By 2050, the global urban population is estimated to soar to around 70 percent. This dramatic urbanization surge will profoundly impact cities, encompassing their environment, governance, and security. To effectively manage this meteoric urban growth, many nations have embraced the concept of smart cities, aiming to optimize resource utilization and energy efficiency.
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Objectives of Smart Cities
Smart city initiatives are poised to address environmental concerns by fostering the adoption of low-carbon emission technologies, with countries such as the United States, European Union, and Japan leading the way. To meet the demands of a smart city, the efficient deployment of information and communication technologies (ICTs) is imperative. ICTs are vital for managing data analysis, communication, and the seamless implementation of complex strategies, ensuring the smooth and secure functioning of smart cities.
AI’s Influence
The Internet of Things (IoT) is a pivotal element within numerous smart city applications, generating vast and complex datasets. Handling such colossal data requires advanced techniques such as artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL) to make optimal decisions. These techniques consider long-term objectives and can lead to near-optimal control decisions. Enhancing their accuracy and precision necessitates augmenting training data to bolster their learning capabilities, thereby improving automated decision efficiency.
AI in Daily Life
The inception of smart city concepts and the utilization of advanced data analysis techniques for big data have coincided. Smart cities, IoT, blockchain, unmanned aerial vehicles (UAVs), and the incorporation of AI, ML, and DRL-based techniques in various applications remain in an evolutionary phase, offering further opportunities in the future. In smart city projects, diverse sectors such as intelligent transportation, cybersecurity, smart grids (SGs), and UAV-assisted next-generation communication (5G and B5G) play crucial roles. Big data analytics and the effective use of AI, ML, and DRL-based techniques enhance the efficiency and scalability of these sectors in smart city initiatives. Modern Intelligent Transportation Systems (ITS), for instance, rely heavily on ML and DRL-based techniques to achieve goals like self-driving vehicles, connected vehicle security, efficient passenger management, and safe travel.
AI, ML, and DRL-based techniques are pivotal in reinforcing the security plane of smart cities and profoundly impact various sectors. Energy generation, management, and consumption are pivotal features of smart cities, with big data analytics significantly influencing ICT-based SG operations.
The ever-increasing influence of AI is reshaping our daily lives, impacting traditional job roles and human interactions with the environment. Effective regulations are crucial to harnessing the positive impacts of AI while mitigating its negative aspects. For instance, an efficient crime detection system based on DRL and neural networks can identify and analyze criminal activities. Additionally, an ML-based architecture was developed for predicting and responding to incidents before they occur.
Advantages and Disadvantages of Smart Cities
Smart city initiatives garner attention for their potential to elevate residents' quality of life, foster economic growth, and tackle environmental issues. However, they entail advantages and disadvantages that necessitate vigilant management to optimize benefits and mitigate drawbacks.
A primary advantage lies in an improved quality of life, as technology enhances public safety, transportation, and access to services. This results in convenience, shorter commutes, and heightened well-being. Furthermore, these initiatives stimulate economic growth by attracting businesses, generating employment, and fueling innovation. Efficiency gains translate to cost savings for businesses and residents, fostering economic expansion. Promoting sustainability is another potential benefit, with smart cities encouraging renewable energy use and waste reduction.
On the other hand, privacy and security concerns rise as more data is collected, risking breaches and erosion of public trust. Technical challenges such as standardization and interoperability hinder smart city implementation. Fragmentation arises from diverse technologies, necessitating standardized interfaces and data models. Financial and legal obstacles, including funding and regulatory complexities, add complexity. Innovative financing models and legal expertise are vital. Social and cultural factors, such as resistance to change and privacy concerns, slow adoption. Addressing these demands requires trust-building and addressing privacy fears. Efficiency gains from AI are presumed beneficial but can be counterproductive due to increased resource demand. Machine learning applications can perpetuate outdated urban models. Promoting multifunctional, sustainable cities is crucial for a more accessible and eco-friendly urban future.
To address these issues effectively, it relies on meticulous planning, active stakeholder involvement, and continuous evaluation to optimize benefits and mitigate negative impacts. The potential for trade-offs underscores the importance of careful management to realize the full potential of smart city initiatives while minimizing adverse effects.
Equitable and Sustainable AI Integration in Smart Cities
Smart cities aim to engage citizens in urban governance, yet AI-focused on this goal relies heavily on data generated by the technologically privileged. These risks reinforcing existing power imbalances benefit those already well-connected, affluent, able-bodied, and educated.
Smart cities should not merely enhance current power dynamics and wealth accumulation but instead aim for social equity and sustainability. Digital data collection depends on energy-intensive infrastructure, often overlooked by high-tech urbanism.
To create a truly transformative AI for smart cities, interdisciplinary collaboration is essential. Insights from science and technology studies, ecological economics, decolonial theory, feminist urbanism, and critical urban studies can guide AI scholars. They encourage critical self-reflection, socio-ecological regenerativity, diverse perspectives, circular urban metabolisms, stakeholder engagement, and questioning techno-optimism.
Efforts should prioritize regenerative urban models that minimize social and ecological externalities while involving marginalized communities as co-producers of urban solutions. Additionally, considerations must extend to repair and maintenance costs, not just unlimited growth.
ITS in Smart Cities
The ITS stands as an amalgamation of advanced sensors, control systems, and ICT, culminating in the generation of extensive datasets that profoundly influence the future of the ITS and the broader notion of smart cities. In this paradigm, AI, ML, and DRL techniques have assumed a pivotal role in the precise monitoring and real-time estimation of traffic flow data within urban environments—an indispensable facet of sustainable ITS.
Embracing the era of UAVs, these versatile aircraft find many applications in ITS within smart cities. Notably, ML and DRL techniques contribute significantly to optimizing UAV trajectories, energy consumption, and efficiency.
In essence, AI, ML, and DRL are becoming the bedrock of ITS and smart cities, fueling innovations across diverse domains, from traffic management to security and resource optimization, underscoring the transformative potential of these technologies.
Challenges and Opportunities in AI and Smart Cities
AI applications within smart cities exhibit promising outcomes, as discerned from the existing literature. Nonetheless, academia and industry experts can hone their focus on salient research avenues to harness these approaches for augmenting smart city efficiency:
Enhanced Data Training: To bolster precision in decision-making, a comprehensive dataset encompassing variables such as vehicle speed, position, inter-vehicle spacing, driver behavior, UAV altitude, and relay base station data is imperative for robust ML and DRL protocol training.
UAV Optimization: Jointly optimizing UAV onboard capabilities such as caching, computing, sensing, and trajectories through ML techniques can significantly elevate ITS efficiency in UAV-vehicle communications.
Channel Modeling: Rigorous measurement campaigns are essential for modeling vehicle-to-vehicle, vehicle-to-UAV, UAV-to-UAV, and UAV-to-ground base station channels. These campaigns should encompass diverse UAV and vehicle velocities in various directions, accounting for regular and irregular infrastructure shapes.
Standardization in SGs: Standardization is pivotal for big data development in SGs and interoperability among SG devices. The judicious selection of AI, ML, and DRL techniques can optimize SG performance to near-optimal levels.
5G Integration: With 5G technology slated for standardization in 2020, commensurate standardization of SG communication infrastructure becomes imperative for seamless interoperability between existing SGs and the forthcoming 5G technology.
Power Management: Optimization of power-down scenarios is paramount for SGs and electric companies. Analyzing communication delays is crucial to engineering an efficient network. ML and DRL techniques can formulate strategies for facilitating smooth transitions between 5G communication technologies while ensuring an uninterrupted power supply.
References and Further Readings
Gracias, Jose Sanchez, Gregory S. Parnell, Eric Specking, Edward A. Pohl, and Randy Buchanan. 2023. "Smart Cities-A Structured Literature Review" Smart Cities 6, no. 4: 1719-1743. DOI: https://doi.org/10.3390/smartcities6040080
Daniela Inclezan, Luis l. Pradanos. (2017). Viewpoint: Acritical View on Smart Cities and AI. Journal of Artificial Intelligence Research, 60. DOI: https://doi.org/10.1613/jair.5660
Zaib Ullah, Fadi Al-Turjman, Leonardo Mostarda, Roberto Gagliardi, (2020). Applications of Artificial Intelligence and Machine learning in smart cities. Computer Communications, 154: 313-323. DOI: https://doi.org/10.1016/j.comcom.2020.02.069