dell-intel.

[20]

Der-Jiunn Deng. 2011. PSSB: Priority enforced slow-start backoff algorithm for multimedia transmission in wireless ad hoc networks. J. Netw. Comput. Appl. 34, 5 (2011), 1468--1473.

Digital Library

[21]

Manuel Díaz, Cristian Martín, and Bartolomé Rubio. 2016. State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J. Netw. Comput. Appl. 67 (2016), 99--117.

Digital Library

[22]

Soufiene Djahel, Ronan Doolan, Gabriel-Miro Muntean, and John Murphy. 2015. A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Commun. Surv. Tutor. 17, 1 (2015), 125--151.

Digital Library

[23]

dnp systems. 2017. Physical Security and Access Control Systems. Retrieved from http://www.dnpsys.com/solution.php.

[24]

Santiago González, Wilder Castellanos, Paola Guzmán, Pau Arce, and Juan Carlos Guerri. 2016. Simulation and experimental testbed for adaptive video streaming in ad hoc networks. Ad Hoc Netw. 52 (2016), 89--105.

Digital Library

[25]

Google. 2013. Google Prediction API. Retrieved from https://cloud.google.com/prediction/docs/.

[26]

Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim. 1998. CURE: An efficient clustering algorithm for large databases. In ACM Sigmod Rec., Vol. 27. ACM, 73--84.

[27]

Hadi Habibzadeh, Zhou Qin, Tolga Soyata, and Burak Kantarci. 2017. Large scale distributed dedicated-and non-dedicated smart city sensing systems. IEEE Sens. J. (2017).

[28]

John A. Hartigan and Manchek A. Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. J. Royal Stat. Societ. Series C (Applied Stat.) 28, 1 (1979), 100--108.

[29]

Ibrahim Abaker Targio Hashem, Victor Chang, Nor Badrul Anuar, Kayode Adewole, Ibrar Yaqoob, Abdullah Gani, Ejaz Ahmed, and Haruna Chiroma. 2016. The role of big data in smart city. Int. J. Inform. Manag. 36, 5 (2016), 748--758.

Digital Library

[30]

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. Overview of supervised learning. In The Elements of Statistical Learning. Springer, 9--41.

[31]

Geoffrey E. Hinton and Terrence Joseph Sejnowski. 1999. Unsupervised Learning: Foundations of Neural Computation. MIT Press.

Digital Library

[32]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computat. 9, 8 (1997), 1735--1780.

Digital Library

[33]

Jun Huang, Qiang Duan, Yanxiao Zhao, Zhong Zheng, and Wei Wang. 2017. Multicast routing for multimedia communications in the Internet of things. IEEE Internet Things J. 4, 1 (2017), 215--224.

[34]

Don R. Hush and Bill G. Horne. 1993. Progress in supervised neural networks. IEEE Sig. Proc. Mag. 10, 1 (1993), 8--39.

[35]

Aapo Hyvärinen, Juha Karhunen, and Erkki Oja. 2004. Independent Component Analysis. Vol. 46. John Wiley 8 Sons.

[36]

IBM. 2012. IBM’s Smarter Cities Challenge: Boston Report. Retrieved from https://www.smartercitieschallenge.org/assets/cities/boston-united-states/documents/boston-united-states-full-report-2012.pdf.

[37]

IBM. 2012. IBM’s Smarter Cities Challenge: Louisville Summary Report. Retrieved from https://www.prd-ibm-smarter-cities-challenge.s3.amazonaws.com/applications/louisville-united-states-summary-2012.pdf.

[38]

IBM. 2013. IBM Watson API. Retrieved from https://www.ibm.com/watson/developercloud/doc/index.html.

[39]

IBM. 2014. IBM’s Smarter Cities Challenge Report: Suffolk County, New York, United States. Retrieved from: https://www.suffolkcountyny.gov/Portals/0/planning/.../IBM_SuffolkCounty_Report2014r.pdf.

[40]

Md. Mofijul Islam, Md. Abdur Razzaque, Mohammad Mehedi Hassan, Walaa Nagy, and Biao Song. 2017. Mobile cloud-based big healthcare data processing in smart cities. IEEE Access 5 (May 2017), 11887--11899.

[41]

Alan Julian Izenman. 2013. Linear discriminant analysis. In Modern Multivariate Statistical Techniques. Springer, 237--280.

[42]

Anil K. Jain. 2010. Data clustering: 50 years beyond k-means. Pattern Recog. Lett. 31, 8 (2010), 651--666.

Digital Library

[43]

Mian Ahmad Jan, Muhammad Usman, Xiangjian He, and Ateeq Ur Rehman. 2018. SAMS: A seamless and authorized multimedia streaming framework for WMSN-based IoMT. IEEE Internet Things J. (2018).

[44]

Stephen C. Johnson. 1967. Hierarchical clustering schemes. Psychometrika 32, 3 (1967), 241--254.

[45]

Josep Miquel Jornet and Ian F. Akyildiz. 2012. The Internet of multimedia nano-things. Nano Commun. Netw. 3, 4 (2012), 242--251.

[46]

Nesrine Khernane, Jean-François Couchot, and Ahmed Mostefaoui. 2019. Optimal power/rate trade-off for Internet of multimedia things lifetime maximization under dynamic links capacity. Future Generation Computer Systems 93 (April 2019), 737--750.

[47]

Teuvo Kohonen. 1990. Improved versions of learning vector quantization. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’90). IEEE, 545--550.

[48]

Teuvo Kohonen. 1995. Learning vector quantization. In Self-organizing Maps. Springer, 175--189.

[49]

Hans-Peter Kriegel, Peer Kröger, Jörg Sander, and Arthur Zimek. 2011. Density-based clustering. Wiley Interdisc. Rev.: Data Mining Knowl. Disc. 1, 3 (2011), 231--240.

[50]

Neeraj Kumar, Sherali Zeadally, and Joel J. P. C. Rodrigues. 2015. QoS-aware hierarchical web caching scheme for online video streaming applications in Internet-based vehicular ad hoc networks. IEEE Trans. Industr. Electron. 62, 12 (2015), 7892--7900.

[51]

Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S. Hopkins, and Nina Kruschwitz. 2011. Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52, 2 (2011), 21.

[52]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444.

[53]

Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th International Conference on Machine Learning. ACM, 609--616.

[54]

Chiehyeon Lim, Kwang-Jae Kim, and Paul P. Maglio. 2018. Smart cities with big data: Reference models, challenges, and considerations. Cities 82 (Dec. 2018), 86--99.

[55]

Julien Mairal, Francis Bach, Jean Ponce, and Guillermo Sapiro. 2010. Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, Jan. (2010), 19--60.

[56]

Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, and Andrew Zisserman. 2009. Non-local sparse models for image restoration. In Proceedings of the 12th IEEE International Conference on Computer Vision. IEEE, 2272--2279.

[57]

Julien Mairal, Jean Ponce, Guillermo Sapiro, Andrew Zisserman, and Francis R. Bach. 2009. Supervised dictionary learning. In Adv. Neural Inform. Proc. Syst. 1033--1040.

Digital Library

[58]

Muhammad Faran Majeed, Syed Hassan Ahmed, Siraj Muhammad, Houbing Song, and Danda B. Rawat. 2017. Multimedia streaming in information-centric networking: A survey and future perspectives. Computer Networks 125, 9 (Oct. 2017), 103--121.

[59]

Pavel Masek, Jan Masek, Petr Frantik, Radek Fujdiak, Aleksandr Ometov, Jiri Hosek, Sergey Andreev, Petr Mlynek, and Jiri Misurec. 2016. A harmonized perspective on transportation management in smart cities: The novel IoT-driven environment for road traffic modeling. Sensors 16, 11 (2016), 1872.

[60]

Yasir Mehmood, Farhan Ahmad, Ibrar Yaqoob, Asma Adnane, Muhammad Imran, and Sghaier Guizani. 2017. Internet-of-things-based smart cities: Recent advances and challenges. IEEE Commun. Mag. 55, 9 (2017), 16--24.

Digital Library

[61]

Ahmad Mohamad Mezher and Mónica Aguilar Igartua. 2017. Multimedia multimetric map-aware routing protocol to send video-reporting messages over VANETs in smart cities. IEEE Trans. Vehic. Technol. 66, 12 (Dec. 2017), 10611--10625.

[62]

Microsoft. 2015. Microsoft Azure API. Retrieved from https://azure.microsoft.com/en-us/services/cognitive-services/.

[63]

Satyajayant Misra, Martin Reisslein, and Guoliang Xue. 2008. A survey of multimedia streaming in wireless sensor networks. IEEE Commun. Surv. Tutor. 10, 4 (2008).

Digital Library

[64]

Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2012. Foundations of Machine Learning. MIT Press.

Digital Library

[65]

M. Victoria Moreno, Fernando Terroso-Sáenz, Aurora González-Vidal, Mercedes Valdés-Vela, Antonio F. Skarmeta, Miguel A. Zamora, and Victor Chang. 2017. Applicability of big data techniques to smart cities deployments. IEEE Trans. Industr. Inform. 13, 2 (2017), 800--809.

[66]

Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML’10). 807--814.

Digital Library

[67]

S. Aasha Nandhini and S. Radha. 2017. Efficient compressed sensing-based security approach for video surveillance application in wireless multimedia sensor networks. Computers 8 Electrical Engineering 60 (May 2017), 175--192.

Digital Library

[68]

Andrew Ng. 2011. Sparse autoencoder. CS294A Lect. Notes 72, 2011 (2011), 1--19.

[69]

Zhaolong Ning, Feng Xia, Noor Ullah, Xiangjie Kong, and Xiping Hu. 2017. Vehicular social networks: Enabling smart mobility. IEEE Commun. Mag. 55, 5 (2017), 16--55.

Digital Library

[70]

OECD. 2015. The OECD Model Survey on ICT Access and Usage by Households and Individuals. Retrieved from https://www.oecd.org/sti/ieconomy/.

[71]

Ahmed M. Shahat Osman, Ahmed Elragal, and Birgitta Bergvall-Kåreborn. 2017. Big data analytics and smart cities: A loose or tight couple? In Proceedings of the 10th International Conference on Connected Smart Cities (CSC’17). IADIS, 157--168.

[72]

Michael Quinn Patton. 2005. Qualitative Research. Wiley Online Library.

[73]

João Paulo Just Peixoto and Daniel G. Costa. 2017. Wireless visual sensor networks for smart city applications: A relevance-based approach for multiple sinks mobility. Future Generation Computer Systems 76 (Nov. 2017), 51--62.

Digital Library

[74]

Javier Poncela, Panagiotis Vlacheas, Raffaele Giaffreda, Suparna De, Massimo Vecchio, Septimiu Nechifor, Raquel Barco, Mari Carmen Aguayo-Torres, Vera Stavroulaki, Klaus Moessner, et al. 2014. Smart cities via data aggregation. Wireless Personal Communications 76, 2 (2014), 149--168.

Digital Library

[75]

Behrouz Pourghebleh and Nima Jafari Navimipour. 2017. Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research. J. Netw. Comput. Applicat. 97, 1 (Nov. 2017), 23--34.

Digital Library

[76]

Dan Puiu, Payam Barnaghi, Ralf Toenjes, Daniel Kümper, Muhammad Intizar Ali, Alessandra Mileo, Josiane Xavier Parreira, Marten Fischer, Sefki Kolozali, Nazli Farajidavar, et al. 2016. Citypulse: Large scale data analytics framework for smart cities. IEEE Access 4 (2016), 1086--1108.

[77]

Wullianallur Raghupathi and Viju Raghupathi. 2014. Big data analytics in healthcare: Promise and potential. Health Inform. Sci. Syst. 2, 1 (2014), 3.

[78]

Cristiano Rezende, Azzedine Boukerche, Heitor S. Ramos, and Antonio A. F. Loureiro. 2015. A reactive and scalable unicast solution for video streaming over VANETs. IEEE Trans. Comput. 64, 3 (2015), 614--626.

Digital Library

[79]

Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio. 2011. Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th International Conference on Machine Learning (ICML’11). 833--840.

Digital Library

[80]

Denis Rosário, Zhongliang Zhao, Aldri Santos, Torsten Braun, and Eduardo Cerqueira. 2014. A beaconless opportunistic routing based on a cross-layer approach for efficient video dissemination in mobile multimedia IoT applications. Comput. Commun. 45 (2014), 21--31.

[81]

Sam T. Roweis and Lawrence K. Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 5500 (2000), 2323--2326.

[82]

Thomas A. Runkler. 2012. Data Analytics. Springer.

[83]

Omar Said, Yasser Albagory, Mostafa Nofal, and Fahad Al Raddady. 2017. IoT-RTP and IoT-RTCP: Adaptive protocols for multimedia transmission over Internet of things environments. IEEE Access 5 (2017), 16757--16773.

[84]

Johannes M. Schleicher, Michael Vögler, Schahram Dustdar, and Christian Inzinger. 2016. Application architecture for the Internet of cities: Blueprints for future smart city applications. IEEE Internet Comput. 20, 6 (2016), 68--75.

Digital Library

[85]

Jürgen Schmidhuber. 2015. Deep learning in neural networks: An overview. Neural Netw. 61 (2015), 85--117.

Digital Library

[86]

Hang Shen and Guangwei Bai. 2016. Routing in wireless multimedia sensor networks: A survey and challenges ahead. J. Netw. Comput. Applicat. 71 (2016), 30--49.

Digital Library

[87]

Bhagya Nathali Silva, Murad Khan, and Kijun Han. 2018. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Societ. 38 (2018), 697--713.

[88]

Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations.

[89]

Hatem Ben Sta. 2017. Quality and the efficiency of data in “Smart-Cities.” Fut. Gen. Comput. Syst. 74 (2017), 409--416.

[90]

Paula Ta-Shma, Adnan Akbar, Guy Gerson-Golan, Guy Hadash, Francois Carrez, and Klaus Moessner. 2018. An ingestion and analytics architecture for IoT applied to smart city use cases. IEEE Internet Things J. 5, 2 (April 2018), 765--774.

[91]

Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: A simple and general method for semi-supervised learning. In Proceedings of the 48th Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 384--394.

Digital Library

[92]

Muhammad Usman, Xiangjian He, Kenneth K. M. Lam, Min Xu, Jinjun Chen, Syed Mohsin Matloob Bokhari, and Mian Ahmad Jan. 2017. Error concealment for cloud-based and scalable video coding of HD videos. IEEE Trans. Cloud Comput. (2017). Early Access.

[93]

Muhammad Usman, Xiangjian He, Kin-Man Lam, Min Xu, Syed Mohsin Matloob Bokhari, and Jinjun Chen. 2016. Frame interpolation for cloud-based mobile video streaming. IEEE Trans. Multimed. 18, 5 (2016), 831--839.

Digital Library

[94]

Muhammad Usman, Mian Ahmad Jan, and Xiangjian He. 2017. Cryptography-based secure data storage and sharing using HEVC and public clouds. Inform. Sci. 387 (2017), 90--102.

Digital Library

[95]

Muhammad Usman, Mian Ahmad Jan, Xiangjian He, and Muhammad Alam. 2018. Performance evaluation of high definition video streaming over mobile ad hoc networks. Sig. Proc. 148 (2018), 303--313.

Digital Library

[96]

Muhammad Usman, Mian Ahmad Jan, Xiangjian He, and Jinjun Chen. 2018. A mobile multimedia data collection scheme for secured wireless multimedia sensor networks. IEEE Trans. Netw. Sci. Eng. (2018). Early Access.

[97]

Muhammad Usman, Mian Ahmad Jan, Xiangjian He, and Priyadarsi Nanda. 2016. Data sharing in secure multimedia wireless sensor networks. In Proceedings of the IEEE Trustcom/BigDataSE/ISPA. IEEE, 590--597.

[98]

Muhammad Usman, Mian Ahmad Jan, Xiangjian He, and Priyadarsi Nanda. 2018. QASEC: A secured data communication scheme for mobile ad hoc networks. Fut. Gen. Comput. Syst. (2018). Early Access.

[99]

Muhammad Usman, Ning Yang, Mian Ahmad Jan, Xiangjian He, Min Xu, and Kin-Man Lam. 2018. A joint framework for QoS and QoE for video transmission over wireless multimedia sensor networks. IEEE Trans. Mobile Comput. 17, 4 (2018), 746--759.

[100]

Rosamunde Van Brakel and Paul De Hert. 2011. Policing, surveillance and law in a pre-crime society: Understanding the consequences of technology based strategies.Technol.-Led Polic. 20 (2011), 165--92.

[101]

Nguyen-Son Vo, Thanh-Hieu Nguyen, and Huu Khuong Nguyen. 2017. Joint active duty scheduling and encoding rate allocation optimized performance of wireless multimedia sensor networks in smart cities. Mobile Netw. Applicat. (2017), 1--11.

Digital Library

[102]

Michael Vögler, Johannes M. Schleicher, Christian Inzinger, Schahram Dustdar, and Rajiv Ranjan. 2016. Migrating smart city applications to the cloud. IEEE Cloud Comput. 3, 2 (2016), 72--79.

[103]

Junyi Wang, Zhihui Liu, Yuxiang Shen, Hongbin Chen, Lin Zheng, Hongbing Qiu, and Shi Shu. 2016. A distributed algorithm for inter-layer network coding-based multimedia multicast in Internet of things. Comput. Elect. Eng. 52 (2016), 125--137.

Digital Library

[104]

Xiaolong Wang and Abhinav Gupta. 2015. Unsupervised learning of visual representations using videos. In Proceedings of the IEEE International Conference on Computer Vision. 2794--2802.

Digital Library

[105]

Wikipedia. 2017. Surveillance Issues in Smart Cities. Retrieved from https://en.wikipedia.org/wiki/Surveillance_issues_in_smart_cities.

[106]

Wikipedia. 2018. Comparison of Deep Learning Software. Retrieved from: https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software.

[107]

Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemomet. Intell. Lab. Syst. 2, 1--3 (1987), 37--52.

[108]

John Wright, Allen Y. Yang, Arvind Ganesh, S. Shankar Sastry, and Yi Ma. 2009. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2 (2009), 210--227.

Digital Library

[109]

Zhe Xiao, Hock Beng Lim, and Loganathan Ponnambalam. 2017. Participatory sensing for smart cities: A case study on transport trip quality measurement. IEEE Trans. Industr. Inform. 13, 2 (2017), 759--770.

[110]

Changqiao Xu, Wei Quan, Hongke Zhang, and Luigi Alfredo Grieco. 2018. GrIMS: Green information-centric multimedia streaming framework in vehicular ad hoc networks. IEEE Trans. Circ. Syst. Vid. Technol. 28, 2 (Feb. 2018), 483--498.

[111]

Xiaowei Xu, Martin Ester, H.-P. Kriegel, and Jörg Sander. 1998. A distribution-based clustering algorithm for mining in large spatial databases. In Proceedings of the 14th International Conference on Data Engineering. IEEE, 324--331.

Digital Library

[112]

Jianchao Yang, Kai Yu, Yihong Gong, and Thomas Huang. 2009. Linear spatial pyramid matching using sparse coding for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’09). IEEE, 1794--1801.

[113]

Runan Yao, Wei Wang, Mahdi Farrokh-Baroughi, Honggang Wang, and Yi Qian. 2013. Quality-driven energy-neutralized power and relay selection for smart grid wireless multimedia sensor based IoTs. IEEE Sens. J. 13, 10 (2013), 3637--3644.

[114]

YIN Yuehong, Yan Zeng, Xing Chen, and Yuanjie Fan. 2016. The Internet of things in healthcare: An overview. J. Industr. Inform. Integ. 1 (2016), 3--13.

[115]

Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, and Michele Zorzi. 2014. Internet of things for smart cities. IEEE Internet Things J. 1, 1 (2014), 22--32.

[116]

Ke Zhang, Haitao Jia, and Haibin Lv. 2016. Coverage-enhancing approach in multimedia directional sensor networks for smart transportation. Multimed. Tools Applicat. 75, 24 (2016), 17593--17615.

Digital Library

[117]

Kaile Zhou, Chao Fu, and Shanlin Yang. 2016. Big data driven smart energy management: From big data to big insights. Renew. Sustain. Energy Rev. 56 (2016), 215--225.

[118]

Paul Zikopoulos, Chris Eaton, et al. 2011. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 3

May 2020

734 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3341324

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Publication History

Published: 18 June 2019

Accepted: 01 February 2019

Revised: 01 January 2019

Received: 01 May 2018

Published in CSUR Volume 52, Issue 3

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