Machine Learning Paradigm for Internet of Things Applications. Группа авторов
Читать онлайн книгу.
When it comes to designing the cities of the future, “smart cities” is the new term. To bring a new brand and distinctive appeal to the lifestyle, smart cities are supposed to be the cornerstone to balancing a prosperous future with sustained economic development and job production.
Cloud-based output and storage face common obstacles to smart city applications. For example, the complexities of cloud-based smart grids include cost-effective provisioning without replacing ageing infrastructure and stable integration of modern capabilities with existing networks [33]. Although the ML has both added advantage and disadvantages, it leads to the deep learning process which enhances development in the technologies like the data visualization in real-time modern world.
Development accomplished by cities is tied to their desire to holistically solve urbanization-based problems and related social, environmental, and economic issues, while at the same time making the most of potential opportunities [30]. It is possible to interpret the smart city idea as a paradigm for incorporating this vision of advanced and modern urbanization. In future, vision is the urban center of the future, making sustainable, safe, eco-friendly, and competitive as all buildings are designed, built, and controlled using new, manufactured materials, sensors, electronics, and networks integrated with computerized systems consisting of databases, surveillance, and de-connected networks.
1.12.4 Conclusion
To make civic processes more cost-effective and environmentally competitive, smart cities make use of digital technologies. By turning streetlights on only when a road is in service, sensors installed in buildings and grid networks will help communities embrace green technologies and conserve electricity [32]. Sensors, smart cards, and digital cameras feed real-time data into advanced control systems, and better infrastructure and analytical technologies will enable decision-making. Rapid urbanization has contributed to extreme road jams as large numbers of citizens choose to enter cities by vehicle. As a result, air pollution has been a major challenge for cities. Development in smart cities has led to the introduction of creative integrated transport networks designed to satisfy the needs of residents. For starters, able to implement real-time mobility systems, smart travel passes, shared car trips, smart vehicles (driverless cars), and personal rapid transit.
One of the largest smart city projects currently taking place is the India Smart Cities Competition, a contest where 100 cities can receive funds from the Ministry of Urban Development and Bloomberg Philanthropies. Competition is meant to promote more innovation from municipal officers and their partners, as well as more involvement from people, in the development of smart city plans [31, 32]. As several critics find out that critical needs such as drinking water and sanitation need to be resolved, this problem has been scrutinized.
Using technologies and data improves resistance to urban problems, through greater efficiencies, and using creativity and industry introduces these fantastic opportunities for our communities. Those with good leadership and productive public-private collaborations working with community participation are the cities positioned to build on these possibilities.
References
1. Su, K., Li, J., Fu, H., Smart city and the applications. International Conference on Electronics, Communications and Control (ICECC), Ningbo, China, 2011.
2. Fan, L., Boshnakov, K., Lv, J., Integrated control for the urban wastewater treatment. BHUBANESWAR SMART CITY PROPOSAL, 2015, smartcities. gov.in.
3. Lavalle, A.G., Serafim, L., de Oliveira, O.P., Hordijk, M., Takano, G., Sridharan, N., Participatory Governance, Inclusive Development and Decentralization in the Global South, Literature Review, pmc.gov.in, 2011.
4. Janawani, Paritcipatory Governance in Pune and berlin, Janwani, 2013. www.janwani.org
5. Keruwala, N., Participatory Budgeting in India: The Pune experiment, OWSA, 2016. developmentcentral.wordpress.com
6. Smart Cities Mission Ministry of Urban Development Government of India hosted, 2015, http://mohua.gov.in/cms/smart-cities.php
7. Anthopoulos, L., Janssen, M., Weerakkody, V., Comparing Smart Cities with Different Modelling Approaches. International World Wide Web Conference Committee (IW3C2), 2015.
8. Harekrishna, M., How Relevant is E-Governance Measurement? Experiences in Indian Scenario EGOSE, Conference on Electronic Governance and Open Society: Challenges in Eurasia(EGOSE'14), DOI:10.13140/RG.2.1.4930.9282 2014.
9. Desa, U., United nations department of economic and social affairs population division: World urbanization prospects, 2014, https://population.un.org/wup/
10. Neirotti, P., De Marco, A., Cagliano, A.C., Mangano, G., Scorrano, F., Current trends in smart city initiatives: Some stylised facts, Cities, vol. 38, pp. 25–36, Elsevier, 2014.
11. Shapiro, J.M., Smart cities: quality of life productivity and the growth effects of human capital. Rev. Econ. Stat., 88, 2, 324–335, 2006.
12. Torres, Pina, V., Acerete, B., E-government developments on delivering public services among eu cities. Gov. Inf. Q., 22, 2, 217–238, 2005.
13. Yovanof, G.S. and Hazapis, G.N., An architectural framework and enabling wireless technologies for digital cities & intelligent urban environments. Wirel. Pers. Commun., 49, 3, 445–463, 2009.
14. Brännström, R. and Granlund, D., Sensor monitoring of bridge movement: a system architecture. Proceedings of Local Computer Networks, IEEE Communications Society, pp. 797–800, 2011.
15. Zhang, P., Yan, M., Wang, H., Yu, G., Integration Automation System of Wastewater Treatment Plant. 6th World Congress on Intelligent Control and Automation Dalian, pp. 6626–6630, 2006.
16. Humoreanu, B. and Nascu, I., Wastewater treatment plant SCADA application. IEEE International Conference on Automation Quality and Testing Robotics, pp. 575–580, 2012.
17. Grieu, S., Traore, A., Polit, M., Fault detection in a wastewater treatment plant. 8th International Conference on Emerging Technologies and Factory Automation, vol. 1, pp. 399–402, 2001.
18. Stephens, J.C., Wilson, E.J., Peterson, T.R., Smart Grid Evolution, Cambridge University Press, Smart Grid and Renewable Energy, Vol. 6 No. 8, August 31, 2015.
19. Paul, A., Cleverley, M., Kerr, W., Marzolini, F., Reade, M., Russo, S., Smarter Cities Series: understanding the IBM approach to public safety, IBM Corporation, www.aeiciberseguridad.es/descargas/categoria6/6193257.pdf, 2011.
20. Achaerandio, R., Gallotti, G., Curto, J., Bigliani, R., Maldonado, F., Smart cities analysis in Spain, White paper, IDC, 2011.
21. Bria, F., Gascó, M., Halpin, H., Baeck, P., Almirall, E., Kresin, F., Growing a digital social innovation ecosystem for Europe, DSI final report, European Union, 2015. https://media.nesta.org.uk/documents/dsireport.pdf
22. Hernández-Munoz, J.M. et al., Smart Cities at the Forefront of the Future Internet, in: The Future Internet, Lecture Notes in Computer Science, vol. 6656, pp. 447–462, 2011.
23. Fan, L., Boshnakov, K., Lv, J., System Based on Multivariable Regulator. 29th Chinese Control Conference, pp. 4975–4980, 2010.
24. Kumar, A., Maurya, B., Chandra, D., Dimension reduction and controller design for a waste water treatment plant. International Conference on Power and Advanced Control Engineering (ICPACE), pp. 413–417, 2015.
25.