Smarter cities and their innovation challenges
Abstract
The transformation to smarter cities will require innovation in planning, management, and operations. Several ongoing projects around the world illustrate the opportunities and challenges of this transformation. Cities must get smarter to address an array of emerging urbanization challenges, and as the projects highlighted in this article show, several distinct paths are available. The number of cities worldwide pursuing smarter transformation is growing rapidly. However, these efforts face many political, socioeconomic, and technical hurdles. Changing the status quo is always difficult for city administrators, and smarter city initiatives often require extensive coordination, sponsorship, and support across multiple functional silos. The need to visibly demonstrate a continuous return on investment also presents a challenge. The technical obstacles will center on achieving system interoperability, ensuring security and privacy, accommodating a proliferation of sensors and devices, and adopting a new closed-loop human-computer interaction paradigm.
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This approach requires sustainable partnerships and cooperation strategies among the main stakeholders.
“This approach requires sustainable partnerships and cooperation strategies among the main stakeholders [50].”
From Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience (2016) · cites this paper as [50]
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Hannon, J., & Zaman, A. U. (2018) · Urban Science · doi:10.3390/urbansci2030090 · Cited by 77 · see citing works
“in respect of waste [96]) to foster zero-energetic, zero-waste, environmentally sustainable self-sufficient food security, and symbiotic industrial ecologies [8,94,97,98].”
BackgroundEnergy Strategies, the Urban Dimension, and Spatial Planning
Asarpota, K., & Nadin, V. (2020) · Energies · doi:10.3390/en13143642 · Cited by 75 · see citing works
Smart and Sustainable Planning for Cities and Regions
Bisello, A., Vettorato, D., Laconte, P., et al. (2018) · Green energy and technology · doi:10.1007/978-3-319-75774-2 · Cited by 73 · see citing works
Naphade, M., Anastasiu, D. C., Sharma, A., Jagrlamudi, V., Jeon, H., Liu, K., Chang, M., Lyu, S., & Gao, Z. (2017) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397673 · Cited by 70 · see citing works
“This breakthrough also has great potential for making our cities smarter [2] and safer.”
BackgroundSelf-citationDeployment of an open sensorized platform in a smart city context
Trilles, S., Calia, A., Belmonte, Ó., Torres-Sospedra, J., Montoliu, R., & Huerta, J. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.11.005 · Cited by 71 · see citing works
Shaking for innovation: The (re)building of a (smart) city in a post disaster environment
Marek, L., Campbell, M. H., & Bui, L. (2017) · Cities · doi:10.1016/j.cities.2016.12.013 · Cited by 69 · see citing works
Anthopoulos, L. G. (2017) · Public administration and information technology · doi:10.1007/978-3-319-57015-0_2 · Cited by 87 · see citing works
The Urbanism of Exception: The Dynamics of Global City Building in the Twenty-First Century
Murray, M. J. (2017) · Thammasat University Digital Collections · Cited by 68 · see citing works
Kolozali, Ş., Kuemper, D., Tönjes, R., Bermúdez-Edo, M., Farajidavar, N., Barnaghi, P. M., Gao, F., Ali, M. I., Mileo, A., Fischer, M., et al. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2872606 · Cited by 68 · see citing works
“City authorities, however, encounter several difficulties in implementing, sustaining, and optimizing operations and interactions among different city departments and services [1].”
BackgroundContactless Technologies for Smart Cities: Big Data, IoT, and Cloud Infrastructures
Arunmozhi, M., Dharshini, V., Zografopoulos, I., Priyan, M. K., & Konstantinou, C. (2021) · SN Computer Science · doi:10.1007/s42979-021-00719-0 · Cited by 69 · see citing works
Heterogeneous Sensors Become Homogeneous Things in Smart Cities
Fazio, M. C., Paone, M., Puliafito, A., et al. (2012) · International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) · doi:10.1109/imis.2012.136 · Cited by 64 · see citing works
“Our main idea is to design an innovative architecture, able to add new sensing capabilities with ZeroConf approaches, abstracting sensing data, conferring to World-Wide systems a high reactivity and high level of scalability.”
BackgroundLatin American smart cities: Between worlding infatuation and crawling provincialising
Irazábal, C., & Jirón, P. (2020) · Urban Studies · doi:10.1177/0042098020945201 · Cited by 63 · see citing works
“The initial interest in smart cities focused on the benefits that ICT corporations offered contemporary cities (Dodgson and Gann, 2011) for their efficient management, innovation and optimisation (Naphade et al., 2011).”
BackgroundDeng, S. (2020) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2020.100446 · Cited by 64 · see citing works
Poku‐Boansi, M., Amoako, C., Owusu-Ansah, J. K., et al. (2020) · City and Environment Interactions · doi:10.1016/j.cacint.2020.100038 · Cited by 60 · see citing works
Elevated Performance of the Smart City—A Case Study of the IoT by Innovation Mode
Hsiao, Y., Wu, M.-H., & Li, S. C. (2019) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2908962 · Cited by 59 · see citing works
“TheUnited Nations Population Fund forecasts that, by 2030, approximately 60% of the world’s population will live in an urban environment, and 27 megacities with more than 10 million people are anticipated to exist [41].”
BackgroundIntegrating machine learning in embedded sensor systems for Internet-of-Things applications
Lee, J., Stanley, M., Spanias, A., & Tepedelenlioğlu, C. (2016) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2016.7886051 · Cited by 60 · see citing works
“We provide first some background information and bibliography on applications of machine learning in IoT [19, 20], mobile [24] and cloud systems [25], natural data compression [23, 26] and recognition [27,28,58,60], image classification [41], health monitoring [29,30], smart cities [21, 22], smart campus [31], transpo…”
BackgroundEfficient Identity-Based Encryption Scheme with Equality Test in Smart City
Wu, L., Zhang, Y., Choo, K. R., et al. (2017) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2017.2734110 · Cited by 59 · see citing works
“ACCORDING to the United Nations, about 70 percent of the world population will live in an urban environment by 2050 [1].”
Background
- Authors
- Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., & Morris, R.
- Affiliations
- IBM (United States) (US)
- ORCID
- Banavar, Guruduth
- Venue
- Computer
- Published
- 2011-06
- Volume
- 44
- Issue
- 6
- Pages
- 32-39
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W2079421249
- Cited by
- 567
- References
- 0
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 20 · 2023: 21 · 2022: 30 · 2021: 43 · 2020: 47 · 2019: 50 · 2018: 43 · 2017: 63 · 2016: 62 · 2015: 58 · 2014: 57 · 2013: 29 · 2012: 18
- FWCI
- 82.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer (publishedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Innovative Approaches in Technology and Social Development
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- smart cities, urban transformation, urban planning, urban management, urbanization challenges, return on investment, interoperability, privacy and security, human-computer interaction, organizational silos, barriers to innovation
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