By: Arya Brijith, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, arya.brijithk@gmail.com
Abstract
As urbanization prevails, the idea of smart city arose as a result to tackle the complex challenges of modern urban living. Smart metropolises harness advanced technologies, data analytics, and artificial intelligence (AI) to produce connected systems that enhance quality of life, optimize resource allocation, and promote sustainability. This composition explores how AI acts as a catalyst in the enhancement of smart city and revolutionizing the civic governance. By probing into real-world operations of AI in areas like renewable energy, public safety, transportation, and smart structure, we witness the disruptive eventuality of AI in reshaping civic geographies.
Introduction
By 2050, it is anticipated that two-thirds of the world’s population would live in cities. Thus, the demand for a smart city is increasing. Smart cities are distinguished by their linked, integrated systems that improve citizens’ quality of life by making the best use of resources, and encourage sustainability. In a smart city, sensors are placed throughout the area to collect information about various aspects of the city, such as transportation, healthcare, and the environment. This information is then sent to a central server for analysis or processed locally at edge devices to produce insightful data using artificial intelligence (AI) methods.[8]
AI systems help smart cities become more effective, sustainable, and sensitive to the demands of their inhabitants, ultimately resulting in urban settings that are more reliable and inclusive. Smart city initiatives are being developed and put into action thanks to artificial intelligence (AI), which has become a potent catalyst. Its incorporation into urban planning and administration has completely changed how cities operate, providing a wealth of advantages and opportunities.
How it is a disruptive revolution
The distribution of resources, including those for energy, transportation, and public services, is optimized by AI. Significant efficiencies result from this, cutting waste and expenses. For instance, traffic management programs maximize circulation, cutting down on pollutants and fuel use. Further, it can assist real-time security threat detection and response in video analytics and surveillance systems.
Accessibility for people with impairments can be improved through AI technology as technology for voice recognition and visual aids provide more inclusive services and facilities. Similarly, simulations and modelling tools powered by AI enable more accurate urban planning and design which results in the development of urban places that are enhanced. It opens prospects in newly developing domains involving the use of AI.
AI-powered solutions
Renewable Energy (RE) [1]: The energy industry is undergoing enormous transformations that will significantly affect its capacity to grow and adapt. The integration of cutting-edge shaft technologies, which involve varying energy eventualities, huge data sets, two-way energy flow, and an urgent need to improve the functioning of energy reserves, are driving its significant modifications.
AI can identify recurring, cyclical models and patterns, describe inconsistencies in process stages, read the trends for both energy product and energy demand, reduce or remove the imbalances in demand and force caused by the variation in shaft, help power outages by optimizing the demand and force within the smart grids, increase energy effectiveness, and more through visualization, simulation, and operation opinions based on deep analysis of large data sets.[8]
Public Safety: Smart cities are accountable for saving lives and decreasing crime in addition to saving energy and eliminating locks. The resources’ extensive data collection can help find locations where accidents occur frequently, pinpoint the causes, and assist with prevention in the future. Gunshot detection sensors, video surveillance and analytics, drones, and cybersecurity are some of the methods that might be used to protect the safety of the residents of a smart city.[3]
Controlled traffic and public transportation: AI-powered traffic monitoring and optimization systems regulate traffic flow, ease congestion, and boost transportation effectiveness by analysing real-time data from cameras, sensors, and GPS devices. The system can identify traffic congestion, parking offenses, and weather reports and inform city officials in real-time using a mix of machine learning and picture recognition [2]. Artificial intelligence systems offer real-time traffic and travel information, including transit routes and timetables, navigational instructions, and details regarding delays brought on by traffic jams, accidents, bad weather, or road maintenance. [4].
Smart parking: According to reports, an average driver in India spends 2.02 days per year in traffic jams due to poor urban design and traffic management. AI can be a helpful tool for transportation system design, building, maintenance, and time scheduling. It may be used to create models that solve complicated transport system issues requiring large amounts of data to analyse traffic demands and analysis pedestrian behaviour.[4]
Mitigations of Industry 4.0
Industry 4.0 (also called the Fourth Industrial Revolution or 4IR) is the upcoming phase in the field of digitization. It includes the manufacturing sector, driven by disruptive trends, the rise of data and connectivity, analytics, mortal-machine commerce, and advancements in robotics. Smart factories that use high-tech IoT bias have increased production and improved quality. Artificial intelligence-powered visual perception can minimize industrial crimes and save time and money for the wealthy by replacing manual examination business methods. All categories of industrial businesses, including discrete and process manufacturing, as well as oil painting and gas, mining, and other industrial elements, can use Industry 4.0 generalizations and technology. Nevertheless, there are many challenges associated with the same, they are as follows:
- To provide accessibility (24/7) Gadgets should have a long battery life and basic machine learning models should be launched.[5]
- Several tools might not be authentic or authorized.
Entities like intelligent agents, sensing nodes, IoT sensor nodes, and machines participating in smart city systems must undergo authentication and authorization processes. This is crucial to prevent any potential misuse or exposure of the AI model to malicious actors.[6]
- Transparency
Prior to training models to tackle smart city challenges, it’s essential to consider what is being pursued and whether there exists a genuine requirement for a black-box model [5]. Black box AI refers to a system or model that produces a vaticination without furnishing a clear understanding of how it arrived at that result. It means that the internal workings of the system are not fluently interpretable or resolvable by humans.
- Maintenance
Applications for smart cities require all the smart city stakeholders to be regularly monitored and maintained utilizing audit-log management and preventive maintenance techniques.
The success of Barcelona: Smart City
Barcelona is a success story in terms of urbanization. Even in the aftermath of the 2008 downturn, Barcelona managed to hold its ground as one of the leading cities in Europe. As Spain’s second-largest city, Barcelona has been experiencing growth and a remarkable transformation, positioning itself as a city focused on knowledge-intensive industries [7].
Barcelona’s journey toward becoming a smart city is a remarkable tale of civic invention and sustainability. By using slice-edge technology, the city has readdressed civic living, setting an illustration for cosmopolises around the globe. Smart transportation results, including an intertwined mobility platform and a bike-sharing program, give residents flawless and Eco-friendly trip options. Also, Barcelona’s investment in digital structure ensures high-speed internet access throughout the city.
These advancements, coupled with intelligent road lighting, optimized waste operation, and a strong focus on green spaces, have not only increased the overall quality of life for the residents but also propelled Barcelona into the transnational limelight as a model smart megacity. The megacity’s transformation stands as a testament to the eventuality of technology in creating further sustainable, effective, and inhabitable civic surroundings.
Conclusion
Barcelona serves as a case study of urban development success, demonstrating how a city may develop into a smart metropolis via thoughtful technology integration. It has improved the quality of life for its citizens with cutting-edge ideas like superblocks as well as sophisticated solutions in transportation and garbage management. This change is a prime example of how AI is accelerating the transition to smart cities. AI is not only transforming cities but also reinventing the future of urban living globally by enhancing the agility, data-driven, and efficiency of urban government. Barcelona’s experience serves as motivation, showing that cities may improve their sustainability, effectiveness, and likability for future generations with the correct technology basis and forward-thinking efforts.
References
- A. C. Şerban and M. D. Lytras, “Artificial Intelligence for Smart Renewable Energy Sector in Europe—Smart Energy Infrastructures for Next Generation Smart Cities,” in IEEE Access, vol. 8, pp. 77364-77377, 2020, .
- P. J. Navarathna and V. P. Malagi, “Artificial Intelligence in Smart City Analysis,” 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2018, pp. 44-47, .
- Swetha Srivastava, Aditya Bisht and Neetu Narayan, “Safety and security in smart cities using artificial intelligence”, 2017 7th International Conference on Cloud Computing Data Science & Engineering – Confluence.
- Agarwal, P. K., Gurjar, J., Agarwal, A. K., & Birla, R. (2015). Application of artificial intelligence for development of intelligent transport system in smart cities. Journal of Traffic and Transportation Engineering, 1(1), 20-30.
- Javed, Abdul Rehman, Waqas Ahmed, Sharnil Pandya, Praveen Kumar Reddy Maddikunta, Mamoun Alazab, and Thippa Reddy Gadekallu. “A survey of explainable artificial intelligence for smart cities.” Electronics 12, no. 4 (2023): 1020.
- Gadekallu, T.R.; Manoj, M.; Kumar, N.; Hakak, S.; Bhattacharya, S. Blockchain-Based Attack Detection on Machine Learning Algorithms for IoT-Based e-Health Applications. IEEE Internet Things Mag. 2021, 4, 30–33.
- Bakici, Tuba & Almirall, Esteve & Wareham, Jonathan. (2012). A Smart City Initiative: The Case of Barcelona. Journal of the Knowledge Economy. 4. 10.1007/s13132-012-0084-9.
- Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022). Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Computer Science Review, 43, 100452.
Cite As
Brijith A. (2023) Smart City: An AI-based disruptive revolution, Insights2Techinfo, pp.1