Abstract
Internet of Things (IoT) is already a reality, with a vast number of Internet connected objects and devices that has exceeded the number of humans on Earth. Nowadays, there is a novel IoT paradigm that is rapidly gaining ground, this is the scenario of modern human-centric smart environments, where people are not passively affected by technology, but actively shape its use and influence. However, for achieving user-centric aware IoT that brings together people and their devices into a sustainable ecosystem, first, it is necessary to deal with the integration of disparate technologies, ensuring trusted communications, managing the huge amount of data and services, and bringing users to an active involvement. In this chapter, we describe such challenges and present the interesting user-centric perspective of IoT. Furthermore, a management platform for smart environments is presented as a proposal to cover these needs, based on a layered architecture using artificial intelligent capabilities to transform raw data into semantically meaningful information used by services. Two real use cases framed in the smart buildings field exemplify the usefulness of this proposal through a real-system implementation called City Explorer. City Explorer is already deployed in several installations of the University of Murcia, where services such as energy efficiency, appliance management, and analysis of the impact of user involvement in the system are being provided at the moment.
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References
Atzori, L., Iera, A., Morabito, G.: The internet of things: A survey. Computer Networks 54(15), 2787–2805 (2010)
Ganti, R.K., Fan, Y., Hui, L.: Mobile crowdsensing: Current state and future challenges. IEEE Communications Magazine 49(11), 32–39 (2011)
SENSEI EU PROJECT, http://www.sensei-project.eu
Bélissent, J.: Getting clever about smart cities: new opportunities require new business models (2010)
Ducatel, K., et al.: Scenarios for ambient intelligence 2010, ISTAG report, European Commission. Institute for Prospective Technological Studies, Seville, ftp://ftp.cordis.lu/pub/ist/docs/istagscenarios2010.pdf (November 2001)
Newell, A.: Unified theories of cognition, vol. 187. Harvard University Press (1994)
Wasserman, S.: Social network analysis: Methods and applications, vol. 8. Cambridge University Press (1994)
ISTAG. Report on revising europe ict strategy. Technical report, European Commission (2009)
Spiliotopoulos, T., Oakley, I.: Applications of Social Network Analysis for User Modeling
Shi, Y., Larson, M., Hanjalic, A.: Towards understanding the challenges facing effective trust-aware recommendation. Recommender Systems and the Social Web, 40 (2010)
Vassileva, J.: Motivating participation in social computing applications: a user modeling perspective. User Modeling and User-Adapted Interaction 22(1-2), 177–201 (2012)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)
Bin, S., Yuan, L., Xiaoyi, W.: Research on data mining models for the internet of things. In: 2010 International Conference on Image Analysis and Signal Processing (IASP). IEEE (2010)
Reilly, D., Taleb-Bendiab, A.: An jini-based infrastructure for networked appliance management and adaptation. In: Proceedings of the 2002 IEEE 5th International Workshop on Networked Appliances, Liverpool. IEEE (2002)
Sarikaya, B., Ohba, Y., Moskowitz, R., Cao, Z., Cragie, R.: Security Bootstrapping Solution for Resource-Constrained Devices. IETF Internet-Draft (2012)
Tschofenig, H., Gilger, J.: A Minimal (Datagram) Transport Layer Security Implementation. IETF Internet-Draft (2012)
Kivinen, T.: Minimal IKEv2, IETF Internet-Draft (2012)
Moskowitz, R.: HIP Diet EXchange (DEX), IETF Internet-Draft (2012)
Zamora-Izquierdo, M.A., Santa, J., Gomez-Skarmeta, A.F.: An Integral and Networked Home Automation Solution for Indoor Ambient Intelligence. IEEE Pervasive Computing 9, 66–77 (2010)
Nieto, I., Botía, J.A., Gómez-Skarmeta, A.F.: Information and hybrid architecture model of the OCP contextual information management system. Journal of Universal Computer Science 12(3), 357–366 (2006)
Centre Europeen de Normalisation: Indoor Environmental Input Parameters for Design and Assesment of Energy Performance of Buildings - Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics. EN 15251 (2006)
Handbook, A. S. H. R. A. E. Fundamentals. American Society of Heating, Refrigerating and Air Conditioning Engineers. Atlanta (2001)
Perez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy and Buildings 40(3), 394–398 (2008)
Moreno-Cano, M.V., Zamora-Izquierdo, M.A., Santa, J., Skarmeta, A.F.: An Indoor Localization System Based on Artificial Neural Networks and Particle Filters Applied to Intelligent Buildings. Neurocomputing 122, 116–125 (2013)
Berglund, L.: Mathematical models for predicting the thermal comfort response of building occupants. ASHRAE Transactions 84(1), 1848–1858 (1978)
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Moreno-Cano, M.V., Santa, J., Zamora-Izquierdo, M.A., Skarmeta, A.F. (2015). Future Human-Centric Smart Environments. In: Xhafa, F., Barolli, L., Barolli, A., Papajorgji, P. (eds) Modeling and Processing for Next-Generation Big-Data Technologies. Modeling and Optimization in Science and Technologies, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-09177-8_14
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DOI: https://doi.org/10.1007/978-3-319-09177-8_14
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