Solutions to Manage Smart Cities’ Risks in Times of Pandemic Crisis
2. Materials and Methods
2.1. Method Description
2.2. Inclusion Criteria
2.3. Literature Search
- 20 for smart cities fundamentals;
- 12 supplementing COVID-19/smart city risk management.
3.1. Smart City Basis
- User count;
- Device count;
- Information quantity;
- Delay awareness;
- On-the-spot applications;
- System integration and flexibility.
- Legal fumbled snap;
- Infrastructure-functioning consistency;
- Safeguarding private information;
- Provider swaps;
- Interoperability and free access to data and services.
- Availability of ICT infrastructure—the secure and protected ICT infrastructure of the latest generation is of paramount importance for the successful provision of new services in smart cities and to ensure readiness for the provision of new services in the future;
- Existence of a built and integrated management system—the many systems of a smart city can work together via various integration mechanisms (Lane and Epstein 2013);
- Availability of smart users—technologies are a means to ensure the functioning of the smart city, but they are useless in the absence of competent users who know how to interact with the services provided.
- Smart financial system—urban areas ought to have an increased performance predicated on the utilization and mixture of knowledge-based means of production, an innovative climate, and a flexible labor market. The economy should be distinguished by the use of creative technologies and adaptability to changing circumstances;
- Smart mobility—the city grows into a massive system of links among all of its assets;
- Intelligent surroundings—a smart city enhances its power usage through the use of renewable resources as well as other implications, seeks to minimize environmental pollution, and outposts its recycling programs on the principles of responsible development. Sustainability initiatives necessitate a significant amount of environmental learning as well;
- Intelligent citizens—an educational community;
- Smart residing—sociable atmosphere, particularly through the stipulation of broad public service accessibility;
- Smart governance—cooperation of local authorities and other users of the city;
- Viewed as a functioning system, smart cities use a network of interconnected gadgets as well as other systems to enhance the standard of living and empower productivity expansion in four steps (Twi 2018). They are:
- Gathering—intelligent devices retrieve information directly;
- Analysis—the information is analyzed to get an idea of the work of city services and operations;
- Connection—information results obtained are conveyed to the government;
- Do—activities are implemented to enhance processes, financial services, and citizens’ value in urban life.
3.2. COVID-19 Impact
3.3. Measures Implementation Challenges
- Priority must be given to confidentiality, trust, and human rights risks. The effective fight against the COVID-19 pandemic necessitates the use of a variety of tracking applications, sensors, and other real-time data collection devices that severely infringe on residents’ privacy. Furthermore, technology institutions that provide digital technologies for the city and that access residents have full access to each individual’s information, which poses privacy risks. These challenges necessitate careful consideration and the application of appropriate technologies in order to eliminate citizens’ risks and concerns during times of crisis (Tedeschi 2020);
- Digital inequality (UNCLG 2020). Unequal access to digital resources is problematic from two perspectives: access to technology and the risk of irrational use after access (Watts 2020). During the COVID-19 pandemic in particular, certain age groups may be less knowledgeable and skilled regarding various technologies and devices, and temporary residents may not have access to the smart city’s services at all (Seifert 2020). As a result, ensuring access for all residents and increasing social inclusion has become a critical risk issue for both digital service institutions and governing institutions (Levenda et al. 2020);
- Turning digitalization into an undemocratic process in which governments misuse technology (Vanolo 2014). There is also the risk of intentional or unintentional dissemination of incorrect information through specific channels, which, as previously stated, can be corrected by administrators of relevant digital resources. However, they may be influenced by various large corporations using their reputation and putting pressure on them to misrepresent false data and facts, thereby violating democratic principles;
- Complexity of data-processing actions due to COVID-19’s current data sources’ specific algorithms. This can lead to misinterpretation, incorrect understanding, and, as a result, incorrect outcomes, thereby putting urban governance at risk of less transparency and accountability (Matheus et al. 2020). At the same time, a city government’s high-tech information process costs for the dissemination and use of technologically oriented tools (Papadopoulos et al. 2020) against COVID-19 are quite high;
- Difficulties with educational services. In the case of mass training institutions, foreign individuals with varying understandings and abilities are trained, which brings to light the risk of digital access inequality when conducting distance learning. Numerous studies conducted prior to the days of COVID-19 demonstrated that digital learning was ineffective due to the inability to account for learner differences. This can result in a lag in certain individuals and, as a result, a situation of educational inequality (Murat and Bonacini 2020). It should also be noted that some training courses necessitate physical presence and an increased level of social contact, which is impossible to achieve through distance learning;
- Remote employment is also at risk during COVID-19. Although remote work allows for a more flexible schedule, it still significantly blurs the lines between personal and professional life. According to Hu’s (2020) research, remote employment can significantly boost productivity. However, there is a risk of serious negative consequences in the long run due to the accumulation of schedules and the reductions in free time (Okubo 2020). Security and stability risks in remote employment, as well as access to organizational data, should be supplemented;
- Vaccination issues. With the spread of the 2019 coronavirus (COVID-19), vaccination is becoming increasingly important around the world (Singh et al. 2022). Vaccines are widely regarded as the most effective means of reducing the number of infected people and eventually eliminating the infection. Significant issues with this direction include the impossibility of vaccinating a large portion of the population, which is the case in China (Statista 2022); and, perhaps more importantly, insufficient quantities of necessary vaccines. This highlights the importance of optimizing production and ensuring adequate vaccine supplies. The Internet of Things concept is critical in a smart city for manufacturing vaccines with wearable sensors. Traditional manufacturers rely on trusted third parties, which can serve as a single point of failure. Due to demand response data in advanced manufacturing, access control, big data, and scalability are also challenging issues in existing systems. To address these issues, a P2P smart-contract-based smart architecture of a smart city for vaccine production with three layers (including connection, conversion, and a smart cloud layer) was developed and proposed (Singh et al. 2022). The goal was to provide security and privacy while maintaining adequate access control. Contracts such as contract registration, contract manager, and contract access control are used for secure data communication with distributed access control to produce vaccines.
3.4. Smart Cities’ Risk-Management Indicators
3.5. Estimate Studies
3.5.1. Asian Region
3.5.2. Outside Asian Region
3.6. COVID-19 Countering Model
- Detect—anticipation of threats through in-depth analysis;
- Protect—strengthening vulnerabilities;
- React—creation of a crisis center for counteraction;
- Restore—identification of key means of functioning at reduced capacity.
- The idea behind edge computing is to build networks that will allow urban IoT devices to be supported by the cloud (Wang et al. 2018). The goal of this model is to reduce data-transmission delays between network nodes. If a device cannot connect to a network node, it will temporarily receive data from the cloud to provide continuous services;
- Artificial intelligence is used in systems and devices for voice and facial recognition, network security against foreign country penetration, authentication device profiling, analyses to optimize the performance of IoT devices in smart cities, and other purposes (Chen et al. 2018);
- A software-defined network is a concept for ensuring network availability and eliminating congestion by prioritizing traffic routing (Abhishek et al. 2016). Software agents set priorities to redistribute network resources as needed and achieve a stable and permanent connection between residents in an emergency;
- The massive amount of data collected by devices in various smart city systems necessitates the implementation of a big data analysis model in urban planning. Rathore et al. (2016) proposed a four-step model for analyzing big data in smart cities to enable the government to make smart decisions that are implemented in real-time.
- Being fully democratic in the use and implementation of technologies;
- Finding effective ways to activate the technically inactive population and direct it to participate in the educational system and social processes;
- Effective telecommunications use to facilitate the continuity of everyday COVID-19 situations;
- Integration of various social media and tracking applications, etc., with the urban system by promoting digitalization processes.
- The research spanned the years 2019–2021 and did not include the post-pandemic era;
- Risk-management approaches were technologically oriented with no consideration given to internal institutional measures. For example:
- Small populated areas in the composition of smart cities in which there were several peculiarities in the targeting of technologies and measures were not studied.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Petrova, M.; Tairov, I. Solutions to Manage Smart Cities’ Risks in Times of Pandemic Crisis. Risks 2022, 10, 240. https://doi.org/10.3390/risks10120240
Petrova M, Tairov I. Solutions to Manage Smart Cities’ Risks in Times of Pandemic Crisis. Risks. 2022; 10(12):240. https://doi.org/10.3390/risks10120240Chicago/Turabian Style
Petrova, Mariana, and Iskren Tairov. 2022. "Solutions to Manage Smart Cities’ Risks in Times of Pandemic Crisis" Risks 10, no. 12: 240. https://doi.org/10.3390/risks10120240