Skip to main content
SpringerLink
Log in

Urban Computing: The Technological Framework for Smart Cities

  • Living reference work entry
  • First Online:
Handbook of Smart Cities

Abstract

Increased urbanization is putting a strain on the limited shared urban resources, for example, road space, energy, and clean air and water. Smart cities leverage technology to manage such shared resources more efficiently, thereby improving citizens’ quality of life. This chapter introduces and discusses technical challenges in managing city-scale resource infrastructures and potential solutions. We frame the discussion within the Sense-Analyze-Actuate paradigm, a model leveraged by most smart city solutions. The Sense step entails gathering data from existing or newly deployed dedicated sensors, owned by public agencies and businesses, as well as contributed by citizens. In the Analyze step, these disparate and often unreliable sources of data are fused to improve the authenticity of data (improving detection, confidence, reliability, and reducing ambiguity) as well as extending its spatial and temporal coverage. In this step, optimization techniques, and artificial intelligence in particular, allow to reason on the data, making resource management decisions in a centralized or decentralized approach. In the Actuate step, the results of the analysis can either be presented to human operators, i.e., visualized for decisions support, or used to directly actuate the changes, adapted to the specific urban resource.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Abdullah, M. A., Al Hadhrami, T., Tan, C. W., & Yatim, A. H. (2018). Towards green energy for smart cities: Particle swarm optimization based MPPT approach. IEEE Access.

    Google Scholar 

  • Accenture. Smart cities: How 5G can help municipalities become vibrant smart cities, report 2017.

    Google Scholar 

  • Al-Abbasi, A., Ghosh, A., & Aggarwal, V. (2019). DeepPool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 20, 4714.

    Article  Google Scholar 

  • Alonso, L., Pérez-Oria, J., Al-Hadithi, B. M., & Jiménez, A. (2013). Self-tuning PID controller for autonomous car tracking in urban traffic. In 17th International Conference on System Theory, Control, and Computing (ICSTCC).

    Google Scholar 

  • Andrienko, G., et al. (2010). Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. In Proceedings of the Eurographics/IEEE-VGTC Symposium on Visualization.

    Google Scholar 

  • Asaro, P. M. (2019). AI ethics in predictive policing: From models of threat to an ethics of care. IEEE Technology and Society Magazine, 38(2), 40–53.

    Article  MathSciNet  Google Scholar 

  • Augusto, et al. (2013). Intelligent environments: A manifesto. Human-centric Computing and Information Sciences, 3, 12.

    Article  Google Scholar 

  • Bagchi, M., & White, P. R. (2005). The potential of public transport smart card data. Transport Policy, 12(5), 464–474. https://doi.org/10.1016/j.tranpol.2005.06.008, ISSN 0967-070X.

    Article  Google Scholar 

  • Barns, S. (2018). Smart cities and urban data platforms: Designing interfaces for smart governance. City, Culture and Society, 12, 5–12., ISSN 1877-9166. https://doi.org/10.1016/j.ccs.2017.09.006.

    Article  Google Scholar 

  • Bazzan, A. L. C., Cagaram, D., Scheuermann, B. (2014, December). An evolutionary approach to traffic assignment. In: 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS).

    Google Scholar 

  • Bennati, S., Dusparic, I., Shinde, R., & Jonker, C. M. (2018). Volunteers in the smart city: Comparison of contribution strategies on human-centered measures. Sensors, 18(11), 3707.

    Article  Google Scholar 

  • Borenstein, J., Herkert, J., & Miller, K. (2017). Self-driving cars: Ethical responsibilities of design engineers. IEEE Technology and Society Magazine, 36(2), 67–75.

    Google Scholar 

  • Bueno-Delgado, M., Romero-Gázquez, J., Jiménez, P., & Pavón-Mariño, P. (2019). Optimal path planning for selective waste collection in smart cities. Sensors, 19(9), 1973. https://doi.org/10.3390/s19091973

    Article  Google Scholar 

  • Cardullo, P., & Kitchin, R. (2018). Being a ‘citizen’ in the smart city: Up and down the scaffold of smart citizen participation in Dublin, Ireland. GeoJournal. https://doi.org/10.1007/s10708-018-9845-8.

    Article  Google Scholar 

  • Cavallo, J., Marinescu, A., Dusparic, I., Clarke, S. Evaluation of forecasting methods for very small-scale networks. In ECML/PKDD 2015, 3rd International Workshop on Data Analytics for Renewable Energy Integration (DARE’15).

    Google Scholar 

  • Chon, Y., Kim, S., Lee, S., Kim, D., Kim, Y., & Cha, H. (2014). Sensing WiFi packets in the air: Practicality and implications in urban mobility monitoring. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’14) (pp. 189–200). New York: ACM. https://doi.org/10.1145/2632048.2636066.

  • Colakovic, A., & Hadzialic, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144. https://doi.org/10.1016/j.comnet.2018.07.017.

    Article  Google Scholar 

  • Connolly, M., Dusparic, I., Iosifidis, G., & Bouroche, M. (2018a). Adaptive reward allocation for participatory sensing. Wireless Communications and Mobile Computing, Volume 2018, Article ID 6353425, 15 pages. https://doi.org/10.1155/2018/6353425.

    Article  Google Scholar 

  • Connolly, M., Dusparic, I., & Bouroche, M. (2018b). An identity privacy preserving incentivization scheme for participatory sensing. In International conference on mobile computing and ubiquitous networking (ICMU), Auckland, New Zealand, 2018, pp. 1–6.

    Google Scholar 

  • Crisostomi, E., Shorten, R., & Wirth, F. (2016). Smart cities: A Golden Age for control theory? IEEE Technology and Society Magazine, 35(3), 23–24.

    Google Scholar 

  • Dahiru, A. T. (2015). Fuzzy logic inference applications in road traffic and parking space management. Journal of Software Engineering and Applications, 08(07), 339–345. https://doi.org/10.4236/jsea.2015.87034.

    Article  Google Scholar 

  • D’Andrea, E., & Marcelloni, F. (2017) Detection of traffic congestion and incidents from GPS trace analysis. Expert Systems with Applications, 73, 43–56.

    Google Scholar 

  • Diddigi, RB., Danda, SKR., & Bhatnagar, S. (2017) Multi-agent Q-learning for minimizing demand-supply power deficit in microgrids. CoRR, arXiv preprint arXiv:1708.07732.

    Google Scholar 

  • Dusparic, I., & Cahill, V. (2016, November). Towards autonomic urban traffic control with collaborative multi-policy reinforcement learning. In IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016).

    Google Scholar 

  • Dusparic, I., Taylor, A., Marinescu, A., Cahill, V., & Clarke, S. (2015). Maximizing renewable energy use with decentralized residential demand response. In The first IEEE International Smart Cities Conference.

    Google Scholar 

  • Dusparic, I., Taylor, A., Marinescu, A., Golpayegani, F., & Clarke, S. (2017). Residential demand response: Experimental evaluation and comparison of self-organizing techniques. Renewable and Sustainable Energy Reviews, 80, 1528–1536.

    Article  Google Scholar 

  • European Commission High-Level Expert Group on AI. (2019, April). Ethics guidelines for trustworthy AI. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.

  • Eze, U. F., Emmanuel, I., & Stephen, E. (2014). Fuzzy logic model for traffic congestion. IOSR Journal of Mobile Computing & Application (IOSR-JMCA), 1(1), 15–20. e-ISSN: 2394-0050, P-ISSN: 2394-0042.

    Google Scholar 

  • Ferreira, N., Poco, J., Vo, H. T., Freire, J., & Silva, C. T. (2013). Visual exploration of big spatio-temporal urban data: A study of New York city taxi trips. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2149–2158.

    Article  Google Scholar 

  • Fourtané, S. (2018). The technologies building the smart cities of the future. Interesting Engineering.

    Google Scholar 

  • Gatalsky, P., Andrienko, N., & Andrienko, G. (2004). Interactive analysis of event data using space-time cube. In Proceedings of the 8th International Conference on Information Visualisation, pp. 145–152.)

    Google Scholar 

  • Global forum by Atomium – European Institute for Science, Media and Democracy. AI4People – An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. November 2018.

    Google Scholar 

  • Gueriau, M., & Dusparic, I. (2018, November). SAMoD: Shared autonomous mobility-on-demand using decentralized reinforcement learning. In The 21st IEEE International Conference on Intelligent Transportation Systems.

    Google Scholar 

  • Guibene, W., Nowack, J., Chalikias, N., Fitzgibbon, K., Kelly, M., & Prendergast, D. (2017). Evaluation of LPWAN technologies for smart cities: River monitoring use-case. In 2017 IEEE Wireless communications and networking conference workshops (WCNCW), San Francisco, CA, pp. 1–5. https://doi.org/10.1109/WCNCW.2017.7919089.

  • Haefke, M., Mukhopadhyay, S. C., & Ewald, H. (2011). A Zigbee based smart sensing platform for monitoring environmental parameters. In 2011 IEEE International Instrumentation and Measurement Technology Conference, Binjiang, pp. 1–8. https://doi.org/10.1109/IMTC.2011.5944154

  • Harris, C., Dusparic, I., Galvan-Lopez, E., Marinescu, A., Cahill, V., & Clarke, S. (2014). Set point control for charging of electric vehicles on the distribution network. In Innovative Smart Grid Technologies (ISGT). IEEE PES.

    Google Scholar 

  • Jacob, B., Lanyon-Hogg, R., Nadgir, D. K., & Yassin, A. F. (2004). A practical guide to the IBM autonomic computing toolkit. Raleigh: IBM Redbooks.

    Google Scholar 

  • Jhawar, S., Agarwaal, S., Oberoi, T., Sharma, T., & Thakkar, A. (2018). Application of game theory in water resource management. International Journal or Advance Research and Development, 3(10), 63–68.

    Google Scholar 

  • Kanhere, S. S. (2011). Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces.In 2011 IEEE 12th International Conference on Mobile Data Management, Lulea, pp. 3–6. https://doi.org/10.1109/MDM.2011.16.

  • Karadimas, N. V., Papatzelou, K., & Loumos, V.G. (2007). Genetic algorithms for municipal solid waste collection and routing optimization. Artificial intelligence and innovations 2007: From theory to applications; AIAI 2007.

    Google Scholar 

  • Kitchin, R., & McArdle, G. (2016) Urban data and city dashboards: Six key issues. Working Paper. Programmable City Working Paper 21, Maynooth.

    Google Scholar 

  • Koeman, L. (2017). Urban visualisation: The role of situated technology interventions in facilitating engagement with local topics (Ph.D. thesis, University College London, 2017).

    Google Scholar 

  • Kovacic, M. (2018). Robot cities: Three urban prototypes for future living. The Conversation. http://theconversation.com/robot-cities-three-urban-prototypes-for-future-living-90281.

  • Lauro, F., Moretti, F., Capozzoli, A., & Panzieria, S. (2015). Model predictive control for building active demand respond systems. Energy Procedia, 83, 494–503. ISSN 1876-6102. https://doi.org/10.1016/j.egypro.2015.12.169.

    Article  Google Scholar 

  • Liu, H., Gao, Y., Lu, L., Liu, S., Qu, H., & Ni, L. M. (2011). Visual analysis of route diversity. In Proceedings of the IEEE Conference on Visual Analytics Science and Technology, pp. 171–180.

    Google Scholar 

  • Liu, S., Cui, W., Wu, Y., & Liu, M. (2014). A survey on information visualization: Recent advances and challenges. Springer Visual Computer Journal, 30, 1373. https://doi.org/10.1007/s00371-013-0892-3.

    Article  Google Scholar 

  • Lund, N. S. V., Falk, A. K. V., Borup, M., Madsen, H., & Steen, M. P. (2018). Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management. Critical Reviews in Environmental Science and Technology, 48(3), 279–339.

    Google Scholar 

  • Lutge, C. (2017). The German ethics code for automated and connected driving. Philos. Technol, 30, 547–558. https://doi.org/10.1007/s13347-017-0284-0.

    Article  Google Scholar 

  • Manzoor, A., Bouroche, M., Clarke, S., & Cahill, V. (2012). Trust evaluation for participatory sensing. In 9th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), LNCS, Vol. 120.

    Google Scholar 

  • Manzoor, A., Patsakis, C., Morris, A., McCarthy, J., Mullarkey, G., Pham, H., Clarke, S., Cahill, V., & Bouroche, M. (2014). CityWatch: Exploiting sensor data to manage cities better. Transaction on Emerging Telecommunications Technologies, Special Issue: Smart Cities – Trends & Technologies, 25(1), 64–80, Wiley.

    Article  Google Scholar 

  • Marinescu, D., Čurn, J., Bouroche, M. & Cahill, V., 2012, September. On-ramp traffic merging using cooperative intelligent vehicles: A slot-based approach. In 2012 15th International IEEE Conference on Intelligent Transportation Systems (pp. 900–906). IEEE.

    Google Scholar 

  • Marinescu, A., Harris, C., Dusparic, I., Clarke, S., & Cahill, V. (2013, May). Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods. In 2nd International Workshop on Software Engineering Challenges for the Smart Grid at ICSE.

    Google Scholar 

  • Marinescu, A., Dusparic, I., Harris, C., Cahill, V., Clarke, S. (2014). A dynamic forecasting method for small scale residential electrical demand. In Proceeding of the International Joint Conference on Neural Networks.

    Google Scholar 

  • Marinescu, A., Dusparic, I., Taylor, A., Cahill, V., & Clarke, S. (2017). P-MARL: Prediction-based multi-agent reinforcement learning in inherently non-stationary environments. ACM Transactions on Autonomous and Adaptive Systems (TAAS).

    Google Scholar 

  • Markland, C. A. (1970). Design of optimal and suboptimal stability augmentation systems. AIAA Journal, 8(4), 673–679.

    Article  Google Scholar 

  • Martijn de Waal. (2014). The city as interface: How new media are changing the city. Rotterdam: NAi010 Publishers, ISBN 9789462080508, 208 p.

    Google Scholar 

  • McCluskey, T. L., Vallati, M., & Franco, S. (2017). Automated planning for urban traffic management. In International Joint Conferences on Artificial Intelligence (IJCAI).

    Google Scholar 

  • Mei, H., Poslad, S., & Du, S. (2017). A game-theory based incentive framework for an intelligent traffic system as part of a smart city initiative. Sensors, 17(12), 2874.

    Google Scholar 

  • Mellouk, L., Boulmalf, M., Aaroud, A., Zine-Dine, D., & Benhaddou, D. (2018). Genetic algorithm to solve demand side management and economic dispatch problem. Procedia Computer Science, 130, 611–618.

    Google Scholar 

  • Menouar, H., Guvenc, I., Akkaya, K., Uluagac, A. S., Kadri, A., & Tuncer, A. (2017). UAV-enabled intelligent transportation Systems for the Smart City: Applications and challenges. IEEE Communications Magazine, 55(3), 22–28.

    Article  Google Scholar 

  • Monteil, J., Sau, J., & Bouroche, M. (2016). Adaptive PID feedback control for the longitudinal dynamics of driver-assisted vehicles in mixed traffic. In IEEE 19th International Conference on Intelligent Transportation.

    Google Scholar 

  • Morais, R., Fernandes, M. A., Matos, S. G., Serôdio, C., Ferreira, P. J. S. G., & Reis, M. J. C. S. (2008). A ZigBee multi-powered wireless acquisition device for remote sensing applications in precision viticulture. Computers and Electronics in Agriculture, 62(2), 94–106., , ISSN 0168-1699. https://doi.org/10.1016/j.compag.2007.12.004.

    Article  Google Scholar 

  • Morris, A., Patsakis, C., Bouroche, M., & Cahill, V. (2017). Context dissemination for dynamic urban-scale applications. Springer Mobile Networks and Applications, 22(2), 305–317.

    Article  Google Scholar 

  • Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., & Morris, R. (2011). Smarter cities and their innovation challenges. Computer, 44(6), 32–39. https://doi.org/10.1109/MC.2011.187.

    Article  Google Scholar 

  • Navigant Research. (2018). Smart city platforms, IoT, digital solutions, and data technologies enabling the City as a service: Global market analysis and forecasts. Navigant Research, US.

    Google Scholar 

  • Nha, V. T. N, Djahel, S., & Murphy, J. A. (2012). Comparative study of vehicles’ routing algorithms for route planning in smart cities. In VTM 2012, Satellite Workshop of IFIP Wireless Days 2012.

    Google Scholar 

  • NYC Taxi and Limousine Commission. TLC trip record data. [Online]. http://www.nyc.gov. Accessed 2019.

  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown Publishing Group.

    MATH  Google Scholar 

  • Osborne, C. (2019, July 19). Oakland follows San Francisco’s lead in banning facial recognition tech. ZDNet.

    Google Scholar 

  • Page, C. (2018, October). Amazon staff protest firm’s sale of ‘unethical’ facial recognition tech. The Inquirer. UK.

    Google Scholar 

  • Paprotny, I., Doering, F., & White, R. M. (2010). MEMS particulate matter (PM) monitor for cellular deployment. SENSORS. 2010 IEEE, Kona, HI, pp. 2435–2440.

    Google Scholar 

  • Pasolini, G., Buratti, C., Feltrin, L., Zabini, F., De Castro, C., Verdone, R., & Andrisano, O. (2018). Smart City pilot projects using LoRa and IEEE802.15.4 technologies. Sensors, 18, 1118.

    Article  Google Scholar 

  • Pesonen, J., & Horster, E. (2012). Near field communication technology in tourism. Tourism Management Perspectives, 4, 11–18.

    Article  Google Scholar 

  • Pettit, C., Lieske, S. N., Jamal, M. (2017). CityDash: Visualising a changing city using open data. In International Conference on Computers in Urban Planning and Urban Management.

    Google Scholar 

  • Prasad, A., & Dusparic, I. (2019) Multi-agent deep reinforcement learning for zero energy communities. In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), Bucharest, Romania, pp. 1–5.

    Google Scholar 

  • Qin, G., Patsakis, C, & Bouroche, M. (2014). Playing hide and seek with Mobile dating applications, IFIP International Information Security and Privacy Conference (SEC), pp 185–196.

    Google Scholar 

  • Ramyam, S. T., Arunagirim, B., & Partha, R. (2017). Novel effective X-path particle swarm optimization based deprived video data retrieval for smart city. Cluster Computing, 22, 13085–13094.

    Google Scholar 

  • Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2015). Middleware for internet of things: A survey. IEEE Internet of Things Journal, 3(1), 70–95.

    Google Scholar 

  • Reggente, M., Mondini, A., Ferri, G., Mazzolait, B., Manzi, A., Gabelletti, M., Dario, P., & Lilienthal, A. J. (2010). The DustBot system: Using mobile robots to monitor pollution in pedestrian area. Chemical Engineering Transactions, 23, 273–278.

    Google Scholar 

  • Rehman, A., Rathore, M. M., Paul, A., Saeed, F., & Ahmad, R. W. (2018). Vehicular traffic optimisation and even distribution using ant colony in smart city environment. IET Intelligent Transport Systems, 12(7), 594–601. https://doi.org/10.1049/iet-its.2017.0308. Print ISSN 1751-956X, Online ISSN 1751-9578.

  • Reymond, M., Patyn, C., Radulescu, R., Nowe, A., & Deconinck, G. (2018). Reinforcement learning for demand response of domestic household appliances. In Proceedings of the Adaptive Learning Agents Workshop (ALA).

    Google Scholar 

  • Salkham, A., & Cahill V. Soilse: A decentralized approach to optimization of fluctuating urban traffic using reinforcement learning. In 13th International IEEE Conference on Intelligent Transportation Systems, ITSC 2010.

    Google Scholar 

  • Samani, H., & Zhu, R. (2016). Robotic automated external defibrillator ambulance for emergency medical service in smart cities. IEEE Access, 4, 268–283.

    Article  Google Scholar 

  • Sivaraman, V., Gharakheili, H. H., Fernandes, C., Clark, N., & Karliychuk, T. (2018). Smart IoT devices in the home: Security and privacy implications. IEEE Technology and Society Magazine, 37(2), 71–79.

    Google Scholar 

  • Smart City Robotics. www.smartcityrobotics.com. Accessed Sept 2019.

  • Stopczynski, A., Larsen, J. E., Lehmann, S., Dynowski, L., & Fuentes, M. (2013). Participatory bluetooth sensing: A method for acquiring spatio-temporal data about participant mobility and interactions at large scale events. In 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), San Diego, CA, pp. 242–247. https://doi.org/10.1109/PerComW.2013.6529489.

  • Tennina, S., Bouroche, M., Braga, P., Gomes, R., Alves, M., Mirza, F., Ciriello, V., Carrozza, G., Oliveira, P., & Cahill, V. (2011). Emmon: A wsn system architecture for large scale and dense real-time embedded monitoring. In 2011 IFIP 9th International Conference on Embedded and Ubiquitous Computing.

    Google Scholar 

  • The European Robotics League (ERL). https://www.eu-robotics.net/robotics_league. Accessed Aug 2019.

  • The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2016). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems. https://standards.ieee.org/industry-connections/ec/ead-v1.html.

  • Tiddi, I., Bastianelli, E., Daga, E., d’Aquin, M., & Motta, E. (2019). Robot–City interaction: Mapping the research landscape – A survey of the interactions between robots and modern cities. International Journal of Social Robotics. https://doi.org/10.1007/s12369-019-00534-x

    Article  Google Scholar 

  • Torresen, J. (2018). A review of future and ethical perspectives of robotics and AI. Frontiers in Robotics and AI.

    Google Scholar 

  • Wang, D., Cao, W., Li, J., Ye, J. (2017). DeepSD: Supply-demand prediction for online car-hailing services using deep neural networks. In International Conference on Data Engineering.

    Google Scholar 

  • Yang, Y., Yuan, Z., Fu, X., Wang, Y., & Sun, D. (2019) Optimization model of taxi fleet size based on GPS tracking data. Sustainability, 11, 731.

    Google Scholar 

  • Yi, H., Jung, H. J., Bae, S. (2017). Deep neural networks for traffic flow prediction. In International Conference on Big Data and Smart Computing (BIGCOMP).

    Google Scholar 

  • Zambonelli, F., Salim, F., Loke, S. W., De Meuter, W., & Kanhere, S. (2018). Algorithmic governance in smart cities: The conundrum and the potential of pervasive computing solutions. IEEE Technology and Society Magazine, 37(2), 80–87.

    Article  Google Scholar 

  • Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 38.

    Google Scholar 

  • Zheng, Y., Wu, W., Chen, Y., Qu, H., & Ni, L. M. (2016). Visual analytics in urban computing: An overview. IEEE Transactions on Big Data, 2(3), 276–296.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Statistics, Trinity College, Dublin, Ireland

    Mélanie Bouroche & Ivana Dusparic

Authors
  1. Mélanie Bouroche
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivana Dusparic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mélanie Bouroche or Ivana Dusparic .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Juan Carlos Augusto

Section Editor information

  1. No affiliation provided

    Juan Carlos Augusto

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Bouroche, M., Dusparic, I. (2020). Urban Computing: The Technological Framework for Smart Cities. In: Augusto, J. (eds) Handbook of Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-15145-4_5-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15145-4_5-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15145-4

  • Online ISBN: 978-3-030-15145-4

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation