Technological Innovations for Enhancing Disaster Resilience in Smart Cities: A Comprehensive Urban Scholar’s Analysis
Abstract
:1. Introduction
2. Methods
- technologies and tools for citywide geodata collection and management (cloud computing, sensor networks, location-based services, geo-visualization, Geographic Information Systems, mapping, the Internet of Things (IoT), and data warehouses, etc.)
- technologies and tools for public participation (crowdsourcing platforms, web-based participatory tools, social media, and Living Labs, etc.), and
- sectoral applications (for example, energy, transport, and environment, etc.)
3. Results and Discussion
3.1. Emerging and Disruptive Technologies for Improving Disaster Resilience in Smart Cities
3.1.1. Technologies and Tools for Citywide Geodata Collection and Management
- Cloud computing
- Internet of Things
- Bigdata
- Geo-visualisation and Geographical Information Systems (GIS)
- Sensor networks
- Grid technologies
- Wireless Wide Area Communication and Wireless Local Area Networks
- Location-Based Services (LBS)
- Geographical positioning techniques
- Blockchain
- Data Warehouses
- Digital twins
- Unmanned Aerial Vehicle (UAV)
- Cyber-Physical Systems (CPS)
- Building Information Modelling (BIM)
- Smart Disaster Response Systems (Smart DRS)
- Early warning systems
- Virtual Reality (VR), Augmented Reality (AR), And Mixed Reality (MR)
- Artificial Intelligence and machine learning
3.1.2. Technologies and Tools for Public Participation
- Crowdsourcing platforms
- Volunteered Geographical Information (VGI)
- Web-based participatory tools
- Social media
- Living Labs
3.2. Classification of Technologies
3.2.1. Impact on the Society
3.2.2. Adoption Speed by Smart Cities
3.2.3. Maturity of the Technology
- TRL 1: Basic principles observed and reported
- TRL 2: Technology concept or application formulated
- TRL 3: Analytical and experimental critical function or characteristic proof-of-concept
- TRL 4: Technology basic validation in a laboratory environment
- TRL 5: Technology basic validation in a relevant environment
- TRL 6: Technology model or prototype demonstration in a relevant environment
- TRL 7: Technology prototype demonstration in an operational environment
- TRL 8: Actual technology completed and qualified through test and demonstration
- TRL 9: Actual technology qualified through successful mission operations.
3.2.4. Capabilities Offered to the Community
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- United Nations. The World’s Cities in 2018—Data Booklet; Department of Economic and Social Affairs, Population Division: San Francisco, CA, USA, 2018. [Google Scholar]
- Chourabi, H.; Nam, T.; Walker, S.; Gil-Garcia, J.R.; Mellouli, S.; Nahon, K.; Pardo, T.A.; Scholl, H.J. Understanding Smart Cities: An Integrative Framework. In Proceedings of the 2012 45th Hawaii International Conference on System Sciences, Maui, HI, USA, 4–7 January 2012; pp. 2289–2297. [Google Scholar]
- Marek, L.; Campbell, M.; Bui, L. Shaking for innovation: The (re)building of a (smart) city in a post disaster environment. Cities 2017, 63, 41–50. [Google Scholar] [CrossRef]
- Lee, J.; Kim, J.; Seo, J. Cyber attack scenarios on smart city and their ripple effects. In Proceedings of the 2019 International Conference on Platform Technology and Service (PlatCon), Jeju, Republic of Korea, 28–30 January 2019; pp. 1–5. [Google Scholar]
- Cowley, R.; Caprotti, F.; Ferretti, M.; Zhong, C. Ordinary Chinese smart cities: The case of Wuhan. In Inside Smart Cities; Routledge: New York, NY, USA, 2018; pp. 45–64. [Google Scholar]
- Hu, X.; Li, L.; Dong, K. What matters for regional economic resilience amid COVID-19? Evidence from cities in Northeast China. Cities 2022, 120, 103440. [Google Scholar] [CrossRef]
- Samarakkody, A.; Amaratunga, D.; Haigh, R. Characterising Smartness to Make Smart Cities Resilient. Sustainability 2022, 14, 12716. [Google Scholar] [CrossRef]
- MacAskill, K.; Guthrie, P. Multiple Interpretations of Resilience in Disaster Risk Management. Procedia Econ. Financ. 2014, 18, 667–674. [Google Scholar] [CrossRef] [Green Version]
- Bansal, N.; Mukherjee, M.; Gairola, A. Smart Cities and Disaster Resilience; Springer: Singapore, 2017; pp. 109–122. [Google Scholar]
- Rauniyar, A.; Engelstad, P.; Feng, B.; Thanh, D.V. Crowdsourcing-Based Disaster Management Using Fog Computing in Internet of Things Paradigm. In Proceedings of the 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC), Pittsburgh, PA, USA, 1–3 November 2016; pp. 490–494. [Google Scholar]
- Kurwakumire, E.; Muchechetere, P.; Kuzhazha, S.; Ikokou, G.B. Geographic Information and Geo-visualisation in support of Disaster Resilience. Proc. ICA 2019, 2, 68. [Google Scholar] [CrossRef] [Green Version]
- Lee, S.K.; Kwon, H.R.; Cho, H.; Kim, J.; Lee, D. International Case Studies of Smart Cities; Inter-American Bank: Orlando, FL, USA, 2016. [Google Scholar]
- Shunji, M. Smart solutions for disaster management. Learning from the Great East Japan Tsunami and the accident at Fukushima NPs. Интерэкспo Геo-Сибирь 2013, 7, 3–18. [Google Scholar]
- Ryu, H.; Lim, H. Linking Smart City and Urban Sustainability Issue A Comparative Study of Smart City Services in Japan and Korea. Urban Reg. Plan. Rev. 2023, 10, 263–293. [Google Scholar] [CrossRef]
- Dembski, F.; Wössner, U.; Letzgus, M.; Ruddat, M.; Yamu, C. Urban Digital Twins for Smart Cities and Citizens: The Case Study of Herrenberg, Germany. Sustainability 2020, 12, 2307. [Google Scholar] [CrossRef] [Green Version]
- Bellini, E.; Nesi, P. Exploiting smart technologies to build smart resilient cities. In Handbook of Sustainable and Resilient Infrastructure; Routledge: London, UK, 2018; pp. 685–705. [Google Scholar]
- Stratigea, A.; Papadopoulou, C.-A.; Panagiotopoulou, M. Tools and Technologies for Planning the Development of Smart Cities. J. Urban Technol. 2015, 22, 1018725. [Google Scholar] [CrossRef]
- Sakurai, M.; Shaw, R. Emerging Technologies for Disaster Resilience: Practical Cases and Theories; Springer: Berlin/Heidelberg, Germany, 2021. [Google Scholar]
- Yeh, H. The effects of successful ICT-based smart city services: From citizens’ perspectives. Gov. Inf. Q. 2017, 34, 556–565. [Google Scholar] [CrossRef]
- Nam, T.; Pardo, T.A. Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, College Park, MD, USA, 12–15 June 2011; pp. 282–291. [Google Scholar]
- Kogan, N.; Lee, K.J. Exploratory research on the success factors and challenges of smart city projects. Asia Pac. J. Inf. Syst. 2014, 24, 141–189. [Google Scholar] [CrossRef]
- Rodríguez-Bolívar, M.P. Transforming City Governments for Successful Smart Cities; Springer: Berlin/Heidelberg, Germany, 2015. [Google Scholar]
- Lea, R.J. Smart cities: An overview of the technology trends driving smart cities. IEEE Adv. Technol. Humanit. 2017. Available online: https://eprints.lancs.ac.uk/id/eprint/126363 (accessed on 1 July 2023).
- Ahad, M.A.; Paiva, S.; Tripathi, G.; Feroz, N. Enabling technologies and sustainable smart cities. Sustain. Cities Soc. 2020, 61, 102301. [Google Scholar] [CrossRef]
- Sepasgozar, S.M.E.; Hawken, S.; Sargolzaei, S.; Foroozanfa, M. Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies. Technol. Forecast. Soc. Change 2019, 142, 105–116. [Google Scholar] [CrossRef]
- Angelidou, M. The Role of Smart City Characteristics in the Plans of Fifteen Cities. J. Urban Technol. 2017, 24, 3–28. [Google Scholar] [CrossRef]
- Stock, K.; Guesgen, H. Chapter 10—Geospatial Reasoning with Open Data. In Automating Open Source Intelligence; Layton, R., Watters, P.A., Eds.; Syngress: Boston, MA, USA, 2016; pp. 171–204. [Google Scholar]
- Pashova, E.L.; Bandrova, E.T.; Kouteva-Guentcheva, E.M. Usage of geo-data for educational, purposes to improve disaster preparedness. In Proceedings of the 7th International Conference on Cartography and GIS, Sozopol, Bulgaria, 18–23 June 2018. [Google Scholar]
- Qian, L.; Luo, Z.; Du, Y.; Guo, L. Cloud Computing: An Overview; Springer: Berlin/Heidelberg, Germany, 2009; pp. 626–631. [Google Scholar]
- Voorsluys, W.; Broberg, J.; Buyya, R. Introduction to Cloud Computing. In Cloud Computing; Wiley Online Library: Hoboken, NJ, USA, 2011; pp. 1–41. [Google Scholar]
- Riisager, P.; Herzberg, P.; Bødtkjer, M.; Jørgensen, J.; Bergstedt, T.; Helsted, C. Geodata in the Cloud. Geoforum Perspekt. 2015, 14. [Google Scholar] [CrossRef]
- Wieclaw, L.; Pasichnyk, V.; Kunanets, N.; Duda, O.; Matsiuk, O.; Falat, P. Cloud computing technologies in “smart city” projects. In Proceedings of the 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Bucharest, Romania, 21–23 September 2017; pp. 339–342. [Google Scholar]
- Alazawi, Z.; Alani, O.; Abdljabar, M.B.; Altowaijri, S.; Mehmood, R. A smart disaster management system for future cities. In Proceedings of the 2014 ACM international workshop on Wireless and Mobile Technologies for Smart Cities, Philadelphia, PA, USA, 11 August 2014; pp. 1–10. [Google Scholar]
- Ujjwal, K.C.; Saurabh, G.; James, H.; Jagannath, A.; Nicholas, F.-S. Cloud Computing in natural hazard modeling systems: Current research trends and future directions. Int. J. Disaster Risk Reduct. 2019, 38, 101188. [Google Scholar] [CrossRef]
- Wan, Z.; Hong, Y.; Khan, S.; Gourley, J.; Flamig, Z.; Kirschbaum, D.; Tang, G. A cloud-based global flood disaster community cyber-infrastructure: Development and demonstration. Environ. Model. Softw. 2014, 58, 86–94. [Google Scholar] [CrossRef] [Green Version]
- Huang, Q.; Cervone, G.; Zhang, G. A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data. Comput. Environ. Urban Syst. 2017, 66, 23–37. [Google Scholar] [CrossRef]
- Qiu, M.; Ming, Z.; Wang, J.; Yang, L.T.; Xiang, Y. Enabling cloud computing in emergency management systems. IEEE Cloud Comput. 2014, 1, 60–67. [Google Scholar] [CrossRef]
- Colman-Meixner, C.; Develder, C.; Tornatore, M.; Mukherjee, B. A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications. IEEE Commun. Surv. Tutor. 2016, 18, 2244–2281. [Google Scholar] [CrossRef] [Green Version]
- Tsai, C.-W.; Lai, C.-F.; Vasilakos, A.V. Future Internet of Things: Open issues and challenges. Wirel. Netw. 2014, 20, 2201–2217. [Google Scholar] [CrossRef]
- Goel, S.S.; Goel, A.; Kumar, M.; Moltó, G. A review of Internet of Things: Qualifying technologies and boundless horizon. J. Reliab. Intell. Environ. 2021, 7, 23–33. [Google Scholar] [CrossRef]
- Harrison, C.G.; Williams, P.R. A systems approach to natural disaster resilience. Simul. Model. Pract. Theory 2016, 65, 11–31. [Google Scholar] [CrossRef]
- Li, H.; Ota, K.; Dong, M. Always Connected Things: Building Disaster Resilience IoT Communications. In Proceedings of the 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), Tianjin, China, 4–6 December 2019; pp. 570–577. [Google Scholar]
- Ray, P.P.; Mukherjee, M.; Shu, L. Internet of Things for Disaster Management: State-of-the-Art and Prospects. IEEE Access 2017, 5, 18818–18835. [Google Scholar] [CrossRef] [Green Version]
- Shah, S.A.; Seker, D.Z.; Rathore, M.M.; Hameed, S.; Ben Yahia, S.; Draheim, D. Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers? IEEE Access 2019, 7, 91885–91903. [Google Scholar] [CrossRef]
- Pence, H.E. What is big data and why is it important? J. Educ. Technol. Syst. 2014, 43, 159–171. [Google Scholar] [CrossRef]
- Sarker, M.N.I.; Peng, Y.; Yiran, C.; Shouse, R.C. Disaster resilience through big data: Way to environmental sustainability. Int. J. Disaster Risk Reduct. 2020, 51, 101769. [Google Scholar] [CrossRef]
- Dattana, V.; Gupta, K.; Kush, A. A probability based model for big data security in Smart city. In Proceedings of the 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), Muscat, Oman, 15–16 January 2019. [Google Scholar]
- Malik, K.R.; Sam, Y.; Hussain, M.; Abuarqoub, A. A methodology for real-time data sustainability in smart city: Towards inferencing and analytics for big-data. Sustain. Cities Soc. 2018, 39, 548–556. [Google Scholar] [CrossRef]
- Lwin, K.K.; Sekimoto, Y.; Takeuchi, W. Development of GIS Integrated Big Data Research Toolbox (BigGIS-RTX) for Mobile CDR Data Processing in Disasters Management. J. Disaster Res. 2018, 13, 380–386. [Google Scholar] [CrossRef]
- Cariolet, J.-M.; Vuillet, M.; Diab, Y. Mapping urban resilience to disasters—A review. Sustain. Cities Soc. 2019, 51, 101746. [Google Scholar] [CrossRef]
- Tomaszewski, B. Geographic Information Systems for Disaster Management; Routledge: New York, NY, USA, 2015. [Google Scholar]
- Aloudat, A.; Michael, K. The application of location based services in national emergency warning systems: SMS, cell broadcast services and beyond. In Recent Advances in National Security Technology and Research; Mendis, P., Yates, A., Eds.; Australian Security Research Centre: Canberra, Austrilia, 2011; pp. 21–49. [Google Scholar]
- DesRoches, R.; Taylor, J.J.T.B. The Promise of Smart and Resilient Cities. 2018. Available online: https://trid.trb.org/view/1567234 (accessed on 1 July 2023).
- Asimakopoulou, E.; Bessis, N. Buildings and crowds: Forming smart cities for more effective disaster management. In Proceedings of the 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Seoul, Republic of Korea, 30 June–2 July 2011; pp. 229–234. [Google Scholar]
- Adeel, A.; Gogate, M.; Farooq, S.; Ieracitano, C.; Dashtipour, K.; Larijani, H.; Hussain, A. A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management. In Geological Disaster Monitoring Based on Sensor Networks; Durrani, T.S., Wang, W., Forbes, S.M., Eds.; Springer: Singapore, 2019; pp. 57–66. [Google Scholar]
- Cheikhrouhou, O.; Koubaa, A.; Zarrad, A. A Cloud Based Disaster Management System. J. Sens. Actuator Netw. 2020, 9, 6. [Google Scholar] [CrossRef] [Green Version]
- Sharma, H.; Haque, A.; Blaabjerg, F. Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey. Electronics 2021, 10, 1012. [Google Scholar] [CrossRef]
- Khalifeh, A.; Darabkh, K.A.; Khasawneh, A.M.; Alqaisieh, I.; Salameh, M.; AlAbdala, A.; Alrubaye, S.; Alassaf, A.; Al-HajAli, S.; Al-Wardat, R.; et al. Wireless Sensor Networks for Smart Cities: Network Design, Implementation and Performance Evaluation. Electronics 2021, 10, 218. [Google Scholar] [CrossRef]
- Heirman, D. What makes Smart Grid—Smart—And who is in the “game”? IEEE Electromagn. Compat. Mag. 2012, 1, 95–99. [Google Scholar] [CrossRef]
- Farmanbar, M.; Parham, K.; Arild, Ø.; Rong, C. A Widespread Review of Smart Grids Towards Smart Cities. Energies 2019, 12, 4484. [Google Scholar] [CrossRef] [Green Version]
- Nie, H.; Chen, Y.; Xia, Y.; Huang, S.; Liu, B. Optimizing the Post-Disaster Control of Islanded Microgrid: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Access 2020, 8, 153455–153469. [Google Scholar] [CrossRef]
- Palmieri, F.; Ficco, M.; Pardi, S.; Castiglione, A. A cloud-based architecture for emergency management and first responders localization in smart city environments. Comput. Electr. Eng. 2016, 56, 810–830. [Google Scholar] [CrossRef]
- Ring, J.; Foo, E.; Looi, M. On Ensuring Continuity of Mobile Communications in a Disaster Environment. Recent Adv. Secur. Technol. 2007, 2007, 268–277. [Google Scholar]
- Casoni, M.; Grazia, C.A.; Klapez, M.; Patriciello, N.; Amditis, A.; Sdongos, E. Integration of satellite and LTE for disaster recovery. IEEE Commun. Mag. 2015, 53, 47–53. [Google Scholar] [CrossRef]
- Yaqoob, I.; Hashem, I.A.T.; Mehmood, Y.; Gani, A.; Mokhtar, S.; Guizani, S. Enabling Communication Technologies for Smart Cities. IEEE Commun. Mag. 2017, 55, 112–120. [Google Scholar] [CrossRef]
- Chan, N.; Lars, H. Introduction to location-based services. Lund Univ. GIS Cent. 2003, 1–12. Available online: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ce2bbb181878b08ddf7fdea881269454640a7d34 (accessed on 1 July 2023).
- Barnes, S.J. Location-Based Services: The State of the Art. e-Serv. J. 2003, 2, 59–70. [Google Scholar] [CrossRef]
- Yudha, N.; Nasaruddin, S.; Roslidar, R. The Development of Online Disaster Information System Using Location Based Service (LBS) Technology. Int. J. Inform. Commun. Technol. (IJ-ICT) 2014, 47–58. [Google Scholar] [CrossRef]
- Zandbergen, P.A. Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning. Trans. GIS 2009, 13, 5–25. [Google Scholar] [CrossRef]
- Xie, Y.; Gupta, J.; Li, Y.; Shekhar, S. Transforming Smart Cities with Spatial Computing. In Proceedings of the 2018 IEEE International Smart Cities Conference (ISC2), Kansas City, MO, USA, 16–19 September 2018; pp. 1–9. [Google Scholar]
- Hakak, S.; Khan, W.Z.; Gilkar, G.A.; Imran, M.; Guizani, N. Securing Smart Cities through Blockchain Technology: Architecture, Requirements, and Challenges. IEEE Netw. 2020, 34, 8–14. [Google Scholar] [CrossRef]
- McIsaac, J.; Brulle, J.; Burg, J.; Tarnacki, G.; Sullivan, C.; Wassel, R. Blockchain Technology for Disaster and Refugee Relief Operations. Prehospital Disaster Med. 2019, 34, s106. [Google Scholar] [CrossRef] [Green Version]
- Pour, F.S.A. Application of a Blockchain Enabled Model in Disaster aids Supply Network Resilience; Old Dominion University: Norfolk, VA, USA, 2021. [Google Scholar]
- Hüsemann, B.; Lechtenbörger, J.; Vossen, G. Conceptual Data Warehouse Design; Universität Münster. Angewandte Mathematik und Informatik: Münster, Germany, 2000; Volume 168. [Google Scholar]
- Costa, C.; Santos, M.Y. The SusCity big data warehousing approach for smart cities. In Proceedings of the 21st International Database Engineering & Applications Symposium, Bristol, UK, 12–14 June 2017; pp. 264–273. [Google Scholar]
- Krasniqi, Z. The Role of Data Warehouse During the Natural Disaster. Eur. Acad. Res. 2015, 3. Available online: https://www.researchgate.net/publication/322941070_THE_ROLE_OF_DATA_WAREHOUSE_DURING_THE_NATURAL_DISASTER (accessed on 1 July 2023).
- Kuhn, T. Digitaler Zwilling. Inform.-Spektrum 2017, 40, 440–444. [Google Scholar] [CrossRef]
- Ford, D.N.; Wolf, C.M. Smart Cities with Digital Twin Systems for Disaster Management. J. Manag. Eng. 2020, 36. [Google Scholar] [CrossRef] [Green Version]
- Ye, X.; Du, J.; Han, Y.; Newman, G.; Retchless, D.; Zou, L.; Ham, Y.; Cai, Z. Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda. J. Plan. Lit. 2023, 38, 187–199. [Google Scholar] [CrossRef] [PubMed]
- Fan, C.; Zhang, C.; Yahja, A.; Mostafavi, A. Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management. Int. J. Inf. Manag. 2021, 56, 102049. [Google Scholar] [CrossRef]
- Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W. Digital Twin in manufacturing: A categorical literature review and classification. IFAC-Pap. 2018, 51, 1016–1022. [Google Scholar] [CrossRef]
- Aggarwal, S.; Kumar, N. Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Comput. Commun. 2020, 149, 270–299. [Google Scholar] [CrossRef]
- Ali, M.; Naeem, F.; Adam, N.; Kaddoum, G.; Adnan, M.; Tariq, M. Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review. arXiv 2023, arXiv:2301.08814. [Google Scholar]
- Qadir, Z.; Ullah, F.; Munawar, H.S.; Al-Turjman, F. Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review. Comput. Commun. 2021, 168, 114–135. [Google Scholar] [CrossRef]
- Wolf, W. Cyber-physical systems. Computer 2009, 42, 88–89. [Google Scholar] [CrossRef]
- Lei, Y.; Rao, Y.; Wu, J.; Lin, C.-H. BIM based cyber-physical systems for intelligent disaster prevention. J. Ind. Inf. Integr. 2020, 20, 100171. [Google Scholar] [CrossRef]
- Gunes, V.; Peter, S.; Givargis, T.; Vahid, F. A survey on concepts, applications, and challenges in cyber-physical systems. KSII Trans. Internet Inf. Syst. (TIIS) 2014, 8, 4242–4268. [Google Scholar]
- Madanian, S.; Johnson, K.; St Martin, M.; Sinha, R.; Cámara, J.; Parry, D. Adaptable socio-cyber physical systems for supporting disaster. Australas. J. Disaster Trauma Stud. 2022, 26, 221–234. [Google Scholar]
- Kesswani, N.; Kumar, S. Cyber Physical Systems and Smart Cities. CSI CommuniCationS 2017, 41, 29. [Google Scholar]
- Butun, I.; Österberg, P. Detecting intrusions in cyber-physical systems of smart cities: Challenges and directions. Secur. Cyber-Phys. Syst. Smart Cities 2019, 74–102. [Google Scholar] [CrossRef]
- Baker, R. Exploiting the Physical in Cyber-Physical Systems; University of Oxford: Oxford, UK, 2019. [Google Scholar]
- Habibzadeh, H.; Nussbaum, B.H.; Anjomshoa, F.; Kantarci, B.; Soyata, T. A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustain. Cities Soc. 2019, 50, 101660. [Google Scholar] [CrossRef]
- Isikdag, U. BIM: Steppingstone in Disaster management. 2010. Available online: https://www.gim-international.com/content/article/bim-steppingstone-in-disaster-management (accessed on 1 July 2023).
- Barnes, P.H.; Goonetilleke, A. (Eds.) Proceedings of the 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction; International Institute for Infrastructure Renewal and Reconstruction: Brisbane, Australia, 2015; ISBN 978-1-921897-73-3. [Google Scholar]
- Abdel-Basset, M.; Mohamed, R.; Elhoseny, M.; Chang, V. Evaluation framework for smart disaster response systems in uncertainty environment. Mech. Syst. Signal Process. 2020, 145, 106941. [Google Scholar] [CrossRef]
- Jung, Y. Smart disaster response through localized short-term cooperation. In Applications for Future Internet, Proceedings of the International Summit, AFI 2016, Puebla, Mexico, 25–28 May 2016; Springer International Publishing: Cham, Switzerland, 2017; pp. 12–21. [Google Scholar] [CrossRef]
- Riaz, K.; McAfee, M.; Gharbia, S.S. Management of Climate Resilience: Exploring the Potential of Digital Twin Technology, 3D City Modelling, and Early Warning Systems. Sensors 2023, 23, 2659. [Google Scholar] [CrossRef]
- Zhang, Y.; Geng, P.; Sivaparthipan, C.; Muthu, B.A. Big data and artificial intelligence based early risk warning system of fire hazard for smart cities. Sustain. Energy Technol. Assess. 2021, 45, 100986. [Google Scholar] [CrossRef]
- Ullah, F.; Qayyum, S.; Thaheem, M.J.; Al-Turjman, F.; Sepasgozar, S.M.E. Risk management in sustainable smart cities governance: A TOE framework. Technol. Forecast. Soc. Change 2021, 167, 120743. [Google Scholar] [CrossRef]
- Taylor, J.E.; Bennett, G.; Mohammadi, N. Engineering Smarter Cities with Smart City Digital Twins. J. Manag. Eng. 2021, 37, 02021001. [Google Scholar] [CrossRef]
- Elvas, L.B.; Mataloto, B.M.; Martins, A.L.; Ferreira, J.C. Disaster Management in Smart Cities. Smart Cities 2021, 4, 819–839. [Google Scholar] [CrossRef]
- Abid, S.K.; Sulaiman, N.; Chan, S.W.; Nazir, U.; Abid, M.; Han, H.; Ariza-Montes, A.; Vega-Muñoz, A. Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management. Sustainability 2021, 13, 12560. [Google Scholar] [CrossRef]
- Yigitcanlar, T.; Butler, L.; Windle, E.; Desouza, K.C.; Mehmood, R.; Corchado, J.M. Can building “artificially intelligent cities” safeguard humanity from natural disasters, pandemics, and other catastrophes? An urban scholar’s perspective. Sensors 2020, 20, 2988. [Google Scholar] [CrossRef]
- Sun, W.; Bocchini, P.; Davison, B.D. Applications of artificial intelligence for disaster management. Nat. Hazards 2020, 103, 2631–2689. [Google Scholar] [CrossRef]
- Ghezzi, A.; Gabelloni, D.; Martini, A.; Natalicchio, A. Crowdsourcing: A review and suggestions for future research. Int. J. Manag. Rev. 2018, 20, 343–363. [Google Scholar] [CrossRef]
- Hossain, M.; Kauranen, I. Crowdsourcing: A comprehensive literature review. Strateg. Outsourcing Int. J. 2015, 8, 2–22. [Google Scholar] [CrossRef]
- Harrison, S.E.; Johnson, P.A. Crowdsourcing the Disaster Management Cycle. Int. J. Inf. Syst. Crisis Response Manag. (IJISCRAM) 2016, 8, 17–40. [Google Scholar] [CrossRef] [Green Version]
- Haklay, M. Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation. In Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice; Sui, D., Elwood, S., Goodchild, M., Eds.; Springer: Dordrecht, The Netherlands, 2013; pp. 105–122. [Google Scholar]
- Goodchild, M.F.; Li, L. Assuring the quality of volunteered geographic information. Spat. Stat. 2012, 1, 110–120. [Google Scholar] [CrossRef]
- Flanagin, A.J.; Metzger, M.J. The credibility of volunteered geographic information. GeoJournal 2008, 72, 137–148. [Google Scholar] [CrossRef]
- Horita, F.E.A.; Degrossi, L.C.; de Assis, L.F.G.; Zipf, A.; de Albuquerque, J.P. The use of volunteered geographic information (VGI) and crowdsourcing in disaster management: A systematic literature review. In Proceedings of the Nineteenth Americas Conference on Information Systems, Chicago, IL, USA, 15–17 August 2013. [Google Scholar]
- Paolotti, D.; Carnahan, A.; Colizza, V.; Eames, K.; Edmunds, J.; Gomes, G.; Koppeschaar, C.; Rehn, M.; Smallenburg, R.; Turbelin, C.; et al. Web-based participatory surveillance of infectious diseases: The Influenzanet participatory surveillance experience. Clin. Microbiol. Infect. 2014, 20, 17–21. [Google Scholar] [CrossRef] [Green Version]
- Aye, Z.C.; Jaboyedoff, M.; Derron, M.-H.; Van Westen, C.J. Prototype of a Web-based Participative Decision Support Platform in Natural Hazards and Risk Management. ISPRS Int. J. Geo-Inf. 2015, 4, 1201–1224. [Google Scholar] [CrossRef] [Green Version]
- Rouse, L.J.; Bergeron, S.J.; Harris, T.M. Participating in the Geospatial Web: Collaborative Mapping, Social Networks and Participatory GIS. In The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 Are Shaping the Network Society; Scharl, A., Tochtermann, K., Eds.; Springer: London, UK, 2007; pp. 153–158. [Google Scholar]
- Phengsuwan, J.; Shah, T.; Thekkummal, N.B.; Wen, Z.; Sun, R.; Pullarkatt, D.; Thirugnanam, H.; Ramesh, M.V.; Morgan, G.; James, P.; et al. Use of Social Media Data in Disaster Management: A Survey. Future Internet 2021, 13, 46. [Google Scholar] [CrossRef]
- Sutton, J.N.; Palen, L.; Shklovski, I. Backchannels on the front lines: Emergency uses of social media in the 2007 Southern California Wildfires. In Proceedings of the 5th International ISCRAM Conference, Washington, DC, USA, 4–7 May 2008. [Google Scholar]
- Liu, B.F.; Fraustino, J.D.; Jin, Y. Social media use during disasters: How information form and source influence intended behavioral responses. Commun. Res. 2016, 43, 626–646. [Google Scholar] [CrossRef] [Green Version]
- Houston, J.B.; Hawthorne, J.; Perreault, M.F.; Park, E.H.; Goldstein Hode, M.; Halliwell, M.R.; Turner McGowen, S.E.; Davis, R.; Vaid, S.; McElderry, J.A. Social media and disasters: A functional framework for social media use in disaster planning, response, and research. Disasters 2015, 39, 1–22. [Google Scholar] [CrossRef] [PubMed]
- Schnebele, E.; Tanyu, B.F.; Cervone, G.; Waters, N. Review of remote sensing methodologies for pavement management and assessment. Eur. Transp. Res. Rev. 2015, 7, 7. [Google Scholar] [CrossRef] [Green Version]
- Ballon, P.; Schuurman, D. Living labs: Concepts, tools and cases. Info 2015, 17. [Google Scholar] [CrossRef]
- Sharifi, A.; Srivastava, R.; Singh, N.; Tomar, R.; Raji, M.A. Recent Advances in Smart Cities and Urban Resilience and the Need for Resilient Smart Cities. In Resilient Smart Cities: Theoretical and Empirical Insights; Sharifi, A., Salehi, P., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 17–37. [Google Scholar]
- Sakhardande, P.; Hanagal, S.; Kulkarni, S. Design of disaster management system using IoT based interconnected network with smart city monitoring. In Proceedings of the 2016 International Conference on Internet of Things and Applications (IOTA), Pune, India, 22–24 January 2016; pp. 185–190. [Google Scholar]
- Anttiroiko, A.-V.; Valkama, P.; Bailey, S.J. Smart cities in the new service economy: Building platforms for smart services. AI Soc. 2014, 29, 323–334. [Google Scholar] [CrossRef]
- Petrolo, R.; Loscrì, V.; Mitton, N. Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms. Trans. Emerg. Telecommun. Technol. 2017, 28, e2931. [Google Scholar] [CrossRef] [Green Version]
- Soto, J.A.C.; Werner-Kytölä, O.; Jahn, M.; Pullmann, J.; Bonino, D.; Pastrone, C.; Spirito, M. Towards a Federation of Smart City Services. In Proceedings of the 2015 International Conference on Recent Advances in Computer Systems, Prague, Czech Republic, 9–12 October 2015; pp. 163–168. [Google Scholar]
- Bonino, D.; Alizo, M.T.D.; Alapetite, A.; Gilbert, T.; Axling, M.; Udsen, H.; Soto, J.A.C.; Spirito, M. ALMANAC: Internet of Things for Smart Cities. In Proceedings of the 2015 3rd International Conference on Future Internet of Things and Cloud, Rome, Italy, 24–26 August 2015; pp. 309–316. [Google Scholar]
- Barletta, V.S.; Caivano, D.; Dimauro, G.; Nannavecchia, A.; Scalera, M. Managing a Smart City Integrated Model through Smart Program Management. Appl. Sci. 2020, 10, 714. [Google Scholar] [CrossRef] [Green Version]
- Calder, K.E. Singapore: Smart City, Smart State; Brookings Institution Press: Washington, DC, USA, 2016. [Google Scholar]
- Pierce, P.; Andersson, B. Challenges with smart cities initiatives–A municipal decision makers’ perspective. In Proceedings of the 50th Hawaii International Conference on System Sciences, Hilton Waikoloa Village, HI, USA, 4–7 January 2017. [Google Scholar]
- Cavalcante, E.; Cacho, N.; Lopes, F.; Batista, T. Challenges to the Development of Smart City Systems: A System-of-Systems View. In Proceedings of the XXXI Brazilian Symposium on Software Engineering, Fortaleza, CE, Brazil, 20–22 September 2017; pp. 244–249. [Google Scholar]
- Muvuna, J.; Boutaleb, T.; Baker, K.J.; Mickovski, S.B. A Methodology to Model Integrated Smart City System from the Information Perspective. Smart Cities 2019, 2, 496–511. [Google Scholar] [CrossRef] [Green Version]
- Kopackova, H.; Libalova, P. Smart city concept as socio-technical system. In Proceedings of the 2017 International Conference on Information and Digital Technologies (IDT), Zilina, Slovakia, 5–7 July 2017; pp. 198–205. [Google Scholar]
- Leavitt, H.J. Applied Organizational Change in Industry. In Handbook of Organizations; March, I.J.G., Ed.; Rand McNally: Chicago, IL, USA, 1965; Volume 1144, p. 1170. [Google Scholar]
- Biloria, N. Smart Cities: A Socio-Technical Perspective. In Proceedings of the International Conference on Game Set and Match IV Qatar-2019 [GSM4Q], Doha, Qatar, 6–7 February 2019. [Google Scholar]
- Kopáčková, H.; Libalova, P. The Rise of Smart Cities–Result of Global Problems or Technology Challenge? In Proceedings of the Globalization and Its Socio-Economic Consequences: 17th International Scientific Conference, Rajecke Teplice, Slovak Republic, 21–22 October 2020. [Google Scholar]
- Mora, L.; Deakin, M.; Zhang, X.; Batty, M.; de Jong, M.; Santi, P.; Appio, F.P. Assembling Sustainable Smart City Transitions: An Interdisciplinary Theoretical Perspective. J. Urban Technol. 2021, 28, 1–27. [Google Scholar] [CrossRef]
- Opazo, M.P. Revitalizing the concept of sociotechnical systems in social studies of technology. In Proceedings of the STPIS 2019 Socio-Technical Perspective in IS Development 2019, Stockholm, Sweden, 10 June 2010. [Google Scholar]
- Pichlak, M. The innovation adoption process: A multidimensional approach. J. Manag. Organ. 2016, 22, 476–494. [Google Scholar] [CrossRef]
- Dube, T.; Van Eck, R.; Zuva, T. Review of technology adoption models and theories to measure readiness and acceptable use of technology in a business organization. J. Inf. Technol. 2020, 2, 207–212. [Google Scholar]
- Kopackova, H.; Komarkova, J.; Horak, O. Enhancing the diffusion of e-participation tools in smart cities. Cities 2022, 125, 103640. [Google Scholar] [CrossRef]
- Bokhari, S.A.A.; Myeong, S. Artificial Intelligence-Based Technological-Oriented Knowledge Management, Innovation, and E-Service Delivery in Smart Cities: Moderating Role of E-Governance. Appl. Sci. 2022, 12, 8732. [Google Scholar] [CrossRef]
- Guseva, A.I.; Kireev, V.S.; Bochkarev, P.V.; Kuznetsov, I.A.; Filippov, S.A. End-to-end digital technologies in “smart cities” of Russia. IOP Conf. Ser. Earth Environ. Sci. 2021, 740, 012022. [Google Scholar] [CrossRef]
- Héder, M. From NASA to EU: The evolution of the TRL scale in Public Sector Innovation. Innov. J. 2017, 22, 1–23. [Google Scholar]
- Engel, D.W.; Dalton, A.C.; Anderson, K.K.; Sivaramakrishnan, C.; Lansing, C. Development of Technology Readiness Level (TRL) Metrics and Risk Measures; U.S. Department of Energy: Richland, WA, USA, 2012.
- Bruno, I.; Lobo, G.; Covino, B.V.; Donarelli, A.; Marchetti, V.; Panni, A.S.; Molinari, F. Technology readiness revisited: A proposal for extending the scope of impact assessment of European public services. In Proceedings of the ICEGOV, Athens, Greece, 11–13 March 2020; pp. 369–380. [Google Scholar]
- Giffinger, R.; Fertner, C.; Kramar, H.; Kalasek, R.; Pichler-Milanović, N.; Meijers, E. Smart Cities: Ranking of European Medium-Sized Cities; Centre of Regional Science (srf), Vienna University of Technology: Vienna, Austria, 2007; Available online: www.smart-cities.eu/download/smartcitiesfinalreport.pdf (accessed on 1 July 2023).
- Roche, S.; Nabian, N.; Kloeckl, K.; Ratti, C. Are ‘smart cities’ smart enough. In Proceedings of the Global Geospatial Conference, Québec, QC, Canada, 14–17 May 2012; pp. 215–235. [Google Scholar]
- Haenssgen, M.J.; Ariana, P. The place of technology in the Capability Approach. Oxf. Dev. Stud. 2018, 46, 98–112. [Google Scholar] [CrossRef] [Green Version]
- Nikou, S.; Agahari, W.; Keijzer-Broers, W.; de Reuver, M. Digital healthcare technology adoption by elderly people: A capability approach model. Telemat. Inform. 2020, 53, 101315. [Google Scholar] [CrossRef]
- Yang, C.; Su, G.; Chen, J. Using big data to enhance crisis response and disaster resilience for a smart city. In Proceedings of the 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, China, 10–12 March 2017; pp. 504–507. [Google Scholar]
- Alkhateeb, J. A new framework for identifying the missed pilgrims in Hajj and Umrah. Orient. J. Comput. Sci. Technol. 2017, 10, 718–724. [Google Scholar] [CrossRef] [Green Version]
- Ahmed, Q.A.; Memish, Z.A. Hajj 2016: Required vaccinations, crowd control, novel wearable tech and the Zika threat. Travel Med. Infect. Dis. 2016, 14, 429. [Google Scholar] [CrossRef]
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Samarakkody, A.; Amaratunga, D.; Haigh, R. Technological Innovations for Enhancing Disaster Resilience in Smart Cities: A Comprehensive Urban Scholar’s Analysis. Sustainability 2023, 15, 12036. https://doi.org/10.3390/su151512036
Samarakkody A, Amaratunga D, Haigh R. Technological Innovations for Enhancing Disaster Resilience in Smart Cities: A Comprehensive Urban Scholar’s Analysis. Sustainability. 2023; 15(15):12036. https://doi.org/10.3390/su151512036
Chicago/Turabian StyleSamarakkody, Aravindi, Dilanthi Amaratunga, and Richard Haigh. 2023. "Technological Innovations for Enhancing Disaster Resilience in Smart Cities: A Comprehensive Urban Scholar’s Analysis" Sustainability 15, no. 15: 12036. https://doi.org/10.3390/su151512036