Internet of Things and data mining: From applications to techniques and systems

Advanced Review

Mohamed Medhat Gaber, Adel Aneiba, Shadi Basurra, Oliver Batty, Ahmed M. Elmisery, Yevgeniya Kovalchuk, Muhammad Habib Ur Rehman

Published Online: Nov 09 2018

DOI: 10.1002/widm.1292

Full article on Wiley Online Library: HTML PDF

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The Internet of Things (IoT) is the result of the convergence of sensing, computing, and networking technologies, allowing devices of varying sizes and computational capabilities (things) to intercommunicate. This communication can be achieved locally enabling what is known as edge and fog computing, or through the well‐established Internet infrastructure, exploiting the computational resources in the cloud. The IoT paradigm enables a new breed of applications in various areas including health care, energy management and smart cities. This paper starts off with reviewing these applications and their potential benefits. Challenges facing the realization of such applications are then discussed. The sheer amount of data stemmed from devices forming the IoT requires new data mining systems and techniques that are discussed and categorized later in this paper. Finally, the paper is concluded with future research directions. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Application Areas > Health Care Application Areas > Industry Specific Applications

Using IoT and data mining, energy generated from renewable sources can be distributed fairly; energy can be stored and shared through utilization of private energy storage systems (batteries) in houses

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Categorization of data mining techniques for Internet of Things

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Internet of Things networks connectivity options

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Using Internet of Things to predict human behavior in buildings for optimal energy management control leading to energy savings in buildings

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References

Abdallah,, F., Basurra,, S., & Gaber,, M. (2017). A hybrid agent‐based and probabilistic model for fine‐grained behavioural energy waste simulation. In 29 IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Boston. Piscataway, NJ: IEEE.
Abhishek,, P. V., Manjunatha,, H. G., Sudarshan,, P. B., & Reddy,, K. P. V. (2016). IoT operated wheel chair. International Journal of Engineering Research, 5(4), 1089–1091. https://doi.org/10.5958/2319-6890
Abouzakhar,, N. S., Jones,, A., & Angelopouloui,, O. (2017). Internet of things security: A review of risks and threats to healthcare sector. In Proceedings of IEEE International Conference on Internet of Things (pp. 373–378). Piscataway, NJ: IEEE.
Ahmed,, E., Yaqoob,, I., Hashem,, I. A. T., Khan,, I., Ahmed,, A. I. A., Imran,, M., & Vasilakos,, A. V. (2017). The role of big data analytics in internet of things. Computer Networks, 129, 459–471.
Ahmed,, M. A., Kang,, Y. C., & Kim,, Y.‐C. (2015). Communication network architectures for smart‐house with renewable energy resources. Energies, 8(8), 8716–8735. https://doi.org/10.3390/en8088716
Alam,, F., Mehmood,, R., Katib,, I., & Albeshri,, A. (2016). Analysis of eight data mining algorithms for smarter internet of things (IoT). Procedia Computer Science, 98, 437–442.
Alam,, F., Mehmood,, R., Katib,, I., Albogami,, N., & Albeshri,, A. (2017). Data fusion and IoT for smart ubiquitous environments: A survey. Piscataway, NJ: IEEE Access.
Al‐mawee,, W. (2012). Privacy and security issues in IoT healthcare applications for the disabled users a survey (Unpublished doctoral dissertation). Western Michigan University, Michigan.
Al‐Sarawi,, S., Anbar,, M., Alieyan,, K., & Alzubaidi,, M. (2017). Internet of things (IoT) communication protocols. In 2017 8th International Conference on Information Technology (ICIT) (pp. 685–690). Piscataway, NJ: IEEE.
Alturki,, B., Reiff‐Marganiec,, S., & Perera,, C. (2017). A hybrid approach for data analytics for internet of things. arXiv preprint arXiv:1708.06441.
Amento,, B., Balasubramanian,, B., Hall,, R. J., Joshi,, K., Jung,, G., & Purdy,, K. H. (2016). Focusstack: Orchestrating edge clouds using location‐based focus of attention. In IEEE/ACM Symposium on Edge Computing (SEC) (pp. 179–191). Piscataway, NJ: IEEE.
Appavoo,, P., Chan,, M. C., Bhojan,, A., & Chang,, E. C. (2016). Efficient and privacy‐preserving access to sensor data for internet of things (IoT) based services. In 2016 8th International Conference on Communication Systems and Networks (COMSNETS) (pp. 1–8). Piscataway, NJ: IEEE.
Arfat,, Y., Aqib,, M., Mehmood,, R., Albeshri,, A., Katib,, I., Albogami,, N., & Alzahrani,, A. (2017). Enabling smarter societies through mobile big data fogs and clouds. Procedia Computer Science, 109, 1128–1133.
Augustin,, A., Yi,, J., Clausen,, T., & Townsley,, W. M. (2016). A study of LoRa: Long range %26 low power networks for the internet of things. Sensors, 16(9), 1466.
Baktir,, A. C., Ozgovde,, A., & Ersoy,, C. (2017). How can edge computing benefit from software‐defined networking: A survey, use cases, and future directions. IEEE Communications Surveys %26 Tutorials, 19(4), 2359–2391.
Bardyn,, J.‐P., Melly,, T., Seller,, O., & Sornin,, N. (2016). IoT: The era of LPWAN is starting now. In ESSCIRC Conference 2016: 42nd European Solid‐state Circuits Conference (pp. 25–30). Piscataway, NJ: IEEE.
Basurra,, S., & Jankovic,, L. (2016). Performance comparison between KNN and NSGA‐II algorithms as calibration approaches for building simulation models. In N. Hamza, (Ed.), Proceedings of the 3rd IBPSA‐England Conference BSO 2016, Great North Museum, Newcastle (p. 1093). England: International Building Performance Simulation Association (IBPSA).
Battle,, S., & Gaster,, B. (2017). Lorawan Bristol. In Proceedings of the 21st International Database Engineering %26 Applications Symposium (pp. 287–290). New York, NY: ACM.
Bekara,, C. (2014). Security issues and challenges for the IoT‐based smart grid. Procedia Computer Science, 34(Suppl. C), 532–537.
Bouhafs,, F., Mackay,, M., & Merabti,, M. (2012). Links to the future: Communication requirements and challenges in the smart grid. IEEE Power and Energy Magazine, 10(1), 24–32. https://doi.org/10.1109/MPE.2011.943134
Bretzke,, W.‐R. (2013). Global urbanization: A major challenge for logistics. Logistics Research, 6(2–3), 57–62.
Bröring,, A., Schmid,, S., Schindhelm,, C.‐K., Khelil,, A., Kabisch,, S., Kramer,, D., … Teniente,, E. (2017). Enabling IoT ecosystems through platform interoperability. IEEE Software, 34(1), 54–61.
Cao,, H., Wachowicz,, M., & Cha,, S. (2017). Developing an edge analytics platform for analyzing real‐time transit data streams. arXiv preprint arXiv:1705.08449.
Chiang,, M., & Zhang,, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), 854–864.
Cognizant. (2016). How the internet of things is transforming medical devices. Retrieved from https://www.cognizant.com/whitepapers/how-the-internet-of-things-is-transforming-medical-devices-codex1945.pdf
Connell,, A., Montgomery,, H., Morris,, S., Nightingale,, C., Stanley,, S., Emerson,, M., … Laing,, C. (2017). Service evaluation of the implementation of a digitally‐enabled care pathway for the recognition and management of acute kidney injury. F1000Research, 1(1), 47–65. https://doi.org/10.12688/f1000research.11637.1
Corchado,, J. M., Bajo,, J., Tapia,, D. I., & Abraham,, A. (2010). Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare. IEEE Transactions on Information Technology in Biomedicine, 14(2), 234–240.
Dama,, S., Sathya,, V., Kuchi,, K., & Pasca,, T. V. (2017). A feasible cellular internet of things: Enabling edge computing and the IoT in dense futuristic cellular networks. IEEE Consumer Electronics Magazine, 6(1), 66–72.
Dastjerdi,, A. V., Gupta,, H., Calheiros,, R. N., Ghosh,, S. K., & Buyya,, R. (2016). Fog computing: Principles, architectures, and applications. arXiv preprint arXiv:1601.02752.
Deepmind. (2017). Deepmind health independent review panel annual re‐port. Retrieved from https://deepmind.com/documents/85/DeepMind%20Health%20Independent%20Review%20Annual%20Report%202017.pdf
Department for Business, Energy %26 Industrial Strategy. (2013). Smart meters: A guide. Retrieved from https://www.gov.uk/guidance/smart-meters-how-they-work
Dubey,, H., Yang,, J., Constant,, N., Amiri,, A. M., Yang,, Q., & Makodiya,, K. (2015). Fog data: Enhancing telehealth big data through fog computing. In Proceedings of the ASE bigdata %26 socialinformatics 2015 (p. 14). New York, NY: ACM.
Elmisery,, S. R., Ahmed,, M., & Aborizka,, M. (2017). A new computing environment for collective privacy protection from constrained healthcare devices to IoT cloud services. Cluster Computing, 1–28. https://doi.org/10.1007/s10586-017-1298-1
Elmisery,, S. R., Ahmed,, M., & Botvich,, D. (2016). A fog based middleware for automated compliance with OECD privacy principles in internet of healthcare things. IEEE Access, 4, 8418–8441. https://doi.org/10.1109/ACCESS.2016.2631546
Erlinghagen,, S., Lichtensteiger,, B., & Markard,, J. (2015). Smart meter communication standards in Europe: A comparison. Renewable and Sustainable Energy Reviews, 43(Suppl. C), 1249–1262. https://doi.org/10.1016/j.rser.2014.11.065
European Commission (2010). European commission directive 2010/31/EU on 19 May 2010 on the energy performance of buildings (Tech. Rep. No. 18). Author.
Evans,, D., & Eyers,, D. M. (2012). Efficient data tagging for managing privacy in the Internet of Things. Piscataway, NJ: IEEE.
Farahmandpour,, Z., Versteeg,, S., Han,, J., & Kameswaran,, A. (2017). Service virtualisation of internet‐of‐things devices: Techniques and challenges. In Proceedings of the 3rd international workshop on rapid continuous software engineering (pp. 32–35). Piscataway, NJ: IEEE.
Flore,, D. (2016). 3gpp standards for the internet‐of‐things. Recuperado el, 25.
Fong,, E., & Chung,, W. (2013). Mobile cloud‐computing‐based healthcare service by noncontact ECG monitoring. Sensors, 13(1), 678–708. https://doi.org/10.3390/s130100001
G3ict. (2015). Internet of things: New promises for persons with disabilitie. Retrieved from http://g3ict.org/download/p/fileId1025/productId335
Gaber,, M. (2009). Data stream mining using granularity‐based approach. Foundations of Computational, Intelligence, 6, 47–66.
Gaber,, M., Krishnaswamy,, S., & Zaslavsky,, A. (2005). On‐board mining of data streams in sensor networks. Advanced Methods for Knowledge Discovery from Complex Data, 307–335. https://link.springer.com/chapter/10.1007/1-84628-284-5_12
Gaber,, M. M. (2012). Advances in data stream mining. WIREs Data Mining and Knowledge Discovery, 2(1), 79–85.
Gaber,, M. M., Gomes,, J. B., & Stahl,, F. (2014). Pocket data mining: Big Data on Small Devices. Series: Studies in Big Data. Cham, Switzerland: Springer.
Gaber,, M. M., & Philip,, S. Y. (2006). A holistic approach for resource‐aware adaptive data stream mining. New Generation Computing, 25(1), 95–115.
Gaber,, M. M., & Yu,, P. S. (2006). A framework for resource‐aware knowledge discovery in data streams: A holistic approach with its application to clustering. In Proceedings of the 2006 acm symposium on applied computing (pp. 649–656). New York, NY: ACM.
Gama,, J., & Gaber,, M. M. (2007). Learning from data streams: Processing techniques in sensor networks. Berlin, Heidelberg: Springer‐Verlag.
García,, J. M., Fernández,, P., Ruiz‐Cortes,, A., Dustdar,, S., & Toro,, M. (2017). Edge and cloud pricing for the sharing economy. IEEE Internet Computing, 21(2), 78–84.
Garcia Lopez,, P., Montresor,, A., Epema,, D., Datta,, A., Higashino,, T., Iamnitchi,, A., … Riviere,, E. (2015). Edge‐centric computing: Vision and challenges. ACM SIGCOMM Computer Communication Review, 45(5), 37–42.
Ge,, M., Hong,, J. B., Guttmann,, W., & Kim,, D. S. (2017). A framework for automating security analysis of the internet of things. Journal of Network and Computer Applications, 83, 12–27.
Ghosh,, R., & Simmhan,, Y. (2016). Distributed scheduling of event analytics across edge and cloud. arXiv preprint arXiv:1608.01537.
Gubbi,, J., Buyya,, R., Marusic,, S., & Palaniswami,, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
Gungor,, V. C., Sahin,, D., Kocak,, T., Ergut,, S., Buccella,, C., Cecati,, C., & Hancke,, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529–539. https://doi.org/10.1109/TII.2011.2166794
Gupta,, H., Vahid Dastjerdi,, A., Ghosh,, S. K., & Buyya,, R. (2017). Ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience, 47(9), 1275–1296.
Hagras,, H., Callaghan,, V., Colley,, M., & Clarke,, G. (2003). A hierarchical fuzzygenetic multiagent architecture for intelligent buildings online learning, adaptation and control. Information Sciences, 150(1), 33–57. https://doi.org/10.1016/S0020-0255(02)00368-7
Harth,, N., Anagnostopoulos,, C., & Pezaros,, D. (2017). Predictive intelligence to the edge: Impact on edge analytics. Evolving Systems, 1–24. https://link.springer.com/article/10.1007/s12530‐017‐9190‐z
Heintz,, B., Chandra,, A., & Sitaraman,, R. K. (2016). Trading timeliness and accuracy in geo‐distributed streaming analytics. In SOCC (pp. 361–373). Valencia, CA: American Scientific.
Hii,, P., Lee,, S., Kwon,, T., & Chung,, W. (2011). Smart phone based patient‐centered remote health monitoring application in wireless sensor network. Sensor Letters, 9(2), 791–796.
Hong,, J. B., & Kim,, D. S. (2016). Assessing the effectiveness of moving target defenses using security models. IEEE Transactions on Dependable and Secure Computing, 13(2), 163–177.
Hsu,, C.‐W., & Yeh,, C.‐C. (2017). Understanding the factors affecting the adoption of the internet of things. Technology Analysis %26 Strategic Management, 29(9), 1089–1102.
Hu,, Y. C., Patel,, M., Sabella,, D., Sprecher,, N., & Young,, V. (2015). Mobile edge computinga key technology towards 5g. ETSI White Paper, 11(11), 1–16.
Isa,, N., Yusoff,, M., & Mohamed,, A. (2014). A review on recent traffic congestion relief approaches. In 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology (pp. 121–126). Piscataway, NJ: IEEE. https://doi.org/10.1109/ICAIET.2014.29
Islam,, S., Kwak,, D., Kabir,, H., Hossain,, M., & Kwak,, K. (2015). The internet of things for health care: A comprehensive survey. IEEE Access, 3(1), 678–708. https://doi.org/10.1109/access.2015.2437951
Istepanian,, R. S. H., Hu,, S., Philip,, N. Y., & Sungoor,, A. (2011). The potential of internet of m‐health things m‐IoT for non‐invasive glucose level sensing. In Proceedings IEEE Annual International Conference on Engineering in Medicine and Biology Society (EMBC) (pp. 5264–5266). Piscataway, NJ: IEEE.
Jabbarpour,, M. R., Nabaei,, A., & Zarrabi,, H. (2016). Intelligent guardrails: An IoT application for vehicle traffic congestion reduction in smart city. In 2016 I.E. International Conference on Internet of Things (Ithings) and IEEE Green Computing and Communications (greencom) and IEEE Cyber, Physical and Social Computing (cpscom) and IEEE Smart Data (Smartdata) (pp. 7–13). Piscataway, NJ: IEEE. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.29
Jara,, A. J., Zamora‐Izquierdo,, M. A., & Skarmeta,, A. F. (2013). Interconnection framework for mhealth and remote monitoring based on the internet of things. IEEE Journal on Selected Areas in Communications, 31(9), 47–65. https://doi.org/10.1109/JSAC.2013.SUP.0513005
Jian,, Z., Zhanli,, W., & Zhuang,, M. (2012). Temperature measurement system and method based on home gateway. Google Patents CN Patent App. CN 201,110,148,247. Retrieved from https://www.google.com/patents/CN102811185A?cl=en
Jin,, P. J., Zhang,, G., Walton,, C. M., Jiang,, X., & Singh,, A. (2013). Analyzing the impact of false‐accident cyber attacks on traffic ow stability in connected vehicle environment. In In 2013 International Conference on Connected Vehicles and Expo (ICCVE) (pp. 616–621). Piscataway, NJ: IEEE. https://doi.org/10.1109/ICCVE.2013.6799866
Kim,, H.‐Y., & Kim,, J.‐M. (2017). A load balancing scheme based on deep‐learning in IoT. Cluster Computing, 20(1), 873–878.
Konstantinidis,, E. I., Bamparapoulos,, G., Billis,, A., & Bamidis,, P. D. (2015). Internet of things for an age‐friendly healthcare. Digital Healthcare Empowering Europeans, 210(1), 587–591. https://doi.org/10.3233/978-1-61499-512-8-587
Kortuem,, G., Kawsar,, F., Sundramoorthy,, V., & Fitton,, D. (2010). Smart objects as building blocks for the internet of things. IEEE Internet Computing, 14(1), 44–51.
Lane,, N. D., Bhattacharya,, S., Georgiev,, P., Forlivesi,, C., & Kawsar,, F. (2016). Accelerated deep learning inference for embedded and wearable devices using deepx. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion (pp. 109–109). New York, NY: ACM.
Larson,, E. C., Goel,, M., Boriello,, G., Heltshe,, S., Rosenfeld,, M., & Patel,, S. N. (2011). Spirosmart: Using a microphone to measure lung function on a mobile phone. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (pp. 280–289). New York, NY: ACM.
Lee,, E.‐K. (2016a, 2016). Advancing building energy management system to enable smart grid interoperation. International Journal of Distributed Sensor Networks, 12, 1:1. https://doi.org/10.1155/2016/3295346
Lee,, H. (2016b). The internet of things and assistive technologies for people with disabilities: Applications, trends, and issues. In Internet of things and advanced application in healthcare (pp. 32–65). Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-5225-1820-4.ch002. Retrieved from https://www.igi-global.com/chapter/the-internet-of-things-and-assistive-technologies-for-people-with-disabilities/170236
Li,, Y., & Li,, M. (2017). A privacy protection mechanism for numerical control information in internet of things. International Journal of Distributed Sensor Networks, 13(8). https://doi.org/10.1177/1550147717726312
Liang,, B. (2017). Mobile edge computing. Cambridge, England: Cambridge University Press.
Loai,, A. T., Mehmood,, R., Benkhlifa,, E., & Song,, H. (2016). Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access, 4, 6171–6180.
Manyika,, J., Chui,, M., Bisson,, P., Woetzel,, J., Dobbs,, R., Bughin,, J., & Aharon,, D. (2015). The internet of things: Mapping the value beyond the hype. New York City, NY: McKinsey Global Institute. Retrieved from https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/The%20Internet%20of%20Things%20The%20value%20of%20digitizing%20the%20physical%20world/The-Internet-of-things-Mapping-the-value-beyond-the-hype.ashx
Mao,, Y., You,, C., Zhang,, J., Huang,, K., & Letaief,, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys %26 Tutorials., 19, 2322–2358.
Marchiori,, A. (2017). Maximizing coverage in low‐power wide‐area IoT networks. In 2017 IEEE International Conference on Pervasive computing and Communications Workshops (Percom Workshops) (pp. 467–472). Piscataway, NJ: IEEE.
Margelis,, G., Piechocki,, R., Kaleshi,, D., & Thomas,, P. (2015). Low throughput networks for the IoT: Lessons learned from industrial implementations. In 2015 IEEE 2nd world forum on Internet of things (WF‐IoT) (pp. 181–186). Piscataway, NJ: IEEE.
Martin,, A. B., Kelly,, D., & Palmatier,, R. W. (2017). Data privacy: Effects on customer and firm performance. Journal of Marketing, 81(1), 36–58.
Masek,, P., Masek,, J., Frantik,, P., Fujdiak,, R., Ometov,, A., Hosek,, J., … Misurec,, J. (2016). A harmonized perspective on transportation management in smart cities: The novel IoT‐driven environment for road traffic modeling. Sensors, 16(11), 1872.
Mehmood,, R., Alam,, F., Albogami,, N. N., Katib,, I., Albeshri,, A., & Altowaijri,, S. M. (2017). Utilearn: A personalised ubiquitous teaching and learning system for smart societies. IEEE Access, 5, 2615–2635.
Mittal,, A. K., & Bhandari,, D. (2013). A novel approach to implement green wave system and detection of stolen vehicles. In 2013 3rd IEEE International Advance Computing Conference (IACC) (pp. 1055–1059). Piscataway, NJ: IEEE. https://doi.org/10.1109/IAdCC.2013.6514372
Mohammed,, J., Thakral,, A., Ocneanu,, A. F., Jones,, C., Lung,, C., & Adler,, A. (2014). Internet of things: Remote patient monitoring using web services and cloud computing. In Proceedings of IEEE International Conference on Internet of Things (iThings) (pp. 256–263). Piscataway, NJ: IEEE.
Montero,, D., Yannuzzi,, M., Shaw,, A., Jacquin,, L., Pastor,, A., Serral‐Gracià,, R., … Bosco,, F. (2015). Virtualized security at the network edge: A user‐centric approach. IEEE Communications Magazine, 53(4), 176–186.
Moreno,, M. V., Beda,, B., Skarmeta,, A. F., & Zamora,, M. A. (2014). How can we tackle energy efficiency in IoT based smart buildings? Sensors, 10(6), 9582–9614.
Nellore,, K., & Hancke,, G. (2016). Traffic management for emergency vehicle priority based on visual sensing. Sensors, 11(11), 16. https://doi.org/10.3390/s16111892
Niewolny,, D. (2013). How the internet of things is revolutionizing healthcare. Retrieved from https://www.nxp.com/docs/en/white-paper/IOTREVHEALCARWP.pdf
Noreen,, U., Bounceur,, A., & Clavier,, L. (2017). A study of LoRa low power and wide area network technology. In 3rd IEEE International Conference on Advanced Technologies for Signal and Image Processing (ATSIP`2017). Piscataway, NJ: IEEE.
Orsini,, G., Bade,, D., & Lamersdorf,, W. (2015). Computing at the mobile edge: Designing elastic android applications for computation offloading. In 2015 8th Ifp Wireless and Mobile Networking Conference (WMNC) (pp. 112–119). Piscataway, NJ: IEEE.
Otgonbayar,, A., Pervez,, Z., & Dahal,, K. (2016). Toward Anonymizing IoT Data Streams via Partitioning. Piscataway, NJ: IEEE.
Ozyilmaz,, K. R., & Yurdakul,, A. (2017). Integrating low‐power IoT devices to a blockchainbased infrastructure: Work‐in‐progress. In Proceedings of the Thirteenth ACM International Conference on Embedded Software 2017 Companion (pp. 13:1–13:2). New York, NY: ACM.
Patel,, P., Ali,, M. I., & Sheth,, A. (2017). On using the intelligent edge for IoT analytics. IEEE Intelligent Systems, 32(5), 64–69.
Pérez,, S., Rotondi,, D., Pedone,, D., Straniero,, L., Núñez,, M. J., & Gigante,, F. (2017). Towards the CP‐ABE application for privacy‐preserving secure data sharing in IoT contexts. Cham, Switzerland: Springer.
Prez‐Lombard,, L., Ortiz,, J., & Pout,, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398. https://doi.org/10.1016/j.enbuild.2007.03.007
Puustjarvi,, J., & Puustjarvi,, L. (2011). Automating remote monitoring and information therapy: An opportunity to practice telemedicine in developing countries. In Proceedings of 2011 1st‐Africa Conference (pp. 1–9). Piscataway, NJ: IEEE.
Rahmani,, A., Gia,, T., Negash,, B., Anzanpour,, A., Azimi,, I., Jiang,, M., & Liljeberg,, P. (2017). Exploiting smart e‐health gateways at the edge of healthcare internet‐of‐things: A fog computing approach. Future Generation Computer Systems, 78, 641–658.
Rehman,, M. H., Ahmed,, E., Yaqoob,, I., Hashem,, I. A. T., Imran,, M., & Ahmad,, S. (2018). Big data analytics in industrial IoT using a concentric computing model. IEEE Communications Magazine, 56(2), 37–43.
Rehman,, M. H., Batool,, A., Liew,, C. S., & Teh,, Y.‐W. (2017). Execution models for mobile data analytics. IT Professional, 19(3), 24–30.
Rehman,, M. H., Chang,, V., Batool,, A., & Wah,, T. Y. (2016). Big data reduction framework for value creation in sustainable enterprises. International Journal of Information Management, 36(6), 917–928.
Rehman,, M. H., Jayaraman,, P. P., Malik,, S. R., Rehman Khan,, A., & Gaber,, M. M. (2017). Rededge: A novel architecture for big data processing in mobile edge computing environments. Journal of Sensor and Actuator Networks, 6(3), 17.
Rehman,, M. H., Sun,, C., Wah,, T. Y., Iqbal,, A., & Jayaraman,, P. P. (2016). Opportunistic computation offloading in mobile edge cloud computing environments. In 2016 17th IEEE International Conference on Mobile Data Management (MDM) (Vol. 1, pp. 208–213). Piscataway, NJ: IEEE.
Rehman,, M. H., Sun,, L. C., Wah,, T. Y., & Khan,, M. K. (2016). Towards next‐generation heterogeneous mobile data stream mining applications: Opportunities, challenges, and future research directions. Journal of Network and Computer Applications, 79, 1–24.
Rutledge,, R. L., Massey,, A. K., Antón,, A. I., & Swire,, P. (2014). Defining the internet of devices: Privacy and security implications. Atlanta, Georgia: Georgia Institute of Technology.
Sallam,, A. A., & Malik,, O. P. (2011). Scada systems and smart grid vision. In Electric distribution systems (pp. 469–493). Piscataway, NJ: Wiley‐IEEE Press. Retrieved from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5733135
Salman,, O., Elhajj,, I., Kayssi,, A., & Chehab,, A. (2015). Edge computing enabling the internet of things. In 2015 IEEE 2nd World Forum on Internet of Things (wf‐IoT) (pp. 603–608). Piscataway, NJ: IEEE.
Samie,, F., Tsoutsouras,, V., Bauer,, L., Xydis,, S., Soudris,, D., & Henkel,, J. (2016). Computation offloading and resource allocation for low‐power IoT edge devices. In 2016 IEEE 3rd World Forum on Internet of Things (WF‐IoT) (pp. 7–12). Piscataway, NJ: IEEE.
Saravanan,, M., Das,, A., & Iyer,, V. (2017). Smart water grid management using LPWAN IoT technology. In Global Internet of Things Summit (GIOTS), 2017 (pp. 1–6). Piscataway, NJ: IEEE.
Satria,, D., Park,, D., & Jo,, M. (2017). Recovery for overloaded mobile edge computing. Future Generation Computer Systems, 70, 138–147.
Satyanarayanan,, M., Simoens,, P., Xiao,, Y., Pillai,, P., Chen,, Z., Ha,, K., … Amos,, B. (2015). Edge analytics in the internet of things. IEEE Pervasive Computing, 14(2), 24–31.
Sundar,, R., Hebbar,, S., & Golla,, V. (2015, Feb). Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sensors Journal, 15(2), 1109–1113. https://doi.org/10.1109/JSEN.2014.2360288
Tomtom Traffic Index 2017. (2017). Retrieved from http://corporate.tomtom.com/releasedetail.cfm?releaseid=1012517
Tso,, R., Alelaiwi,, A., Mizanur Rahman,, S. M., Wu,, M.‐E., & Shamim Hossain,, M. (2017). Privacy‐preserving data communication through secure multi‐party computation in healthcare sensor cloud. Journal of Signal Processing Systems, 89(1), 51–59.
Varghese,, B., Wang,, N., Barbhuiya,, S., Kilpatrick,, P., & Nikolopoulos,, D. S. (2016). Challenges and opportunities in edge computing. In IEEE International Conference on Smart Cloud (SMARTCLOUD) (pp. 20–26). Piscataway, NJ: IEEE.
Vulimiri,, A., Curino,, C., Godfrey,, B., Karanasos,, K., & Varghese,, G. (2015). Wanalytics: Analytics for a geo‐distributed data‐intensive world. In Conference on Innovative Data Systems Research (CIDR). New York, NY: ACM.
Wang,, F., Hu,, L., Hu,, J., Zhou,, J., & Zhao,, K. (2017). Recent advances in the internet of things: Multiple perspectives. IETE Technical Review, 34(2), 122–132.
Wang,, L., Jiao,, L., Kliazovich,, D., & Bouvry,, P. (2016). Reconciling task assignment and scheduling in mobile edge clouds. In 2016 IEEE 24th International Conference on Network protocols (ICNP) (pp. 1–6). Piscataway, NJ: IEEE.
Xu,, X., Huang,, S., Feagan,, L., Chen,, Y., Qiu,, Y., & Wang,, Y. (2017). Eaaas: Edge analytics as a service. In 2017 IEEE International Conference on Web services (ICWS) (pp. 349–356). Piscataway, NJ: IEEE.
Yang,, S. (2017). IoT stream processing and analytics in the fog. arXiv preprint arXiv:1705.05988.
Yi,, S., Hao,, Z., Zhang,, Q., Zhang,, Q., Shi,, W., & Li,, Q. (2017). Lavea: Latency‐aware video analytics on edge computing platform. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (pp. 2573–2574). Piscataway, NJ: IEEE.
Yi,, S., Li,, C., & Li,, Q. (2015). A survey of fog computing: Concepts, applications and issues. In Proceedings of the 2015 Workshop on Mobile Big Data (pp. 37–42).
Yi,, X., Willemson,, J., & Nait‐Abdesselam,, F. (2013). Privacy‐preserving wireless medical sensor network. Piscataway, NJ: IEEE.
Zavitsas,, K., Kaparias,, I., & Bell,, M. (2010). Conduits, coordination of network descrip‐tors for urban intelligent transport systems (Tech. Rep. No. 11). London: Imperial College.
Zhang,, Y., He,, C.‐Q., Tang,, B.‐J., & Wei,, Y.‐M. (2015). China`s energy consumption in the building sector: A life cycle approach. Energy and Buildings, 94(Suppl. C), 240–251. https://doi.org/10.1016/j.enbuild.2015.03.011
Zhao,, D., McCoy,, A. P., Du,, J., Agee,, P., & Lu,, Y. (2017). Interaction effects of building technology and resident behavior on energy consumption in residential buildings. Energy and Buildings, 134(Suppl. C), 223–233. https://doi.org/10.1016/j.enbuild.2016.10.049

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