survey

Public Access

Smart City System Design: A Comprehensive Study of the Application and Data Planes

Authors:

Hadi Habibzadeh,

Burak Kantarci,

Azzedine BoukercheAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 2

Article No.: 41, Pages 1 - 38

https://doi.org/10.1145/3309545

Published: 30 May 2019 Publication History

All formats PDF

Abstract

Recent global smart city efforts resemble the establishment of electricity networks when electricity was first invented, which meant the start of a new era to sell electricity as a utility. A century later, in the smart era, the network to deliver services goes far beyond a single entity like electricity. Supplemented by a well-established Internet infrastructure that can run an endless number of applications, abundant processing and storage capabilities of clouds, resilient edge computing, and sophisticated data analysis like machine learning and deep learning, an already-booming Internet of Things movement makes this new era far more exciting.

In this article, we present a multi-faceted survey of machine intelligence in modern implementations. We partition smart city infrastructure into application, sensing, communication, security, and data planes and put an emphasis on the data plane as the mainstay of computing and data storage. We investigate (i) a centralized and distributed implementation of data plane’s physical infrastructure and (ii) a complementary application of data analytics, machine learning, deep learning, and data visualization to implement robust machine intelligence in a smart city software core. We finalize our article with pointers to open issues and challenges.

References

[1]

C. C. Aggarwal, N. Ashish, and A. Sheth. 2013. The internet of things: A survey from the data-centric perspective. In Managing and Mining Sensor Data. Springer, Boston, MA, 383--428.

[2]

M. Agiwal, A. Roy, and N. Saxena. 2016. Next generation 5G wireless networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 18, 3 (2016), 1617--1655.

Digital Library

[3]

B. Ahlgren, C. Dannewitz, C. Imbrenda, D. Kutscher, and B. Ohlman. 2012. A survey of information-centric networking. IEEE Commun. Mag. 50, 7 (2012), 26--36.

[4]

Hannele Ahvenniemi, Aapo Huovila, Isabel Pinto-Seppä, and Miimu Airaksinen. 2017. What are the differences between sustainable and smart cities? Cities 60 (2017), 234--245.

[5]

A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. 2015. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17, 4 (2015), 2347--2376.

Digital Library

[6]

A. Al-Wakeel and J. Wu. 2016. K-means based cluster analysis of residential smart meter measurements. Energy Procedia 88, Suppl. C (2016), 754--760. Cities and Urban Energy 2015-Applied Energy Symposium and Summit 2015: Low carbon cities and urban energy systems.

[7]

M. Alhussein. 2017. Monitoring Parkinson’s disease in smart cities. IEEE Access 5 (2017), 19835--19841.

[8]

S. Ames, M. Venkitasubramaniam, A. Page, O. Kocabas, and T. Soyata. 2015. Secure health monitoring in the cloud using homomorphic encryption, a branching-program formulation. In Enabling Real-Time Mobile Cloud Computing through Emerging Technologies, T. Soyata (Ed.). IGI Global, Chapter 4, 116--152.

[9]

S. M. Amin and B. F. Wollenberg. 2005. Toward a smart grid: Power delivery for the 21st century. IEEE mpe 3, 5 (2005), 34--41.

[10]

O. Arias, J. Wurm, K. Hoang, and Y. Jin. 2015. Privacy and security in internet of things and wearable devices. IEEE Trans. Mobile Comput. Sci. 1, 2 (Apr. 2015), 99--109.

Digital Library

[11]

A. Basalamah. 2016. Sensing the crowds using Bluetooth low energy tags. IEEE Access 4 (2016), 4225--4233.

[12]

Mariana Belgiu and Lucian Drăguţ. 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogram. Remote Sens. 114 (2016), 24--31.

[13]

Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the internet of things. In Proceedings of the 1st MCC Workshop on Mobile Cloud Computing. ACM, New York, NY, 13--16.

Digital Library

[14]

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1 (2011), 1--122.

Digital Library

[15]

Bruno M. Brentan, Edevar Luvizotto Jr., Manuel Herrera, Joaquín Izquierdo, and Rafael Pérez-García. 2017. Hybrid regression model for near real-time urban water demand forecasting. J. Comput. Appl. Math. 309 (2017), 532--541.

Digital Library

[16]

R. Buyya, H. Stockinger, J. Giddy, and D. Abramson. 2001. Economic models for management of resources in peer-to-peer and grid computing. In Commercial Applications for High-Performance Computing. Proc. SPIE 4528 (2001), 13--26.

[17]

K. Cabaj and W. Mazurczyk. 2016. Using software-defined networking for ransomware mitigation: The case of CryptoWall. IEEE Netw. 30, 6 (Nov. 2016), 14--20.

Digital Library

[18]

F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti. 2011. Real-time urban monitoring using cell phones: A case study in Rome. IEEE Trans. Intell. Transport. Syst. 12, 1 (Mar. 2011), 141--151.

Digital Library

[19]

Luca Calderoni, Matteo Ferrara, Annalisa Franco, and Dario Maio. 2015. Indoor localization in a hospital environment using random forest classifiers. Expert Syst. Appl. 42, 1 (2015), 125--134.

Digital Library

[20]

O. Canovas, P. E. Lopez de Teruel, and A. Ruiz. 2017. Detecting indoor/outdoor places using WiFi signals and AdaBoost. IEEE Sens. J. 17, 5 (Mar. 2017), 1443--1453.

[21]

L. Catarinucci, D. de Donno, L. Mainetti, L. Palano, L. Patrono, M. L. Stefanizzi, and L. Tarricone. 2015. An IoT-aware architecture for smart healthcare systems. IEEE IofT J. 2, 6 (Dec. 2015), 515--526.

[22]

K. Y. Chan and T. S. Dillon. 2013. On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and taguchi method. IEEE Trans. Instrum. Meas. 62, 1 (Jan. 2013), 50--59.

[23]

Gary W. Chang and H. J. Lu. 2018. Integrating grey data preprocessor and deep belief network for day-ahead PV power output forecast. IEEE Trans. Sust. Energy (2018), 1--1.

[24]

Min Chen, Jun Yang, Jiehan Zhou, Yixue Hao, Jing Zhang, and Chan-Hyun Youn. 2018. 5G-smart diabetes: Towards personalized diabetes diagnosis with healthcare big data clouds. IEEE Commun. Mag. 56, 4 (2018), 16--23.

[25]

X. Chen and A. L. Yuille. 2005. A time-efficient cascade for real-time object detection: With applications for the visually impaired. in Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition (2005), 28--28.

Digital Library

[26]

W. C. Cheng and D. M. Jhan. 2013. Triaxial accelerometer-based fall detection method using a self-constructing Cascade-AdaBoost-SVM classifier. IEEE J. Biomed. Health Inform. 17, 2 (Mar. 2013), 411--419.

[27]

Igor M. Coelho, Vitor N. Coelho, Eduardo J. da S. Luz, Luiz S. Ochi, Frederico G. Guimarães, and Eyder Rios. 2017. A GPU deep learning metaheuristic based model for time series forecasting. Appl. Energy 201 (2017), 412--418.

[28]

Johan Colding and Stephan Barthel. 2017. An urban ecology critique on the smart city model. J. Clean. Prod. 164 (2017), 95--101.

[29]

M. Collotta and G. Pau. 2015. A novel energy management approach for smart homes using Bluetooth low energy. IEEE J. Select. Areas Commun. 33, 12 (Dec. 2015), 2988--2996.

Digital Library

[30]

B. F. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears. 2010. Benchmarking cloud serving systems with YCSB. In Proceedings of the 1st ACM Symposium on Cloud Computing. ACM, New York, NY, 143--154.

Digital Library

[31]

J. P. Couderc, M. K. Aktas, T. Soyata, and A. T. Page. 2016. ECG Clock Electrocardiogram Based Diagnostic Device and Method. US Patent App. 15/368,587.

[32]

S. Dey, A. Chakraborty, S. Naskar, and P. Misra. 2012. Smart city surveillance: Leveraging benefits of cloud data stores. In Proceedings of the 37th Annual IEEE Conference on Local Computer Networks Workshops. IEEE, Los Alamitos, CA, 868--876.

[33]

R. Du, C. Chen, B. Yang, N. Lu, X. Guan, and X. Shen. 2015. Effective urban traffic monitoring by vehicular sensor networks. IEEE Trans. Vehic. Technol. 64, 1 (Jan. 2015), 273--286.

[34]

M. Erol-Kantarci and M. Uysal. 2016. Multiple Access in Visible Light Communication Networks. Springer International Publishing, Cham, 451--461.

[35]

A. Fahad, T. Soyata, T. Wang, G. Sharma, W. Heinzelman, and K. Shen. 2012. SOLARCAP: Super capacitor buffering of solar energy for self-sustainable field systems. In Proceedings of the 25th IEEE International System-on-Chip Conference. IEEE, Los Alamitos, CA, 236--241.

[36]

Eleni Fotopoulou, Anastasios Zafeiropoulos, Fernando Terroso-Sáenz, Umutcan Şimşek, Aurora González-Vidal, George Tsiolis, Panagiotis Gouvas, Paris Liapis, Anna Fensel, and Antonio Skarmeta. 2017. Providing personalized energy management and awareness services for energy efficiency in smart buildings. Sensors 17, 9 (2017), 2054.

[37]

The OpenStack Foundation. 2010. Openstack Storage. Retrieved from https://www.openstack.org/software/.

[38]

R. K. Ganti, F. Ye, and H. Lei. 2011. Mobile crowdsensing: Current state and future challenges. IEEE Commun. Mag. 49, 11 (Nov. 2011), 32--39.

[39]

C. Gao and M. Redfern. 2011. A review of voltage control in smart grid and smart metering technologies on distribution networks. In Proceedings of the International Universities Power Engineering Conference (UPEC’11). IEEE, Los Alamitos, CA, 1--5.

[40]

Y. Geng, J. Chen, R. Fu, G. Bao, and K. Pahlavan. 2016. Enlighten wearable physiological monitoring systems: On-body RF characteristics based human motion classification using a support vector machine. IEEE Technol. Manage. Council 15, 3 (Mar. 2016), 656--671.

Digital Library

[41]

Van Gerwen, Rob, Saskia Jaarsma, and Rob Wilhite. 2006. Smart Metering, KEMA. Retrieved from http://www.idc-online.com/technical_references/pdfs/electrical_engineering/Smart_Metering.pdf (accessed April 2019).

[42]

A. Gharaibeh, M. A. Salahuddin, S. J. Hussini, A. Khreishah, I. Khalil, M. Guizani, and A. Al-Fuqaha. 2017. Smart cities: A survey on data management, security and enabling technologies. IEEE Commun. Surv. Tutor. 19, 4 (2017), 2456--2501.

[43]

M. Gramaglia, M. Calderon, and C. J. Bernardos. 2014. ABEONA monitored traffic: VANET-assisted cooperative traffic congestion forecasting. IEEE Vehic. Technol. Mag. 9, 2 (Jun. 2014), 50--57.

[44]

T. Guelzim, M. S. Obaidat, and B. Sadoun. 2016. Chapter 1 - Introduction and overview of key enabling technologies for smart cities and homes. In Smart Cities and Homes, Mohammad S. Obaidat and Petros Nicopolitidis (Eds.). Morgan Kaufmann, Boston, 1--16.

[45]

Bin Guo, Zhu Wang, Zhiwen Yu, Yu Wang, Neil Y. Yen, Runhe Huang, and Xingshe Zhou. 2015. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48, 1, Article 7 (Aug. 2015), 31 pages.

Digital Library

[46]

S. Gupta, R. Kambli, S. Wagh, and F. Kazi. 2015. Support-vector-machine-based proactive cascade prediction in smart grid using probabilistic framework. IEEE Trans. Industr. Electron. 62, 4 (Apr. 2015), 2478--2486.

[47]

Hadi Habibzadeh, Tolga Soyata, Burak Kantarci, Azzedine Boukerche, and Cem Kaptan. 2018. Sensing, communication and security planes: A new challenge for a smart city system design. Comput. Netw. 144 (2018), 163--200.

Digital Library

[48]

M. Habibzadeh, A. Boggio-Dandry, Z. Qin, T. Soyata, B. Kantarci, and H. Mouftah. 2018. Soft sensing in smart cities: Handling 3Vs using recommender systems, machine intelligence, and data analytics. IEEE Commun. Mag. 56, 2 (Feb. 2018), 78--86.

Digital Library

[49]

M. Habibzadeh, M. Hassanalieragh, A. Ishikawa, T. Soyata, and G. Sharma. 2017. Hybrid solar-wind energy harvesting for embedded applications: Supercapacitor-based system architectures and design tradeoffs. IEEE Circuits And Systems Magazine (IEEE MCAS) 17, 4 (Nov. 2017), 29--63.

[50]

M. Habibzadeh, M. Hassanalieragh, T. Soyata, and G. Sharma. 2017. Solar/wind hybrid energy harvesting for supercapacitor-based embedded systems. In Proceeding of the IEEE Midwest Symposium on Circuits and Systems. IEEE, Los Alamitos, CA, 329--332.

[51]

M. Habibzadeh, M. Hassanalieragh, T. Soyata, and G. Sharma. 2017. Supercapacitor-based embedded hybrid solar/wind harvesting system architectures. In Proceedings of the 30th IEEE International System-on-Chip Conference. IEEE, Los Alamitos, CA.

[52]

M. Habibzadeh, Z. Qin, T. Soyata, and B. Kantarci. 2017. Large scale distributed dedicated- and non-dedicated smart city sensing systems. IEEE Sens. J. 17, 23 (Dec. 2017), 7649--7658.

[53]

M. U. Hameed, S. A. Haider, and B. Kantarci. 2017. Performance impacts of hybrid cloud storage. Computing 99, 12 (01 Dec. 2017), 1207--1229.

Digital Library

[54]

F. Harada, T. Ushio, and Y. Nakamoto. 2007. Power-aware optimization of CPU and frequency allocation based on fairness of QoS. Syst. Comput. Jpn. 38, 12 (2007), 37--45.

Digital Library

[55]

M. Hasan, E. Hossain, and D. Niyato. 2013. Random access for machine-to-machine communication in LTE-advanced networks: Issues and approaches. IEEE Commun. Mag. 51, 6 (Jun. 2013), 86--93.

[56]

David Hasenfratz, Olga Saukh, Christoph Walser, Christoph Hueglin, Martin Fierz, Tabita Arn, Jan Beutel, and Lothar Thiele. 2015. Deriving high-resolution urban air pollution maps using mobile sensor nodes. Perv. Mobile Comput. 16 (2015), 268--285.

Digital Library

[57]

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. Inf. Manage. 36, 5 (2016), 748--758.

Digital Library

[58]

M. Hassanalieragh, A. Page, T. Soyata, G. Sharma, M. K. Aktas, G. Mateos, B. Kantarci, and S. Andreescu. 2015. Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: Opportunities and challenges. In Proceedings of the 2015 IEEE International Conference on Services Computing. IEEE, 285--292.

Digital Library

[59]

M. Hassanalieragh, T. Soyata, A. Nadeau, and G. Sharma. 2016. UR-SolarCap: An open source intelligent auto-wakeup solar energy harvesting system for supercapacitor based energy buffering. IEEE Access 4 (Mar. 2016), 542--557.

[60]

G. Hauber-Davidson and E. Idris. 2006. Smart water metering. Water 33, 3 (2006), 38--41.

[61]

Y. He, G. J. Mendis, Q. Gao, and J. Wei. 2016. Towards smarter cities: A self-healing resilient microgrid social network. In Proceedings of the Power and Energy Society General Meeting (PESGM’16). IEEE, 1--5.

[62]

S. Hijazi, A. Page, B. Kantarci, and T. Soyata. 2016. Machine learning in cardiac health monitoring and decision support. IEEE Comput. Mag. 49, 11 (Nov. 2016), 38--48.

Digital Library

[63]

H. Huang. 2015. Multi-access Edge Computing. Retrieved from http://www.etsi.org/technologies-clusters/technologies/multi-access-edge-computing.

[64]

MongoDB Inc. 2009. MongoDB. Retrieved from https://www.mongodb.com/.

[65]

A. Ipakchi and F. Albuyeh. 2009. Grid of the future. IEEE Power Energy Mag. 7, 2 (2009), 52--62.

[66]

Ishwarappa and J. Anuradha. 2015. A brief introduction on big data 5Vs characteristics and hadoop technology. Proc. Comput. Sci. 48, Suppl. C (2015), 319--324.

[67]

J. Jung and K. Sohn. 2017. Deep-learning architecture to forecast destinations of bus passengers from entry-only smart-card data. IET Intell. Transport Syst. 11, 6 (2017), 334--339.

[68]

Yu Kang, Xinting Wang, Xiu Cao, Yangfan Zhou, Zhichao Lai, Yuhao Li, Xuqi Zhang, and Wei Geng. 2018. Detecting anomalous users via streaming data processing in smart grid. In Proceedings of the 2018 International Conference on Minign Software Repositories (MSR’18). ACM, 14--20.

Digital Library

[69]

C. Kaptan, B. Kantarci, T. Soyata, and A. Boukerche. 2018. Emulating smart city sensors using soft sensing and machine intelligence: A case study in public transportation. In Proceedings of the IEEE International Conference on Communications (ICC’18). IEEE.

[70]

R. Khatoun and S. Zeadally. 2016. Smart cities: Concepts, architectures, research opportunities. Commun. ACM 59, 8 (2016), 46--57.

Digital Library

[71]

Y. Kim, T. Soyata, and R. F. Behnagh. 2018. Towards emotionally-aware AI smart classroom: Current issues and directions for engineering and education. IEEE Access 6 (2018), 5308--5331.

[72]

C. K. Koc, T. Acar, and B. S. Kaliski. 1996. Analyzing and comparing montgomery multiplication algorithms. IEEE Micro 16, 3 (1996), 26--33.

Digital Library

[73]

O. Kocabas and T. Soyata. 2015. Utilizing homomorphic encryption to implement secure and private medical cloud computing. In Proceedings of the IEEE 8th International Conference on Cloud Computing. IEEE, 540--547.

Digital Library

[74]

O. Kocabas, T. Soyata, and M. K. Aktas. 2016. Emerging security mechanisms for medical cyber physical systems. IEEE/ACM Trans. Comput. Biol. Bioinform. 13, 3 (Jun. 2016), 401--416.

Digital Library

[75]

A. O. Kotb, Y. c. Shen, and Y. Huang. 2017. Smart parking guidance, monitoring and reservations: A review. IEEE Intell. Transport. Syst. Mag. 9, 2 (2017), 6--16.

[76]

N. Lavrač, M. Bohanec, A. Pur, B. Cestnik, M. Debeljak, and A. Kobler. 2007. Data mining and visualization for decision support and modeling of public health-care resources. J. Biomed. Inf. 40, 4 (2007), 438--447.

Digital Library

[77]

I-G. Lee and M. Kim. 2016. Interference-aware self-optimizing Wi-Fi for high efficiency internet of things in dense networks. Comput. Commun. 89, Suppl. C (2016), 60--74.

Digital Library

[78]

W. D. Leon-Salas and C. Halmen. 2016. A RFID sensor for corrosion monitoring in concrete. IEEE Sens. J. 16, 1 (Jan 2016), 32--42.

[79]

Chao Li, Yushu Xue, Jing Wang, Weigong Zhang, and Tao Li. 2018. Edge-oriented computing paradigms: A survey on architecture design and system management. ACM Comput. Surv. 51, 2, Article 39 (Apr. 2018), 34 pages.

Digital Library

[80]

C.-S. Li and W. Liao. 2013. Software defined networks. IEEE Commun. Mag. 51, 2 (2013), 113--113.

[81]

I. Li, L. Deng, B. B. Gupta, H. Wang, and C. Choi. 2019. A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Information Sciences 479 (2019) 432--447.

[82]

Han Li, Hui Gao, Tiejun Lv, and Yueming Lu. 2018. Deep q-learning based dynamic resource allocation for self-powered ultra-dense networks. In Proceedings of the IEEE International Conference on Communications Workshops. IEEE, 1--6.

[83]

Ronghua Liang, Yuge Zhu, and Haixia Wang. 2014. Counting crowd flow based on feature points. Neurocomputing 133 (2014), 377--384.

Digital Library

[84]

K. Liao, Z. Zhao, A. Doupe, and G. J. Ahn. 2016. Behind closed doors: Measurement and analysis of CryptoLocker Ransoms in Bitcoin. In Proceedings of the 2016 APWG Symposium on Electronic Crime Research (eCrime’16). IEEE, 1--13.

[85]

F. Lin, A. Wang, Y. Zhuang, M. R. Tomita, and W. Xu. 2016. Smart insole: A wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Trans. Industr. Inf. 12, 6 (Dec. 2016), 2281--2291.

[86]

T. Lin, H. Rivano, and F. Le Mouël. 2017. A survey of smart parking solutions. IEEE Trans. Intell. Transport. Syst. 18, 12 (Dec. 2017), 3229--3253.

Digital Library

[87]

T. H. Luan, L. Gao, Z. Li, Y. Xiang, and L. Sun. 2015. Fog computing: Focusing on mobile users at the edge. CoRR abs/1502.01815 (2015). arxiv:1502.01815 http://arxiv.org/abs/1502.01815

[88]

I. Lujic, V. D. Maio, and I. Brandic. 2017. Efficient edge storage management based on near real-time forecasts. In Proceedings of the IEEE International Conference on Fog and Edge Computing (ICFEC’17). IEEE, 21--30.

[89]

X. Ma, H. Yu, Y. Wang, and Y. Wang. 2015. Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE 10, 3 (Mar. 2015), 1--17.

[90]

P. Mannion, J. Duggan, and E. Howley. 2016. An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control. Springer International Publishing, Cham, 47--66.

[91]

L. J. V. Miranda, M. J. S. Gutierrez, S. M. G. Dumlao, and R. S. Reyes. 2016. Appliance recognition using hall effect sensors and k-nearest neighbors for power management systems. In Proceedings of the IEEE Region Ten Conference (TENCON’16). IEEE, 6--9.

[92]

P. Mirchandani and Fei-Yue Wang. 2005. RHODES to intelligent transportation systems. IEEE Intelligent Systems 20, 1 (Jan. 2005), 10--15.

Digital Library

[93]

M. Mohammadi and A. Al-Fuqaha. 2018. Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. IEEE Commun. Mag. 56, 2 (Feb. 2018), 94--101.

Digital Library

[94]

A. Molina-Markham, P. Shenoy, K. Fu, E. Cecchet, and D. Irwin. 2010. Private memoirs of a smart meter. In Proceedings of the International Conference on Embedded Systems for Energy-Efficient Buildings (BuildSys’10). ACM, New York, NY, 61--66.

Digital Library

[95]

M. Muja and D. G. Lowe. 2014. Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36, 11 (Nov. 2014), 2227--2240.

[96]

A. Nadeau, M. Hassanalieragh, G. Sharma, and T. Soyata. 2015. Energy awareness for supercapacitors using kalman filter state-of-charge tracking. J. Power Sources 296 (Nov. 2015), 383--391.

[97]

D. Neumann, C. Bodenstein, O. F. Rana, and R. Krishnaswamy. 2011. STACEE: Enhancing storage clouds using edge devices. In Proceedings of the 1st ACM/IEEE Workshop on Autonomic Computing in Economics (ACE’11). ACM, New York, NY, 19--26.

Digital Library

[98]

T. T. T. Nguyen and G. Armitage. 2008. A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutor. 10, 4 (2008), 56--76.

Digital Library

[99]

A. A. Obinikpo and B. Kantarci. 2017. Big sensed data meets deep learning for smarter health care in smart cities. J. Sens. Actuator Netw. 6, 4 (2017).

[100]

A. Page, M. K. Aktas, T. Soyata, W. Zareba, and J. Couderc. 2016. QT clock to improve detection of QT prolongation in long QT syndrome patients. Heart Rhythm 13, 1 (Jan. 2016), 190--198.

[101]

A. Page, O. Kocabas, T. Soyata, M. K. Aktas, and J. Couderc. 2014. Cloud-based privacy-preserving remote ECG monitoring and surveillance. Ann. Noninvas. Electrocardiol. 20, 4 (2014), 328--337.

[102]

A. Page, T. Soyata, J. Couderc, M. Aktas, B. Kantarci, and S. Andreescu. 2015. Visualization of health monitoring data acquired from distributed sensors for multiple patients. In Proceedings of the IEEE Global Communications Conference (GLOBECOM’15). IEEE, 1--7.

[103]

A. Page, T. Soyata, J. Couderc, and M. K. Aktas. 2015. An open source ECG clock generator for visualization of long-term cardiac monitoring data. IEEE Access 3 (Dec. 2015), 2704--2714.

[104]

M. R. Palattella, M. Dohler, A. Grieco, G. Rizzo, J. Torsner, T. Engel, and L. Ladid. 2016. Internet of things in the 5G era: Enablers, architecture, and business models. IEEE J. Select. Areas Commun. 34, 3 (Ma. 2016), 510--527.

Digital Library

[105]

Veljko Pejovic and Mirco Musolesi. 2015. Anticipatory mobile computing: A survey of the state of the art and research challenges. ACM Comput. Surv. 47, 3 (2015), 47.

Digital Library

[106]

Gang Peng. 2004. CDN: Content distribution network. CoRR cs.NI/0411069 (2004). http://arxiv.org/abs/cs.NI/0411069

[107]

Charith Perera and Athanasios V. Vasilakos. 2016. A knowledge-based resource discovery for internet of things. Knowl.-Based Syst. 109 (2016), 122--136.

Digital Library

[108]

Gianluigi Pillonetto, Francesco Dinuzzo, Tianshi Chen, Giuseppe De Nicolao, and Lennart Ljung. 2014. Kernel methods in system identification, machine learning and function estimation: A survey. Automatica 50, 3 (2014), 657--682.

Digital Library

[109]

J. S. Plank. 2013. Erasure codes for storage systems: A brief primer. USENIX Mag. 38, 6 (2013), 44--50.

[110]

R. Polishetty, M. Roopaei, and P. Rad. 2016. A next-generation secure cloud-based deep learning license plate recognition for smart cities. In Proceedings of the International Conference on Machine Learning and Applications (ICMLA’16). IEEE, 286--293.

[111]

M. Pouryazdan, C. Fiandrino, B. Kantarci, T. Soyata, D. Kliazovich, and P. Bouvry. 2017. Intelligent gaming for mobile crowd-sensing participants to acquire trustworthy big data in the internet of things. IEEE Access 5, 1 (Dec. 2017), 22209--22223.

[112]

M. Pouryazdan, B. Kantarci, T. Soyata, L. Foschini, and H. Song. 2017. Quantifying user reputation scores, data trustworthiness, and user incentives in mobile crowd-sensing. IEEE Access 5 (Jan. 2017), 1382--1397.

[113]

M. Pouryazdan, B. Kantarci, T. Soyata, and H. Song. 2016. Anchor-assisted and vote-based trustworthiness assurance in smart city crowdsensing. IEEE Access 4 (Mar. 2016), 529--541.

[114]

J. Qin, W. Fu, H. Gao, and W. X. Zheng. 2017. Distributed -means algorithm and fuzzy -means algorithm for sensor networks based on multiagent consensus theory. IEEE Trans. Cybernet. 47, 3 (Mar. 2017), 772--783.

[115]

J. R. Lin, T. Talty, and O. K. Tonguz. 2015. On the potential of Bluetooth low energy technology for vehicular applications. IEEE Commun. Mag. 53, 1 (Jan. 2015), 267--275.

Digital Library

[116]

A. Reinhardt, D. Burkhardt, P. S. Mogre, M. Zaheer, and R. Steinmetz. 2011. SmartMeter.KOM: A low-cost wireless sensor for distributed power metering. In Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks (LCN’11). IEEE, 1032--1039.

Digital Library

[117]

RIEGL. 2017. RIEGL VMX 450. Retrieved from http://www.riegl.com/uploads/tx_pxpriegldownloads/RIEGL_VMX-450-RAIL_2015-08-24.pdf.

[118]

E. S. Rigas, S. D. Ramchurn, and N. Bassiliades. 2015. Managing electric vehicles in the smart grid using artificial intelligence: A survey. IEEE Trans. Intell. Transport. Syst. 16, 4 (Aug. 2015), 1619--1635.

Digital Library

[119]

J. Salamon and J. P. Bello. 2015. Unsupervised feature learning for urban sound classification. In Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’15). IEEE, 171--175.

[120]

R. Salpietro, L. Bedogni, M. Di Felice, and L. Bononi. 2015. Park here&excl; A smart parking system based on smartphones’ embedded sensors and short range communication technologies. In Proceedings of the World Forum on Internet of Things (WF-IoT’15). IEEE, 18--23.

Digital Library

[121]

E. F. Z. Santana, A. P. Chaves, M. A. Gerosa, F. Kon, and D. S. Milojicic. 2017. Software platforms for smart cities: Concepts, requirements, challenges, and a unified reference architecture. ACM Comput. Surv. 50, 6, Article 78 (Nov. 2017), 37 pages.

Digital Library

[122]

R. Schollmeier. 2001. A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications. In Proceedings of the 1st International Conference on Peer-to-Peer Computing. IEEE, 101--102.

Digital Library

[123]

A. Sevincer, A. Bhattarai, M. Bilgi, M. Yuksel, and N. Pala. 2013. LIGHTNETs: Smart LIGHTing and mobile optical wireless NETworks—A survey. IEEE Commun. Surv. Tutor. 15, 4 (2013), 1620--1641.

[124]

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. 2016. Edge computing: Vision and challenges. IEEE IoT J. 3, 5 (Oct. 2016), 637--646.

[125]

H. Y. Shwe, T. K. Jet, and P. H. J. Chong. 2016. An IoT-oriented data storage framework in smart city applications. In Proceedings of the International Conference on ICT Convergence (ICTC’16). IEEE, 106--108.

[126]

Y. Simmhan, S. Aman, A. Kumbhare, R. Liu, S. Stevens, Q. Zhou, and V. Prasanna. 2013. Cloud-based software platform for big data analytics in smart grids. Comput. Sci. Eng. 15, 4 (Jul. 2013), 38--47.

Digital Library

[127]

Eugene Siow, Thanassis Tiropanis, and Wendy Hall. 2018. Analytics for the internet of things: A survey. ACM Comput. Surv. 51, 4 (2018), 74.

Digital Library

[128]

E. Sit, A. Haeberlen, F. Dabek, B.-G. Chun, H. Weatherspoon, R. Morris, M. F. Kaashoek, and J. Kubiatowicz. 2006. Proactive replication for data durability. In Proceedings of the International Workshop on Peer-to-Peer Systems (IPTPS’06).

[129]

A. Solanas, C. Patsakis, M. Conti, I. S. Vlachos, V. Ramos, F. Falcone, O. Postolache, P. A. Perez-martinez, R. D. Pietro, D. N. Perrea, and A. Martinez-Balleste. 2014. Smart health: A context-aware health paradigm within smart cities. IEEE Commun. Mag. 52, 8 (Aug. 2014), 74--81.

[130]

Yunsheng Song, Jiye Liang, Jing Lu, and Xingwang Zhao. 2017. An efficient instance selection algorithm for k-nearest neighbor regression. Neurocomputing 251 (2017), 26--34.

Digital Library

[131]

T. Soyata. 2018. GPU Parallel Program Development Using CUDA. Taylor 8 Francis.

[132]

Apache Spark. 2017. Apache Spark—Lightening-Fast Cluster Computing. Retrieved from http://spark.apache.org/.

[133]

I. Stojmenovic. 2014. Fog computing: A cloud to the ground support for smart things and machine-to-machine networks. In Proceedings of the Australian Telecommunication Networks and Applications Conference (ATNAC’14). IEEE, 117--122.

[134]

J. Stößer, D. Neumann, and C. Weinhardt. 2010. Market-based pricing in grids: On strategic manipulation and computational cost. Eur. J. Operat. Res. 203, 2 (2010), 464--475.

[135]

M. Taneja and A. Davy. 2016. Poster abstract: Resource aware placement of data stream analytics operators on fog infrastructure for internet of things applications. In Proceedings of the 2016 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, 113--114.

[136]

Ben Taylor, Vicent Sanz Marco, Willy Wolff, Yehia Elkhatib, and Zheng Wang. 2018. Adaptive deep learning model selection on embedded systems. SIGPLAN Not. 53, 6 (Jun. 2018), 31--43.

Digital Library

[137]

M. R. Thomas. 2002. A GIS-based decision support system for brownfield redevelopment. Landsc. Urban Plan. 58, 1 (2002), 7--23.

[138]

L. Valerio, A. Passarella, and M. Conti. 2016. Hypothesis transfer learning for efficient data computing in smart cities environments. In Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP’16). IEEE, 1--8.

[139]

P. M. Varela and T. Otsuki Ohtsuki. 2016. Discovering co-located walking groups of people using iBeacon technology. IEEE Access 4 (2016), 6591--6601.

[140]

F. Viani, A. Polo, P. Garofalo, N. Anselmi, M. Salucci, and E. Giarola. 2017. Evolutionary optimization applied to wireless smart lighting in energy-efficient museums. IEEE Sens. J. 17, 5 (Mar. 2017), 1213--1214.

[141]

F. J. Villanueva, C. Aguirre, D. Villa, M. J. Santofimia, and J. C. López. 2014. Smart city data stream visualization using glyphs. In Proceedings of the 2014 8th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. IEEE, 399--403.

Digital Library

[142]

Jaime Vitola, Francesc Pozo, Diego A. Tibaduiza, and Maribel Anaya. 2017. A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications. Sensors 17, 2 (2017).

[143]

Petra Vrablecová, Anna Bou Ezzeddine, Viera Rozinajová, Slavomír Šárik, and Arun Kumar Sangaiah. 2017. Smart grid load forecasting using online support vector regression. Comput. Electr. Eng. 65 (2017), 102--117.

[144]

L. Wang, Z. Zhang, J. Xu, and R. Liu. 2018. Wind turbine blade breakage monitoring with deep autoencoders. IEEE Transactions on Smart Grid 9, 4 (July 2018), 2824--2833.

[145]

M. Wang, S. Yang, Y. Sun, and J. Gao. 2016. Predicting human mobility from region functions. In Proceedings of the IEEE International Conference on Green Computing and Communications (GreenCom’16). IEEE, 540--547.

[146]

Y. Wang, S. Ram, F. Currim, E. Dantas, and L. A. Sabóia. 2016. A big data approach for smart transportation management on bus network. In Proceedings of the IEEE International Smart Cities Conference (ISC2’16). IEEE, 1--6.

[147]

R. A. Waraich, M. D. Galus, C. Dobler, M. Balmer, G. Andersson, and K. W. Axhausen. 2013. Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation. Transport. Res. C: Emerg. Technol. 28, Supplement C (2013), 74--86.

[148]

F. Wu, C. Wen, Y. Guo, J. Wang, Y. Yu, C. Wang, and J. Li. 2017. Rapid localization and extraction of street light poles in mobile lidar point clouds: A supervoxel-based approach. IEEE Trans. Intell. Transport. Syst. 18, 2 (Feb. 2017), 292--305.

[149]

G. Wu, J. Chen, W. Bao, X. Zhu, W. Xiao, and J. Wang. 2017. Towards collaborative storage scheduling using alternating direction method of multipliers for mobile edge cloud. J. Syst. Softw. 134, Suppl. C (2017), 29--43.

Digital Library

[150]

W. Wu and M. Peng. 2017. A data mining approach combining k-means clustering with bagging neural network for short-term wind power forecasting. IEEE IoT J. 4, 4 (Aug. 2017), 979--986.

[151]

S. Yi, C. Li, and Q. Li. 2015. A survey of fog computing: Concepts, applications and issues. In Proceedings of the 2015 Workshop on Mobile Big Data. ACM, 37--42.

Digital Library

[152]

J. Yin, I. Gorton, and S. Poorva. 2012. Toward real time data analysis for smart grids. In Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis. IEEE, 827--832.

Digital Library

[153]

Ruiyun Yu, Yu Yang, Leyou Yang, Guangjie Han, and Oguti Ann Move. 2016. RAQ—A random forest approach for predicting air quality in urban sensing systems. Sensors 16, 1 (2016).

[154]

X. Zhang, Z. Yang, W. Sun, Y. Liu, S. Tang, K. Xing, and X. Mao. 2016. Incentives for mobile crowd sensing: A survey. IEEE Commun. Surv. Tutor. 18, 1 (2016), 54--67.

Digital Library

[155]

Y. D. Zhang, Z. J. Yang, H. M. Lu, X. X. Zhou, P. Phillips, Q. M. Liu, and S. H. Wang. 2016. Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4 (2016), 8375--8385.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 2

March 2020

770 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3320149

Editor:
Sartaj Sahni
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Published: 30 May 2019

Accepted: 01 January 2019

Revised: 01 January 2019

Received: 01 March 2018

Published in CSUR Volume 52, Issue 2

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