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Review

An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application

1
Escolla Técnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politécnica de Catalunya (UPC), Av. Diagonal, 647, 08028 Barcelona, Spain
2
Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
3
Center for Environmental Policy, Imperial College London, London SE1 0AA, UK
4
Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Via Gramsci 53, 00197 Rome, Italy
5
Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, QLD 4006, Australia
6
Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
7
Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh 3919715179, Iran
8
Research Fellow in IoT and Big Data, Business and Management Research Institute, University of Bedfordshire, Luton LU1 3JU, UK
9
Centre for Efficiency and Performance Engineering (CEPE), Department of Engineering & Technology, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, West Yorkshire HD1 3DH, UK
10
Solar Thermal Vacuum Engineering Research Group, London Centre for Energy Engineering, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2022, 12(5), 2672; https://doi.org/10.3390/app12052672
Submission received: 13 January 2022 / Revised: 28 February 2022 / Accepted: 1 March 2022 / Published: 4 March 2022
(This article belongs to the Special Issue Future Transportation)

Abstract

:
As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisticated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking.

1. Introduction

While old, crowded cities are under pressure from many issues such as population explosion and improper infrastructure, the rise of smart cities (SCs) has provided a good solution for solving many of the existing problems and overcoming urban challenges [1]. Therefore, the rapid development of SCs in recent years reflects their importance [2]. In fact, SCs with greater opportunities for citizens and proper services are becoming an attractive choice for people and communities, and they can be a place for fostering success in health and businesses across the world with the help of smart infrastructure [3]. On the other hand, achieving SC development requires detailed and specific planning and the proper implementation and establishment of policies. Thus, identifying obstacles can grant us a deeper understanding of how to determine the best solutions with less difficulty [4]. Since, in developing SCs, we are faced with numerous barriers and problems in various areas, such as roadways, environment, utilities, parking, public safety, waste management, and public transport, it is pertinent to enhance these sectors through accurate investigation and practical actions [5]. To overcome such barriers, SC governments must implement appropriate strategies and present proper solutions to mitigate or eliminate these barriers [6]. In this regard, the role of the Internet of Things (IoT) and the utilization of these systems is an essential and beneficial strategy to appropriately develop SCs [7]. In fact, with the appearance of new technologies such as the IoT, the concept of SCs has changed and continues to evolve for the better, subsequently improving and accelerating urban management across various sectors [8]. This means that the utilization of the IoT leads to the development of smart cities [9]. In recent years, many types of research have been conducted on SC development [10]. For instance, the importance of the IoT for SC development was also reviewed by Badis Hammi et al., who demonstrated that a higher level of interaction between SCs and IoT development is essential, as it can integrate electronic devices. However, the safety risks and privacy issues of participating individuals, companies, and organizations should be considered carefully in such cases [11]. Ejaz et al., investigated efficient energy management for the IoT in the context of SCs and observed that such management is a key paradigm to monitoring complex energy systems. They also showed that efficient energy management can support wireless energy transfer for IoT devices and energy-efficient planning in smart homes [12]. Tanveer et al. investigated the growth of the IoT markets across energy systems of SCs. Regarding the importance of smart grid technology innovations in supporting smart energy systems in SCs, the study showed that investment in these systems has increased in recent years. Based on the literature, the IoT of the global energy market exceeded USD 6.8 billion in 2015 and is anticipated to reach USD 26.5 billion by 2023, which portrays an annual growth rate of 15.5% between 2016 and 2023 [13]. Bresciani et al., investigated the IoT in terms of organizations, in order to innovate and implement it in everyday business activities. The results from the 43 IoT SC project alliances across Italian cities they investigated demonstrated that multinational enterprises are building alliances for exploring new technologies for cities as well as exploiting new IoT-based devices to gain economic profit. The study proved that for companies to achieve the desired results, they must integrate different types of knowledge to ensure efficient management and effective support [14]. Evertzen et al., analyzed the effects of smart governance on the quality of life in SCs in the three well-known cities of Palo Alto, Nice, and Stockholm. This research emphasized the importance of innovative approaches across SCs, which should be implemented based on the IoT, and consequently, many services should be promptly digitalized. Therefore, in order to achieve these goals and successfully implement an SC model, strong leadership, citizen involvement, and business collaboration are required [15]. With regard to the importance of transportation systems in SCs, the prospect of handling considerable information using sensor data from the environment for better monitoring of transport systems in SCs was examined by AlZubi et al., as the time data extracted from sensors is important; the researchers presented a responder-dependent add-on information fusion scheme concerning sensor data. This guided vehicle scheme can observe the responding sensor information in order to determine the success of the goal endorsed. This scheme, which is based on classification machine learning, can help us identify and subsequently reduce the errors caused by sensor information [16]. In light of the importance of the IoT in the development of smart cities, this article examines the problems and solutions of seven key sectors that have a significant impact on SC development, including the environment, public transportation, utilities, street lighting, waste management, public safety, and smart parking. We also considered certain important cities in the EU (Paris, London, Copenhagen, Barcelona, Amsterdam, and Oslo) and in the United States (Boston, New York, and San Francisco) based on the relevance of the IoT.

2. Motivation and Objective of the Critical Review

Creating and developing SCs is an important objective for many countries [17] to enhance the life quality of their population through the optimal management of their resources [18]. In addition, SC development supports global mitigation strategies, especially across the environmental and energy sectors [19]. One of the most important factors for SC development is the IoT [20], which integrates different systems related to energy, transport, and waste and water management within SCs in order to enhance the inhabitants’ quality of life [21]. Given that more of the global population resides in urban areas, therefore it can be said that cities are held accountable for the majority of the global energy consumption and greenhouse gas (GHG) emissions [22]. Thus, a reduction in energy use and the maximization of renewable energy use, when available, can support these objectives. The use of the IoT in SCs provides an opportunity to make incremental changes in efficiency by harnessing new technologies and automating processes in applications [23]. It is important to recognize that the innovation, advancement, and implementation of the IoT across SCs have a dynamic impact on many other intertwined systems, including the environment, economy, and transportation. Therefore, it is crucial to create an in-depth understanding of these independencies to ensure that negative impacts are not overlooked and positive impacts are enhanced and used to create an incentive to create changes across cities. The aims of this study include investigating the concept of SCs, identifying the IoT barriers across seven important sectors, and compiling appropriate solutions to tackle each barrier.

3. Methodology

To identify the potential barriers to IoT development in SC development and, based on the importance of the IoT, we conducted an exhaustive review of more than 400 relevant publications related to the IoT, and have searched in Internet the using established scientific databases, such as Google Scholar, Scopus, Web of Science and Journal sites (Taylor & Francis, Elsevier, MDPI, Springer, Willey, etc.).
In this regard, we searched, in the Internet, words such as smart cities, environment, road traffic, public transport, utilities, smart lighting, public safety, waste management, street lighting, and smart parking. In the first step, between 2019 and 2020, we investigated more than 200 review papers to understand the concepts of the IoT and smart cities. Then, we investigated, in 2021, more than 200 technical papers, and eventually selected 121 papers. After these steps, we categorized the most important papers which helped us to start writing this paper and we selected the methodology. Review articles helped us understand SC development and the IoT technologies that have come under the spotlight within a short period of time. Moreover, technical articles established a deeper understanding of effective policies in SC development relative to the IoT in order to obtain proper solutions to the barriers. Figure 1 shows the flowchart for the methodology of this study. After all the relative papers were collected, the articles were categorized into two groups—review papers and technical papers. We based the methodology on the best of these. In the last step, we determined recommended actions and policies to achieve the goal of the paper.

4. Results and Discussion

4.1. Recognizing the Existing Obstacles in the Development of SCs

As we are faced with various barriers and problems across seven specific sectors in SCs, i.e., environment, public transport, utilities, street lighting, waste management, public safety, and parking, we believe that the utilization and implementation of the IoT will be effective in mitigating or resolving the problems associated with these areas. In the following sections, we comprehensively discuss these problems and the solutions that we obtained from the review articles and scientific research.

4.1.1. Environment

Cleaner air and water systems are crucial elements of the environment [24]; for this, a network of sensors should be used to monitor air [25] and water quality [26]. Specifically, sensors can be used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage, pH levels, and changes in the chemical composition of water. Therefore, sensors can be implanted along busy roads, around plants, and near houses, offices, and organizations [27]. Moreover, it is necessary to utilize sensors for detection and monitoring and to obtain data and results [28]. According to the McKinsey Global Institute, emissions can be reduced by 10–15% through applications that focus on building automation, mobility, and dynamic electricity pricing. Thus, SCs can support and contribute to a cleaner and more sustainable environment [29]. Nowadays, sensors, as well as environmental sensors, have significantly affected lives, as individual environmental sensors obtain data about the environment and then transform that data into electrical signals to feed higher-level systems around the individual sensors. The advantages of these sensors are lower cost, smaller size, and reliability [30].

4.1.2. Public Transport

Considering the safety and efficiency of citizens of SCs is crucial, especially on roads [31]. Therefore, municipalities are attempting to implement smart traffic using IoT development solutions [32]. In this regard, the IoT will play a crucial role in traffic management. For instance, data from various types of sensors and GPS systems are sent by drivers’ smartphones in order to determine the speed, number, and locations of vehicles on a particular road. Subsequently, smart traffic lights are immediately connected to a cloud-management platform and provide timing information to automatically and accurately monitor green lights, thereby preventing traffic congestion. Additionally, these methods can predict traffic in the future and offer prevention plans, with which the transport administration department is able to detect potentially dangerous situations in time and take required actions to prevent traffic congestion [33]. Therefore, considering the obvious importance of transportation systems in SCs, specific and accurate planning to control these systems is necessary [34]. According to [35], transport technological development with the IoT will have a big revolution between 2020 and 2030, that will have direct impact on toll operators and highways and provide safe and secure networks [35]. In addition, traffic data from multiple sources, such as traffic information and ticket sales, can be used to perform sophisticated analyses and achieve better results, and train operators can maximize the capacity of tracks and easily prevent train delays [36]. Fortunately, many countries around the world, especially developing countries, are now trying to make use of new systems connected to the IoT for controlling their transportation systems [37].

4.1.3. Utilities

IoT-equipped SCs give more control to citizens over their home utilities, reducing overall bills and related costs [38]. By utilizing IoT technologies and effective approaches, such as smart meters for billing, monitoring consumption patterns, and remote monitoring, municipalities can achieve cost-effective connectivity to utility companies’ IT systems. This helps customers consume energy and water based on improved monitoring and, therefore, presents better management services to the citizens [39]. Precooling optimization using system data (IoT), while preserving the thermal comfort of the inhabitants, has a direct influence on expenses and energy consumption (electricity costs) for cooling of a building by up to 30% percent, according to an Australian study [40]. Also, other research shows that, in Arabian Gulf countries, a smart energy management system using the Internet of Things can reduce costs, especially for air conditioning, which accounts for up to 60% of electricity consumption, while still meeting energy demand [41]. On the other hand, use of the IoT in utilities has a good effect on attainment of efficiency (management of large-scale solar photovoltaic systems) [42] and conservation of resources [43].

4.1.4. Street Lighting

In SCs, the maintenance and control of streetlamps can be more cost-effective and straightforward through the use of the IoT [44]. In particular, IoT systems can be paired with sensors that connect to a cloud-management solution [45], providing confident monitoring of illuminated transport paths such as streets and the movement of people and vehicles. Measuring the environmental conditions can also allow for a more accurate analysis regarding the need to improve the lighting schedule and indicate if lights should be brighter or dimmer [46]. On the other hand, IoT systems have a remarkable effect on energy-saving associated with urban street lighting as using warmer lights and increasing light uniformity can result in a 30–50% energy saving on street lighting, and for medium-sized cities with populations around 200,000–400,000 residents, energy savings on street lighting it can reach 8–23 MWh per annum [47].

4.1.5. Waste Management

Waste collection is one of the most important sectors of SCs [48]. In this regard, IoT can reduce a lot of problems in this regard [49]. To achieve this, a sensor will be placed on each waste container, which will gather data regarding the level of waste in the container; then, after the container is filled, a notification will be sent to truck drivers via a mobile app. By following this useful and effective plan, truck drivers will expend time and energy to only empty full containers instead of half-full ones [50]. A study in China of recycling and household waste segregation between 2018 and 2019 showed that integration of the Internet of Things (IoT) was effective in household waste management. During the study, collections of recyclable waste and biodegradable food waste were elevated by 431.8% and 88.8%, respectively, which had good environment effects and meant that this macro policy increased the recyclable waste collection by 431.8% in Shanghai [51].

4.1.6. Public Safety

Theft of motor vehicles throughout the world, coupled with a massive loss of cash, is a disaster for insurance companies. For instance, just in the USA, in 2019 about USD 6.4 billion was lost to motor vehicle theft [52]. Likewise, every year, worldwide, 70 million smartphones are lost or stolen [53]. In these regards, IoT-based SC technologies are vital for offering real-time monitoring, enhancing public safety, and supporting proper decision-making, that will prevent a lot of harm to people [54]. For example, testing of the motorcycle antitheft system (MATS) showed that this system had 100% accuracy at speeds of up to 70 km/h and for speeds up to 80, it had 94.4% accurate [55].

4.1.7. Smart Parking

Parking occupies a large amount of the area in a city—81% of the city area in Los Angeles, 23% in Munich, 23% in Paris, 19% in Copenhagen, and 18% in Zurich and Hamburg. Therefore, cities must use of intelligent parking systems in order to reduce congestion and help drivers [56]. In this regard, IoT technology has built a special mobile application in order to solve vehicle parking problems and this has had a remarkable effect for drivers. Based on research, from 2013 to 2018, downloading of the mobile application, increased from 17 million downloads to 80 million, which shows the benefit of this application in solving problems related to parking [57]. Therefore, the importance of smart parking in SCs should be investigated by policymakers [58], considering that finding parking spots can improve the welfare of citizens [59]. This action can be achieved by utilizing GPS data from drivers’ smartphones and road-surface sensors embedded in the ground of parking spots. As a result, drivers can be notified of occupied and vacant parking spots via a real-time parking map [60].

4.2. Strategic Policies for Boosting Economic Recovery of Smart Cities through the IoT

The IoT technology inherent in smart cities, promises effective options that will allow cities to be more safe, inclusive, and resilient [61]. In this regard, the IoT helps cities to improve good governance and privacy which are important for the socio-economic dimensions of urban areas [62]. In addition, the advance of 5G technologies [63] and artificial neural networks (ANNs) will prompt further innovations in smart city technologies of the IoT [64]. In fact, cloud-based IoT applications that contain information gathered from citizens could help smart cities to monitor and manage crime detection, proficiency, water supply systems, healthcare facilities, electric utilities, digital libraries, transportation networks, resource management, waste management, and security mechanisms [65]. Therefore, smart technologies such as the IoT are significant when developing SCs, while maintaining emphasis on the implemented strategies and policies [66]. It is clear that the implementation of targets related to SCs requires strong and calculated strategies and policies [67]. In fact, achieving “smartness” is not a one-time action; it is a continuous process. Therefore, policymakers should aim to devise a plan [68] that considers the individual goals of each sector whilst also evaluating the dynamic and indirect impacts on other areas within an SC. Undoubtedly, to advance SCs and continue their expansion, officials and policymakers must vigorously strive to create a unique quality of life, work, and environment for the citizens of their cities [69]. On the other hand, since the concept of SCs falls in line with the smart grid, economic issues related to the programs that are used for demand-response management (DRM) and real-time pricing should be taken into consideration [70]. In addition, it can be added that as SCs aim to improve the quality of life of urban citizens, the success of SCs depends on participation by private companies [71]. Therefore, through the use of new communication channels between the government and its citizens, policymakers should focus on the essential needs of stakeholders, such as affordable energy, urban security, and energy security [69], because, public participation will help improve quality of life and establish trust between local governments and people [72]. This means that the investment in developing SCs has advantages for both people and the community, including a reduction in the cost of living, improvement of living standards and environmental sustainability, improvement of operational efficiency, improvement of eco-friendly infrastructure, and development of smart technology through the IoT [73]. Moreover, private investment (companies) can help governments easily overcome old issues pertaining to big cities or developing cities that have not been well planned [74]. In general, investment on IoT technologies, is opening new possibilities for cities and helping them to be smart cities [75]. According to these cases, effective strategies and policies can accelerate the conversion of a standard city into an SC and, thus, should aim to attract investment, improve IT infrastructure, integrate connected local energy storage systems in order to support better renewable energy sources on the power grids, and adapt an IoT implementation strategy based on the city’s size to reduce costs, support the utilization of smart LED streetlights in major metropolises, increase the collaboration between local governments and stakeholders, increase the utilization of new technologies such as sensors, change the mentality of the citizens, and redefine the governance model with proper politics. Based on the comprehensive explanations presented above, the most important barriers and the most appropriate solutions related to IoT-based SC development are presented in Table 1.
To complete this Table, other investigations can be added. For example one of the greatest challenges at present is the low or inadequate quality of the life in many areas of the world. This means that in many areas, use of energy is not based on world standards and there is less use of modern technologies to manage it. This affects the quality of life of citizens, and, in particular, the economy of households [104,105]. Without a doubt, collection of the wastes in crowded areas especially cities, is important for citizens, because it prevents illness. Utilization of IoT is very important in providing more efficient waste management and overcoming other problems in this area [106]. Healthcare is one of the most important challenges for governments because low health of the citizens can have negative effects on people. In this regard, IoT systems can improve net health and increase people’s health knowledge [107]. In addition, using e-health services, for instance in a global pandemic such as COVID-19, for data collection by citizens, for giving health advice through the Internet, and for increasing the health of medical staff is fruitful [108]. On the other hand, as mentioned previously, transportation systems are one of most important sectors in many countries because transport has a large effect on the environment and the movement of people. Therefore, today, the emergence of IoT systems inside cars and the conversion to smart cars (vehicles), helps the environment and can also move people easily without loss of time [109,110]. In addition, in order to reduce traffic and help the environment, greater utilization of bicycles and an increase in bike-sharing services has been implemented through IoT systems [111]. Moreover, to improve the electrical energy saving of the cars, increasing the lifetime of battery-operated devices (by up to a couple of years) by using of IoT systems is possible because IoT systems are able, during inactive periods, to keep the device in a low-power state [112]. The issue of the elderly and their care is also important in many countries. Fortunately, however, IoT systems have provided assistance applications through the use of a single wearable device in both outdoor and indoor locations. These systems are able to recognize changes in the behavior of elderly people, are low-cost, unobtrusive, have a low power consumption, and can easily prevent problems [113].
Table 2 shows a comparison between results of this work and a number of works in the literature. As can be seen, most previous work has investigated limited subjects related to the IoT and smart cities, while this work comprehensively investigated these subjects.

5. Conclusions

Future with less CO2 [121], and relying upon renewable energy [122] as main fuels, are some of the most important goals of scientists and researchers. In this regard, the role of IoT in controlling CO2 emissions and managing energy consumption is important. This work investigated the problems related to seven important sectors of the IoT, namely environment, public transport, utilities, smart parking, public safety, waste management, and smart lighting. Each sector was analyzed carefully to identify the challenges to be mitigated or removed such that building SCs would be possible. For instance, in the environment sector, the utilization of air and water sensors allows us to monitor air and water quality and detect the amount of CO2, sulfur oxides, and nitrogen, water leakage, and changes in the pH level and chemical composition of water over time, as well as other factors that have potentially detrimental effects. In terms of road traffic, the determination of the speed, number, and locations of vehicles and monitoring of green-light timings can be achieved through the use of various types of sensors and GPS data collected from drivers’ smartphones. Across the public-transport sector, IoT sensors can help enhance our travel experiences and achieve a higher level of safety and punctuality. In utility monitoring, the IoT allows users to control their home utilities for billing, consumption patterns, and remote monitoring. In particular, via cost-effective connectivity to utility companies’ IT systems, customers can adjust their energy and water consumption more economically. For street lighting, utilizing both IoT systems and sensors connected to a cloud-management solution can ensure the confident monitoring of illuminance for the safe movement of people and vehicles. In terms of environmental effects, we can improve the lighting schedule and determine which areas require different intensities of light (some streets may only need a dim light, so less electricity would be used). In the waste-management sector, the use of IoT technologies can lead to the optimization of waste-collection schedules by tracking the waste levels, providing route optimization, and ensuring useful operational analytics. In this regard, each waste container would be implanted with a sensor that gathers data on the level of waste in a container. Then, a notification of filled containers would be sent to truck drivers via a mobile app. This is a strategic plan to avoid emptying half-full containers, resulting in less travel by trucks and reducing GHG emissions. In the public-safety sector, IoT-based SC technologies have a crucial role in offering real-time monitoring, enhancing public safety, and developing decision-making tools and analytics through CCTV cameras and acoustic sensors. At the same time, data from social media feeds can be carefully analyzed to improve public safety in a city and predict potential crime scenes. For the smart-parking sectors, IoT technologies can help drivers identify available parking spots on a real-time map based on GPS data extracted from drivers’ smartphones or road-surface sensors embedded in parking spots.

Author Contributions

Several authors contributed to this research: methodology, validation, review, and editing, A.R. (Armin Razmjoo), A.G., and M.M.; formal analysis and investigation, S.A. and A.R. (Alireza Rezvani); resources, D.A.G.; writing, and final analysis, S.M. (Seyedali Mirjalili) and S.M. (Saim Memon). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not Applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kandt, J.; Batty, M. Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities 2020, 109, 102992. [Google Scholar] [CrossRef]
  2. Razmjoo, A.; Nezhad, M.M.; Kaigutha, L.G.; Marzband, M.; Mirjalili, S.; Pazhoohesh, M.; Memon, S.; Ehyaei, M.A.; Piras, G. Investigating smart city development based on green buildings, electrical vehicles and feasible indicators. Sustainability 2021, 13, 7808. [Google Scholar] [CrossRef]
  3. Razmjoo, A.; Østergaard, P.A.; Denaï, M.; Nezhad, M.M.; Mirjalili, S. Effective policies to overcome barriers in the development of smart cities. Energy Res. Soc. Sci. 2021, 79, 102175. [Google Scholar] [CrossRef]
  4. Abella, A.; Ortiz-De-Urbina-Criado, M.; De-Pablos-Heredero, C. A model for the analysis of data-driven innovation and value generation in smart cities’ ecosystems. Cities 2017, 64, 47–53. [Google Scholar] [CrossRef]
  5. Kundu, D. Developing National Urban Policies: Ways Forward to Green and Smart Cities; Springer: Singapore, 2020. [Google Scholar]
  6. Gil-Garcia, J.R.; Pardo, T.A. E-government success factors: Mapping practical tools to theoretical foundations. Gov. Inf. Q. 2005, 22, 187–216. [Google Scholar] [CrossRef]
  7. Park, E.; Del Pobil, A.P.; Kwon, S.J. The Role of Internet of Things (IoT) in Smart Cities: Technology Roadmap-oriented Approaches. Sustainability 2018, 10, 1388. [Google Scholar] [CrossRef] [Green Version]
  8. Sarin, G. Developing smart cities using Internet of Things: An empirical study. In Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIA Com), New Delhi, India, 16–18 March 2016. [Google Scholar]
  9. Li, X. Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning. Future Generation Comput. Syst. 2022, 128, 167–177. [Google Scholar] [CrossRef]
  10. Cepeda-Pacheco, J.C.; Domingo, M.C. Deep learning and Internet of Things for tourist attraction recommendations in smart cities. Neural Comput. Applic. 2022. [Google Scholar] [CrossRef]
  11. Hammi, B.; Khatoun, R.; Zeadally, S.; Fayad, A.; Khoukhi, L. IoT technologiesfor smart cities. IET Netw. 2018, 7, 1–13. [Google Scholar] [CrossRef]
  12. Ejaz, W.; Naeem, M.; Shahid, A.; Anpalagan, A.; Jo, M. Efficient Energy Management for the Internet of Things in Smart Cities. IEEE Commun. Mag. 2017, 55, 84–91. [Google Scholar] [CrossRef] [Green Version]
  13. Ahmad, T.; Zhang, D. Using the internet of things in smart energy systems and networks. Sustain. Cities Soc. 2021, 68, 102783. [Google Scholar] [CrossRef]
  14. Bresciani, S.; Ferraris, A.; Del Giudice, M. The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technol. Forecast. Soc. Chang. 2018, 136, 331–338. [Google Scholar] [CrossRef]
  15. Evertzen, W.H.N.; Effing, R.; Constantinides, E. The Internet of Things as Smart City Enabler: The Cases of Palo Alto, Nice and Stockholm. In Digital Transformation for a Sustainable Society in the 21st Century; Pappas, I., Mikalef, P., Dwivedi, Y., Jaccheri, L., Krogstie, J., Mäntymäki, M., Eds.; Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2019; Volume 11701. [Google Scholar] [CrossRef]
  16. AlZubi, A.A.; Alarifi, A.; Al-Maitah, M.; Alheyasat, O. Multi-sensor information fusion for Internet of Things assisted automated guided vehicles in smart city. Sustain. Cities Soc. 2020, 64, 102539. [Google Scholar] [CrossRef]
  17. Kumar, S.; Tiwari, P.; Zymbler, M. Internet of Things is a revolutionary approach for future technology enhancement: A review. J. Big Data 2019, 6, 1–21. [Google Scholar] [CrossRef] [Green Version]
  18. Jha, S.; Nkenyereye, L.; Joshi, G.P.; Yang, E. Mitigating and Monitoring Smart City using Internet of Things. Comput. Mater. Contin. 2020, 65, 1059–1079. [Google Scholar] [CrossRef]
  19. Vinod Kumar, T.M. Smart Environment for Smart Cities. In Smart Environment for Smart Cities; Vinod Kumar, T., Ed.; Advances in 21st Century Human Settlements; Springer: Singapore, 2020. [Google Scholar] [CrossRef]
  20. Allam, Z.; Dhunny, Z.A. On big data, artificial intelligence and smart cities. Cities 2019, 89, 80–91. [Google Scholar] [CrossRef]
  21. Latre, S. City of things: An integrated and multi-technology testbed for IoT smart city experiments. In Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2), Trento, Italy, 12–15 September 2016. [Google Scholar] [CrossRef]
  22. Armin, R. Implementation of energy sustainability using hybrid power systems, a case study. Energy Sources Part A Recovery Util. Environ. Eff. 2019, 1–14. [Google Scholar] [CrossRef]
  23. How Smart Cities Can Help Tackle Climate Change. Available online: http://www.frontier-economics.com/uk/en/news-and-articles/articles/article-i4604-how-smart-cities-can-help-tackle-climate-change/#_ftn1 (accessed on 12 January 2022).
  24. Doni, A. Survey on multi sensor based air and water quality monitoring using IoT. Indian J. Sci. Res. 2018, 17, 147–153. [Google Scholar]
  25. Duangsuwan, S. A Development on Air Pollution Detection Sensors based on NB-IoT Network for Smart Cities. In Proceedings of the 2018 18th International Symposium on Communications and Information Technologies (ISCIT), Bangkok, Thailand, 26–29 September 2018. [Google Scholar] [CrossRef]
  26. Kafli, N. Internet of Things (IoT) for measuring and monitoring sensors data of water surface platform. In Proceedings of the 2017 IEEE 7th International Conference on Underwater System Technology: Theory and Applications (USYS), Kuala Lumpur, Malaysia, 18–20 December 2017. [Google Scholar] [CrossRef]
  27. Ullo, S.L.; Sinha, G.R. Advances in Smart Environment Monitoring Systems Using IoT and Sensors. Sensors 2020, 20, 3113. [Google Scholar] [CrossRef]
  28. Randazzo, G.; Italiano, F.; Micallef, A.; Tomasello, A.; Cassetti, F.P.; Zammit, A.; D’Amico, S.; Saliba, O.; Cascio, M.; Cavallaro, F.; et al. WebGIS Implementation for Dynamic Mapping and Visualization of Coastal Geospatial Data: A Case Study of BESS Project. Appl. Sci. 2021, 11, 8233. [Google Scholar] [CrossRef]
  29. Smart Cities: Digital Solutions for a More Livable Future. Available online: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Operations/Our%20Insights/Smart%20cities%20Digital%20solutions%20for%20a%20more%20livable%20future/MGI-Smart-Cities-Full-Report.pdf (accessed on 12 January 2022).
  30. Sanders, D. Environmental sensors and networks of sensors. Sens. Rev. 2008, 28. [Google Scholar] [CrossRef]
  31. Vishal, D. IoT-driven road safety system. In Proceedings of the 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Mysuru, India, 15–16 December 2017. [Google Scholar] [CrossRef]
  32. Majumdar, S.; Subhani, M.M.; Roullier, B.; Anjum, A.; Zhu, R. Congestion prediction for smart sustainable cities using IoT and machine learning approaches. Sustain. Cities Soc. 2020, 64, 102500. [Google Scholar] [CrossRef]
  33. Al-Sakran, H.O. Intelligent traffic information system based on integration of Internet of Things and Agent technology. Int. J. Adv. Comput. Sci. Appl. 2015, 6, 37–43. [Google Scholar]
  34. Chen, C.; Zhang, Y.; Khosravi, M.R.; Pei, Q.; Wan, S. An Intelligent Platooning Algorithm for Sustainable Transportation Systems in Smart Cities. IEEE Sens. J. 2020, 21, 15437–15447. [Google Scholar] [CrossRef]
  35. Azmat, M.; Kummer, S.; Moura, L.T.; Di Gennaro, F.; Moser, R. Future Outlook of Highway Operations with Implementation of Innovative Technologies Like AV, CV, IoT and Big Data. Logistics 2019, 3, 15. [Google Scholar] [CrossRef] [Green Version]
  36. Simmhan, Y.; Ravindra, P.; Chaturvedi, S.; Hegde, M.; Ballamajalu, R. Towards a data-driven IoT software architecture for smart city utilities. Softw. Pr. Exp. 2018, 48, 1390–1416. [Google Scholar] [CrossRef]
  37. Rehman, A.; Mumtaz, T. On Board Intelligence for Public Transportation in Developing Countries using IoT. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2020; Volume 899, p. 012004. [Google Scholar]
  38. Saravanan, K.; Julie, E.G.; Robinson, Y.H. Smart Cities & IoT: Evolution of Applications, Architectures & Technologies, Present Scenarios & Future Dream. In Internet of Things and Big Data Analytics for Smart Generation; Balas, V., Solanki, V., Kumar, R., Khari, M., Eds.; Intelligent Systems Reference Library; Springer: Cham, Switzerland, 2019; Volume 154. [Google Scholar] [CrossRef]
  39. Suresh, M. A novel smart water-meter based on IoT and smartphone app for city distribution management. In Proceedings of the 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, India, 14–16 July 2017. [Google Scholar] [CrossRef]
  40. Vishwanath, A.; Chandan, V.; Saurav, K. An IoT-Based Data Driven Precooling Solution for Electricity Cost Savings in Commercial Buildings. IEEE Internet Things J. 2019, 6, 7337–7347. [Google Scholar] [CrossRef]
  41. Al-Ali, A. A smart home energy management system using IoT and big data analytics approach. In IEEE Transactions on Consumer Electronics; IEEE: Piscataway, NJ, USA, 2017; Volume 63. [Google Scholar] [CrossRef]
  42. Shapsough, S.; Takrouri, M.; Dhaouadi, R.; Zualkernan, I.A. Using IoT and smart monitoring devices to optimize the efficiency of large-scale distributed solar farms. Wirel. Netw. 2018, 27, 4313–4329. [Google Scholar] [CrossRef]
  43. Arshad, R.; Zahoor, S.; Shah, M.A.; Wahid, A.; Yu, H. Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond. IEEE Access 2017, 5, 15667–15681. [Google Scholar] [CrossRef]
  44. Carli, R.; Dotoli, M.; Cianci, E. An optimization tool for energy efficiency of street lighting systems in smart cities. IFAC-Pap. 2017, 50, 14460–14464. [Google Scholar] [CrossRef]
  45. Yusoff, Y.M.; Rosli, R.; Karnaluddin, M.U.; Samad, M. Towards smart street lighting system in Malaysia. In Proceedings of the 2013 IEEE Symposium on Wireless Technology & Applications (ISWTA), Kuching, Malaysia, 22–25 September 2013. [Google Scholar] [CrossRef]
  46. Barve, V. Smart lighting for smart cities. In Proceedings of the 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, India, 14–16 July 2017. [Google Scholar] [CrossRef]
  47. Saad, R.; Portnov, B.A.; Trop, T. Saving energy while maintaining the feeling of safety associated with urban street lighting. Clean Technol. Environ. Policy 2020, 23, 251–269. [Google Scholar] [CrossRef]
  48. Singh, A.; Aggarwal, P.; Arora, R. IoT based waste collection system using infrared sensors. In Proceedings of the 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 7–9 September 2016. [Google Scholar] [CrossRef]
  49. Nasreen Banu, M.I.; Metilda Florence, S. Convergence of Artificial Intelligence in IoT Network for the Smart City—Waste Management System. In Expert Clouds and Applications. Lecture Notes in Networks and Systems; Jeena Jacob, I., Gonzalez-Longatt, F.M., Kolandapalayam Shanmugam, S., Izonin, I., Eds.; Springer: Singapore, 2022; Volume 209. [Google Scholar] [CrossRef]
  50. Medvedev, A.; Fedchenkov, P.; Zaslavsky, A.; Anagnostopoulos, T.; Khoruzhnikov, S. Waste Management as an IoT-Enabled Service in Smart Cities. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems; Balandin, S., Andreev, S., Koucheryavy, Y., Eds.; ruSMART 2015, NEW2AN 2015. Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2015; Volume 9247. [Google Scholar] [CrossRef]
  51. Jiang, P.; Van Fan, Y.; Klemeš, J.J. Data analytics of social media publicity to enhance household waste management. Resour. Conserv. Recycl. 2020, 164, 105146. [Google Scholar] [CrossRef] [PubMed]
  52. Insurance Information Institute. Available online: https://www.iii.org/fact-statistic/facts-statistics-auto-theft#Motor%20Vehicle%20Theft,%202010-2019 (accessed on 30 March 2021).
  53. Hom, E.J. Mobile Device Security: Startling Statistics on Data Loss and Data Breaches. 2017. Available online: http://www.channelpronetwork.com/article/mobile-device-security-startling-statistics-data-loss-anddata-breaches (accessed on 7 March 2021).
  54. Fraga-Lamas, P.; Fernández-Caramés, T.M.; Suárez-Albela, M.; Castedo, L.; González-López, M. A Review on Internet of Things for Defense and Public Safety. Sensors 2016, 16, 1644. [Google Scholar] [CrossRef] [Green Version]
  55. Papadakis, N. An IoT-Based Participatory Antitheft System for Public Safety Enhancement in Smart Cities. Smart Cities 2021, 4, 919–937. [Google Scholar] [CrossRef]
  56. Kalašová, A.; Čulík, K.; Poliak, M.; Otahálová, Z. Smart Parking Applications and Its Efficiency. Sustainability 2021, 13, 6031. [Google Scholar] [CrossRef]
  57. Pariama, R.E.; Manaha, R. Suyoto Parking-RR: Mobile Application Malioboro Smart Parking Based on IoT Technology. IOP Conf. Ser. Earth Environ. Sci. 2021, 704, 012037. [Google Scholar] [CrossRef]
  58. Al-Turjman, F.; Malekloo, A. Smart parking in IoT-enabled cities: A survey. Sustain. Cities Soc. 2019, 49, 101608. [Google Scholar] [CrossRef]
  59. Sadhukhan, P. An IoT-based E-parking system for smart cities. In Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 13–16 September 2017. [Google Scholar] [CrossRef]
  60. Lanza, J.; Sanchez, L.; Gutiérrez, V.; Galache, J.A.; Santana, J.R.; Sotres, P.; Muñoz, L. Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case. Energies 2016, 9, 719. [Google Scholar] [CrossRef] [Green Version]
  61. Allam, Z. Redefining the Smart City: Culture, Metabolism and Governance. Smart Cities 2018, 1, 4–25. [Google Scholar] [CrossRef] [Green Version]
  62. Allam, Z. The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City. Smart Cities 2019, 2, 96–105. [Google Scholar] [CrossRef] [Green Version]
  63. Allam, Z.; Jones, D.S. Future (post-COVID) digital, smart and sustainable cities in the wake of 6G: Digital twins, immersive realities and new urban economies. Land Use Policy 2021, 101, 105201. [Google Scholar] [CrossRef]
  64. Allam, Z. Achieving Neuroplasticity in Artificial Neural Networks through Smart Cities. Smart Cities 2019, 2, 118–134. [Google Scholar] [CrossRef] [Green Version]
  65. Alam, T. Cloud-Based IoT Applications and Their Roles in Smart Cities. Smart Cities 2021, 4, 1196–1219. [Google Scholar] [CrossRef]
  66. Chatterjee, S. Success of IoT in Smart Cities of India: An empirical analysis. Gov. Inf. Q. J. 2018, 35, 349–361. [Google Scholar] [CrossRef]
  67. Sokolov, A.; Veselitskaya, N.; Carabias, V.; Yildirim, O. Scenario-based identification of key factors for smart cities development policies. Technol. Forecast. Soc. Chang. 2019, 148, 119729. [Google Scholar] [CrossRef]
  68. Makeen, P.; Ghali, H.A.; Memon, S. Experimental and Theoretical Analysis of the Fast Charging Polymer Lithium-Ion Battery Based on Cuckoo Optimization Algorithm (COA). IEEE Access 2020, 8, 140486–140496. [Google Scholar] [CrossRef]
  69. Kumar, H.; Singh, M.K.; Gupta, M.; Madaan, J. Moving towards smart cities: Solutions that lead to the Smart City Transformation Framework. Technol. Forecast. Soc. Chang. 2018, 153, 119281. [Google Scholar] [CrossRef]
  70. Kumari, A.; Gupta, R.; Tanwar, S.; Tyagi, S.; Kumar, N. When Blockchain Meets Smart Grid: Secure Energy Trading in Demand Response Management. IEEE Netw. 2020, 34, 299–305. [Google Scholar] [CrossRef]
  71. Dameri, R.P. Triple Helix in Smart Cities: A Literature Review about the Vision of Public Bodies, Universities, and Private Companies. In Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA, 5–8 January 2016. [Google Scholar] [CrossRef]
  72. Castelnovo, W. Smart Cities Governance: The Need for a Holistic Approach to Assessing Urban Participatory Policy Making. Soc. Sci. Comput. Rev. 2015, 34, 724–739. [Google Scholar] [CrossRef]
  73. Milenković, M. Using Public Private Partnership models in smart cities—Proposal for Croatia. In Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 22–26 May 2017. [Google Scholar] [CrossRef]
  74. Jonek-Kowalska, I.; Wolniak, R. Sharing Economies’ Initiatives in Municipal Authorities’ Perspective: Research Evidence from Poland in the Context of Smart Cities’ Development. Sustainability 2022, 14, 2064. [Google Scholar] [CrossRef]
  75. Belli, L. IoT-Enabled Smart Sustainable Cities: Challenges and Approaches. Smart Cities 2020, 3, 1039–1071. [Google Scholar] [CrossRef]
  76. Khan, S. Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion. Comput. Electr. Eng. 2021, 89, 106906. [Google Scholar] [CrossRef]
  77. Prasanth, R.S. Preventing Road Accidents using IoT. Int. J. Comput. Sci. Eng. 2018, 6, 419–423. [Google Scholar] [CrossRef]
  78. Porru, S.; Missoa, F.E.; Pani, F.E.; Repetto, C. Smart mobility and public transport: Opportunities and challenges in rural and urban areas. J. Traffic Transp. Eng. 2020, 7, 88–97. [Google Scholar] [CrossRef]
  79. Harini, B.K. Increasing Efficient Usage of Real-Time Public Transportation Using IOT, Cloud and Customized Mobile App. SN Comput. Sci. 2020, 1, 159. [Google Scholar] [CrossRef]
  80. Ladha, A.; Bhattacharya, P.; Chaubey, N.; Bodkhe, U. IIGPTS: IoT-Based Framework for Intelligent Green Public Transportation System. In Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019); Singh, P., Pawłowski, W., Tanwar, S., Kumar, N., Rodrigues, J., Obaidat, M., Eds.; Lecture Notes in Networks and Systems; Springer: Singapore, 2020; Volume 121. [Google Scholar] [CrossRef]
  81. Umamaheswari, S. Smart Street Lighting in Smart Cities: A Transition from Traditional Street Lighting. In Security and Privacy Applications for Smart City Development; Tamane, S.C., Dey, N., Hassanien, A.E., Eds.; Studies in Systems, Decision and Control; Springer: Cham, Switzerland, 2021; Volume 308. [Google Scholar] [CrossRef]
  82. Zhang, J.J.; Zeng, W.H.; Hou, S.L.; Chen, Y.Q.; Guo, L.Y.; Li, Y.X. A low-power and low cost smart streetlight system based on Internet of Things technology. Telecommun. Syst. 2022, 79, 83–93. [Google Scholar] [CrossRef]
  83. Ali, M.; Youssef, T.; Mohamed, A. A cost-effective viable strategy for gradually transitioning Egypt’s cities into truly IOT-enabled smart cities. Int. J. Ind. Sustain. Dev. 2020, 1, 1–5. [Google Scholar] [CrossRef] [Green Version]
  84. Bhukya, K.A.; Ramasubbareddy, S.; Govinda, K.; Srinivas, T.A.S. Adaptive Mechanism for Smart Street Lighting System. In Smart Intelligent Computing and Applications; Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S., Eds.; Smart Innovation, Systems and Technologies; Springer: Singapore, 2020; Volume 160. [Google Scholar] [CrossRef]
  85. Patel, S. Role of smart meters in smart city development in India. In Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, 4–6 July 2016. [Google Scholar] [CrossRef]
  86. Fettermann, D.C.; Borriello, A.; Pellegrini, A.; Cavalcante, C.G.; Rose, J.M.; Burke, P.F. Getting smarter about household energy: The who and what of demand for smart meters. Build. Res. Inf. 2020, 49, 100–112. [Google Scholar] [CrossRef]
  87. Avancini, D.B.; Rodrigues, J.J.P.C.; Rabêlo, R.A.L.; Das, A.K.; Kozlov, S.; Solic, P. A new IoT-based smart energy meter for smart grids. Int. J. Energy Res. 2020, 45, 189–202. [Google Scholar] [CrossRef]
  88. Gupta, R.; Budhiraja, N.; Mago, S.; Mathur, S. An IoT-Based Smart Parking Framework for Smart Cities. In Data Management, Analytics and Innovation; Sharma, N., Chakrabarti, A., Balas, V., Martinovic, J., Eds.; Advances in Intelligent Systems and Computing; Springer: Singapore, 2021; Volume 1174. [Google Scholar] [CrossRef]
  89. Said, A.M.; Kamal, A.E.; Afifi, H. An intelligent parking sharing system for green and smart cities based IoT. Comput. Commun. 2021, 172, 10–18. [Google Scholar] [CrossRef]
  90. Ali, T.; Irfan, M.; Alwadie, A.S.; Glowacz, A. IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities. Arab. J. Sci. Eng. 2020, 45, 10185–10198. [Google Scholar] [CrossRef]
  91. Shah, A.A.I. A review of IoT-based smart waste level monitoring system for smart cities. Indones. J. Electr. Eng. Comput. Sci. 2021, 21, 450–456. [Google Scholar] [CrossRef]
  92. Carminati, M.; Sinha, G.R.; Mohdiwale, S.; Ullo, S.L. Miniaturized Pervasive Sensors for Indoor Health Monitoring in Smart Cities. Smart Cities 2021, 4, 146–155. [Google Scholar] [CrossRef]
  93. Carminati, M.; Turolla, A.; Mezzera, L.; Di Mauro, M.; Tizzoni, M.; Pani, G.; Zanetto, F.; Foschi, J.; Antonelli, M. A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems. Sensors 2020, 20, 1125. [Google Scholar] [CrossRef] [Green Version]
  94. Geetha, S.; Gouthami, S. Internet of things enabled real time water quality monitoring system. Smart Water 2016, 2, 1. [Google Scholar] [CrossRef]
  95. Da Xu, L.; He, W.; Li, S. Internet of Things in Industries: A Survey. IEEE Trans. Ind. Inform. 2014, 10, 2233–2243. [Google Scholar]
  96. Salles, R.S.; Ribeiro, P.F. Smart Cities, Connected World, and Internet of Things. In Software Defined Internet of Everything. Internet of Things (Technology, Communications and Computing); Aujla, G.S., Garg, S., Kaur, K., Sikdar, B., Eds.; Springer: Cham, Switzerland, 2022. [Google Scholar] [CrossRef]
  97. Yan, Z.; Zhang, P.; Vasilakos, A.V. A survey on trust management for Internet of Things. J. Netw. Comput. Appl. 2014, 42, 120–134. [Google Scholar] [CrossRef]
  98. Cho, J.-R.; Kim, H.S.; Chae, D.K.; Lim, S.J. Smart CCTV Security Service in IoT (Internet of Things) Environment. J. Digit. Contents Soc. 2017, 18, 1135–1142. [Google Scholar] [CrossRef]
  99. Losavio, M.M.; Chow, K.P.; Koltay, A.; James, J. The Internet of Things and the Smart City: Legal challenges with digital forensics, privacy, and security. Secur. Priv. 2018, 1, e23. [Google Scholar] [CrossRef] [Green Version]
  100. Chatzimichail, A. Smart Interconnected Infrastructure for Security and Safety in Public Places. In Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini, Greece, 29–31 May 2019. [Google Scholar] [CrossRef]
  101. Albán, P.; Roberto, L.; Mejía, A.; Vallejo, I.; Alban, I.; Cofre, S.; Molina, E. Public Order Disruption Event Detection Based on IoT Technology. An Approach for the Improvement of Public Security Conditions. In Developments and Advances in Defense and Security; Rocha, Á., Paredes-Calderón, M., Guarda, T., Eds.; MICRADS 2020; Smart Innovation, Systems and Technologies; Springer: Singapore, 2020; Volume 181. [Google Scholar] [CrossRef]
  102. Chatzigeorgiou, C. Increasing safety and security in public places using IoT devices. In Proceedings of the 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA, 2–16 June 2020. [Google Scholar] [CrossRef]
  103. Mehedi, S.K.T.; Shamim, A.A.M.; Miah, M.B.A. Blockchain-based security management of IoT infrastructure with Ethereum transactions. Iran J. Comput. Sci. 2019, 2, 189–195. [Google Scholar] [CrossRef]
  104. Moniruzzaman, M.; Khezr, S.; Yassine, A.; Benlamri, R. Blockchain for smart homes: Review of current trends and research challenges. Comput. Electr. Eng. 2020, 83, 106585. [Google Scholar] [CrossRef]
  105. Stavrakas, V.; Flamos, A. A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector. Energy Convers. Manag. 2020, 205, 112339. [Google Scholar] [CrossRef]
  106. Fan, Y.V.; Lee, C.T.; Lim, J.S.; Klemeš, J.J.; Le, P.T.K. Cross-disciplinary approaches towards smart, resilient and sustainable circular economy. J. Clean. Prod. 2019, 232, 1482–1491. [Google Scholar] [CrossRef]
  107. Farahani, B.; Barzegari, M.; Shams Aliee, F.; Shaik, K.A. Towards collaborative intelligent IoT eHealth: From device to fog, and cloud. Microprocess. Microsyst. 2020, 72, 102938. [Google Scholar] [CrossRef]
  108. Web Source: World Health Organization. Available online: Who.int/health-topics/coronavirus#tab=tab_1 (accessed on 2 April 2020).
  109. Ajanovic, A.; Haas, R. Economic and environmental prospects for battery electric- and fuel cell vehicles: A review. Fuel. Cell. 2019, 19, 515–529. [Google Scholar] [CrossRef] [Green Version]
  110. Chugh, A.; Jain, C.; Mishra, V.P. IoT-based multifunctional smart toy car. Lect. Notes Netw. Syst. 2020, 103, 455–461. [Google Scholar]
  111. Gao, P.; Li, J. Understanding sustainable business model: A framework and a case study of the bike-sharing industry. J. Clean. Prod. 2020, 267, 122229. [Google Scholar] [CrossRef]
  112. Nižetić, S. Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. J. Clean. Prod. 2020, 274, 122877. [Google Scholar] [CrossRef]
  113. Villarrubia, G.; Bajo, J.; de Paz, J.F.; Corchado, J.M. Monitoring and detection platform to prevent anomalous situations in home care. Sensors 2014, 14, 9900–9921. [Google Scholar] [CrossRef] [Green Version]
  114. Asha, P. IoT enabled environmental toxicology for air pollution monitoring using AI techniques. Environ. Res. 2022, 205. [Google Scholar] [CrossRef]
  115. Bolobonov, D.; Frolov, A.; Borremans, A.; Schuur, P. Managing Public Transport Safety Using Digital Technologies. Transp. Res. Procedia 2021, 54, 862–870. [Google Scholar] [CrossRef]
  116. Mounce, S.R. Data Science Trends and Opportunities for Smart Water Utilities. In ICT for Smart Water Systems: Measurements and Data Science; Scozzari, A., Mounce, S., Han, D., Soldovieri, F., Solomatine, D., Eds.; The Handbook of Environmental Chemistry; Springer: Cham, Switzerland, 2020; Volume 102. [Google Scholar] [CrossRef]
  117. Mamoona, H. Energy Optimization for Smart Cities Using IoT. Appl. Artif. Intell. 2022. [Google Scholar] [CrossRef]
  118. Wang, C.; Qin, J.; Qu, C.; Ran, X.; Liu, C.; Chen, B. A smart municipal waste management system based on deep-learning and Internet of Things. Waste Manag. 2021, 135, 20–29. [Google Scholar] [CrossRef]
  119. Ghasan, F.H. A review on 5G technology for smart energy management and smart buildings in Singapore. Energy AI 2022, 7. [Google Scholar] [CrossRef]
  120. Alessandro, F. A Social IoT-based platform for the deployment of a smart parking solution. Comput. Netw. 2022, 205. [Google Scholar] [CrossRef]
  121. Armin, R. Development of Sustainable Energy Use with Attention to Fruitful Policy. Sustainability 2021, 13, 13840. [Google Scholar] [CrossRef]
  122. Armin, R. Development of smart energy systems for communities: Technologies, policies and applications. Energy 2022. [Google Scholar] [CrossRef]
Figure 1. The methodology flowchart.
Figure 1. The methodology flowchart.
Applsci 12 02672 g001
Table 1. The most important barriers related to SC development based on the IoT and the appropriate solutions.
Table 1. The most important barriers related to SC development based on the IoT and the appropriate solutions.
SectorsBarriersSolutionReferences
Public transportMore CO2 emissions due to increased private cars, increase in noise from private cars in cities, lack of monitoring patterns of transport use by citizens, absence or low use of monitoring systems, lack of safety and efficiency in roads, congestion and traffic, sudden accidents, and defective roadsUtilization of proper patterns and use of monitoring sensors, utilization of different types of sensors to accurately monitor roads and improve GPS systems using data from drivers’ smartphones, and improvement in the quality of roads[29,31,34,36,76,77,78,79,80]
Street lightingLack of sensor-equipped street lights and defective lightsStreetlights with sensors and establishment of a connection between the sensors and cloud management, utilization of a monitoring-system switch to scan conditions and send signals to increase or dim the lights, and use of new lights (low consumption)[43,44,45,46,81,82,83,84]
UtilitiesExcessive consumption, extra expenses for fuel and electricity for which there is no need, lack of or improper use of smart meters and smart billing, shortage of revealing consumption patterns, and limited remote monitoring for citizensT-equipped smart-connected meters, proper consumption patterns, and management services to improve the quality of the services[46,85,86,87]
Smart parkingLack of or limited smart parking options for drivers, improper parking of cars on the street, and reduction in street width due to trafficUtilization of GPS data from drivers’ smartphones or road-surface sensors embedded in the ground on parking spots[58,59,60,88,89]
Waste managementRelease of garbage and the resultant unpleasant odor in cities due to absence of accurate systems to monitor the proper time to collect waste in order to prevent fuel losses and empty containersInstallation of a sensor on waste containers to optimize waste-collection schedules by tracking waste levels[90,91]
EnvironmentIncreased CO2 emissions and threats public health and lack of water and air quality monitoringUtilization of different types of sensors such as water sensors and air sensors to improve and provide more accurate monitoring[24,25,26,27,28,92,93,94]
Public safetyWeak security of public safety in cities, increase in crimes such as robbery, lack of ethics regarding law and regulatory rights, and weak infrastructureUtilization of IoT technologies such as CCTV cameras and acoustic sensors in different areas of cities, blockchain-based security management of IoT infrastructure for maintaining security and privacy, improvement of interoperability, leading to vendor lock-in, and control of corruption[95,96,97,98,99,100,101,102,103]
Table 2. A comparison of the results of this work and those of previous work.
Table 2. A comparison of the results of this work and those of previous work.
Present WorkTypes of Consideration in Other WorkReference
EnvironmentReviews of IoT-based environment monitoring systems[114]
Public transportManaging the public transport systems through applying digital technologies[115]
UtilitiesInvestigation of the role of digitalization for smart water networks and improvement by the IoT, artificial intelligence, blockchain, and other novel technologies[116]
Street lightingInvestigation of the street lighting framework using IoT systems to reduce cost and energy consumption[117]
Waste managementProposal of a proof-of-concept municipal waste management system to reduce the cost of waste classification, monitoring, and collection using the IoT[118]
Public safetyIncrease in public safety against theft using IoT systems[119]
Smart parkingInvestigation the role the Internet of Things (IoT) in overcoming the challenges of parking cars. Presentation of smart parking solutions[120]
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Razmjoo, A.; Gandomi, A.; Mahlooji, M.; Astiaso Garcia, D.; Mirjalili, S.; Rezvani, A.; Ahmadzadeh, S.; Memon, S. An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application. Appl. Sci. 2022, 12, 2672. https://doi.org/10.3390/app12052672

AMA Style

Razmjoo A, Gandomi A, Mahlooji M, Astiaso Garcia D, Mirjalili S, Rezvani A, Ahmadzadeh S, Memon S. An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application. Applied Sciences. 2022; 12(5):2672. https://doi.org/10.3390/app12052672

Chicago/Turabian Style

Razmjoo, Armin, Amirhossein Gandomi, Maral Mahlooji, Davide Astiaso Garcia, Seyedali Mirjalili, Alireza Rezvani, Sahar Ahmadzadeh, and Saim Memon. 2022. "An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application" Applied Sciences 12, no. 5: 2672. https://doi.org/10.3390/app12052672

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