Digital Cities Roadmap. Группа авторов

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Digital Cities Roadmap - Группа авторов


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Neural Network Illumination sensor, temperature sensor, door sensors and RFID Energy Efficiency It becomes functional on a knowledge base that stores all information needed to fulfill the goals of energy efficiency and user comfort NA Household appliances Stationary and mobile user interfaces for monitoring and controlling the smart environment NA Wireless power metering plugs, household devices. Designing and evaluating end consumer energy efficient services NA Smart meters, Different types of sensors and actuators Gateway system architecture to support home-automation, energy use management, and smart-grid operations. Classification algorithms such as C4.5 and RIPPER Smart gateway Safety and Security Computer vision platform for security surveillance in smart homes CNN Surveillance cameras Composed of two methods: web camera to detect the Intruder, and GSM technology that sends SMS. NA Web camera and GSM technology Inexpensive, less power consumption NA GSM/GPRS Comfort and Entertainments Deliver the service based on contextaware feature of the user k nearest neighbors’ classifier Environment monitoring sensors Detect the atmospheric changes and predict the indoor air quality Deep learning CO2, fine dust, temperature, humidity, and light quantity seniors Miscellaneous Protects Medical monitoring, green living, and general comfort. Classification regression and clustering algorithms. Wearable sensors SB services in the fields of health and well-being, digital media and entertainment, and sustainability NA Smart floor sensors, assistive robots Control people to control their environment. save resources. Remain mentally and physically active NA Home environmental sensors Context-aware computing services through video tracking and recognition NA Contains myriad devices that work together

      1. Guikema, S. and Gardoni, P., Reliability estimation for networks of reinforced concrete bridges. ASCE J. Infrastruct. Syst., 15, 61–69, 2009.

      2. Kajitani, Y., Okada, N., Tatano, H., Measuring quality of human community life by spatial temporal age group distributions—Case study of recovery process in a disaster-affected region. Nat. Hazards Rev., 6, 1, 41–47, 2005.

      3. Kang, W.H., Song, J., Gardoni, P., Matrix-based system reliability method and applications to bridge networks. Reliab. Eng. Syst. Safe., 93, 1584–93, 2008.

      4. Koliou, M., Van De Lindt, J.W., McAllister, T.P., Ellingwood, B.R., Dillard, M., Cutler, H., State of the research in community resilience: Progress and challenges. Sustain. Resilient Infrastruct., 5, 3, 131–151, 2018.

      5. Lee, Y.-J., Song, J., Gardoni, P., Lim, H.-W., Post-hazard flow capacity of bridge transportation networks considering structural deterioration of bridges. Struct. Infrastruct. Eng., 7, 7, 509–21, 2011.

      6. MacLean, D., Gardoni, P., Murphy, C., Rowell, A. (Eds.), Societal Risk Management of Natural Hazards, Springer, New York, 2016.

      7. Mardfekri, M. and Gardoni, P., Probabilistic demand models and fragility estimates for offshore wind turbine support structures. Eng. Struct., 52, 2013, 478–87, 2013.

      8. Mardfekri, M. and Gardoni, P., Multi-hazard reliability assessment of offshore wind turbines. Wind Energy, 18, 8, 1433–50, 2015.

      9. Mardfekri, M., Gardoni, P., Bisadi, V., Service reliability of offshore wind turbines. Int. J. Sustainable Energy, 34, 7, 468–84, 2015.

      10. Martins, N., Sustainability economics, ontology and the capability approach. Ecol. Econ., 72, 1–4, 2011.

      11. May, P., Organizational and Societal Consequences for Performance-Based Earthquake Engineering, PEER 2001/04, Pacific Earthquake Engineering Research Center, College of Engineering, University of California, Berkeley, Berkeley, CA, 2011.

      12. Chan, M., Estve, D., Escriba, C., Campo, E., A review of smart homes present state and future challenges. Comput. Methods Programs Biomed., 91, 1, 55–81, [Online]. http://www.sciencedirect.com/science/article/pii/S0169260708000436, Jul. 2008.

      13. Alam, M.R., Reaz, M.B.I., Ali, M.A.M., A Review of Smart Homes—Past, Present, and Future. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.), 42, 6, 1190–1203, Nov. 2012.

      14. Lobaccaro, G., Carlucci, S., Lfstrm, E., Lobaccaro, G., Carlucci, S., Lfstrm, E., A Review of Systems and Technologies for Smart Homes and Smart Grids. Energies, 9, 5, 348, [Online] https://www.mdpi.com/1996-1073/9/5/348, May 2016.

      16. Ni, Q., Garca Hernando, A.B., de la Cruz, I.P., The Elderlys Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development. Sensors, 15, 5, 11 312–11 362, [Online] Available: http://www.mdpi.com/1424-8220/15/5/11312, May 2015.

      17. Rashidi, P. and Mihailidis, A., A Survey on Ambient-Assisted Living Tools for Older Adults. IEEE J. Biomed. Health Inform., 17, 3, 579–590, May 2013.

      18. Peetoom, K.K.B., Lexis, M.A.S., Joore, M., Dirksen, C.D., De Witte, L.P., Literature review on monitoring technologies and their outcomes in independently living elderly people, Disability and Rehabilitation. Assist. Technol., 10, 4, 271–294, 2015.

      19. Salih, A. and Abraham, A., A review of ambient intelligence assisted healthcare monitoring. Int. J. Comput. Inf. Syst. Ind. Manage. (IJCISIM), 5, 2013.

      20. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D., Context Aware Computing for The Internet of Things: A Survey. IEEE Commun. Surv. Tut., 16, 1, 414–454, 2014.

      21. Tsai, C.W., Lai, C.F., Chiang, M.C., Yang, L.T., Data Mining for Internet of Things: A Survey. IEEE Commun. Surv. Tut., 16, 1, 77–97, 2014.

      22. Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P.,


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