The escalating energy demands of rapidly urbanizing populations pose a significant challenge to the existing Power Distribution (PD) infrastructure. Zanella et al. [1] Traditional power grids, designed for unidirectional energy flow and static loads, struggle to accommodate the dynamic consumption patterns and integration of renewable energy sources characteristic of modern Smart Cities (SCs). Zanella et al. [1]. This argument necessitates a paradigm shift towards intelligent PD systems capable of optimizing energy consumption, enhancing grid stability, and promoting sustainability.

Internet of Things (IoT)-enabled smart city systems offer a transformative solution to address these challenges. By leveraging a network of interconnected sensors, smart meters, and intelligent controllers, real-time data on power consumption, grid conditions, and environmental factors can be collected and analyzed. This data-driven approach enables dynamic adjustments to PD based on demand patterns, renewable energy generation, and grid stability requirements [1].

This research delves into the architecture, technologies, and benefits of integrating IoT devices and intelligent algorithms within the PD infrastructure of SC. The study investigates the role of Machine Learning (ML) in predictive maintenance, fault detection, and load forecasting to minimize outages and improve operational efficiency. Zanella et al. [1] Furthermore, the research explores the potential of IoT-enabled systems to integrate renewable energy sources seamlessly and promote efficient energy consumption practices among consumers [2].

The findings of this research are expected to highlight significant improvements in Energy Efficiency (EE), reduced peak demand, and enhanced grid resilience. The implications extend to the development of sustainable and intelligent urban ecosystems, paving the way for a greener future.

A substantial body of research has explored the potential of smart technologies to revolutionize urban infrastructure and enhance EE [3], [4]. Stojkoska and Trivodaliev [5] Notably, the IoT has emerged as a key enabler of Smart Grids (SGs), offering the capability to collect and analyze real-time data from various sources within the PD network. Bellini et al. [6]. This data-driven approach forms the foundation for optimizing energy consumption, improving grid stability, and facilitating the integration of renewable energy sources. Several studies have investigated the application of IoT devices and intelligent algorithms in SGs [7]. For instance, researchers have demonstrated the effectiveness of smart meters in monitoring and controlling energy consumption at the household and building levels. [8] These smart meters provide real-time data on energy usage patterns, enabling consumers to make informed decisions about their energy consumption and facilitating demand-side management strategies.

Furthermore, the integration of IoT sensors within the PD infrastructure has shown significant promise in enhancing grid stability and resilience. [8] By monitoring grid conditions, such as voltage levels and current flows, these sensors can detect anomalies and potential faults in real-time. This capability enables proactive maintenance and rapid response to grid disturbances, minimizing outages and improving overall operational efficiency.

In addition to IoT, Artificial Intelligence (AI) and ML techniques have been increasingly employed to optimize PD and energy management in SC. AI-powered algorithms can analyze historical and real-time data to predict future energy demands, optimize load balancing, and facilitate the integration of renewable energy sources. This predictive capability enables proactive adjustments to PD, ensuring grid stability and maximizing the utilization of renewable energy.

The convergence of IoT, AI, and ML has paved the way for the development of intelligent energy management systems capable of dynamically adapting to changing energy demands and grid conditions. These systems hold the potential to transform PD in SC, promoting EE, sustainability, and enhanced grid resilience. Shabani and his colleagues conducted a different study that examined the relationship between the personalities and types of abilities of employees working in government organizations in a particular city with the universe, including the aforementioned employees. A sample of 322 employees was selected and the relevant questionnaire was randomly distributed among them. This paper identifies a relationship between personality types and employees' abilities and there is a relationship between introverts, extroverts, sensitive ... This type of attitude for people in cities can also be considered for the IoT and the electricity system [9].

This research employs a mixed-methods approach combining case study analysis, simulation modeling, and quantitative data analysis to investigate the effectiveness of IoT-enabled smart city systems for efficient PD.

This illustration depicts a smart street lighting system designed to optimize energy consumption. Each streetlight is equipped with a Smart Grid Module (SGM), containing sensors, communication modules, and a processing unit. These SGMs allow the streetlights to collect environmental data, communicate with each other, and respond to central commands.

The streetlights use sensors to gather information about their surroundings, including motion, ambient light levels, and potentially even environmental factors like temperature and humidity. They communicate wirelessly using ZigBee and a 3G network, enabling them to share data and coordinate actions. For instance, if one streetlight detects motion, it can signal neighboring lights to increase brightness. A central web server acts as the control point for the system, receiving data from the streetlights, analyzing it, and sending commands back to adjust lighting levels. Authorized users can access the web server through a web client, allowing for remote monitoring and management of the streetlights, including adjusting settings and receiving alerts. This system offers several advantages: Energy savings through dynamic lighting adjustments, reduced costs due to lower energy consumption, improved safety by providing lighting only when and where needed, and enhanced efficiency and flexibility through remote monitoring and control.

Government E-government systems; online transactions; city operating systems; performance management systems; urban dashboards Security and Centralised control rooms; digital surveillance; predictive policing; coordinated emergency emergency services response Transport Intelligent transport systems; integrated ticketing; smart travel cards; bikeshare; real-time passenger information; smart parking; logistics management; transport apps Energy SGs; smart meters; energy usage apps; smart lighting Waste Compactor bins and dynamic routing/collection Environment Sensor networks (e.g., pollution, noise, weather; land movement; flood management) Buildings Building management systems; sensor networks Homes Smart meters; app-controlled smart appliances

This table illustrates how smart technologies are being integrated into various sectors of a smart city to enhance efficiency, sustainability, and quality of life. In the realm of government, technology facilitates e-government systems, online transactions, city operating systems, performance management tools, and urban dashboards. Security and emergency services benefit from centralized control rooms, digital surveillance systems, predictive policing capabilities, and coordinated emergency response mechanisms. Transportation is optimized through intelligent transport systems, integrated ticketing, smart travel cards, bikeshare programs, real-time passenger information, smart parking systems, logistics management, and transport apps.

Energy management is improved through SGs, smart meters, energy usage apps, and smart lighting. Waste management is made more efficient with compactor bins and dynamic routing/collection. Environmental monitoring and protection are enhanced by sensor networks and flood management systems. Buildings become smarter and more sustainable with building management systems and sensor networks. Homes are transformed with smart meters and app-controlled appliances. Overall, this table paints a picture of a technologically advanced city that leverages innovation to improve various aspects of urban living.

I. High initial costs: Implementing smart street lighting systems involves significant upfront investment in sensors, communication networks, and intelligent control systems.

II. Data security and privacy: Collecting and transmitting data from streetlights raises concerns about data security and privacy. Protecting sensitive information from unauthorized access and ensuring compliance with privacy regulations are crucial challenges.

III. Interoperability and standardization: The lack of standardized protocols and interoperability between different vendors' equipment can hinder the seamless integration and scalability of smart street lighting systems. IV. Maintenance and reliability: Maintaining the complex network of sensors, communication modules, and intelligent controllers requires specialized expertise and can be challenging. Ensuring the reliability and longevity of the system is crucial for long-term cost-effectiveness [11].

Cost reduction: Explore strategies to reduce the initial investment cost of smart street lighting systems, such as adopting open-source technologies and developing cost-effective sensors and communication modules. Enhanced security measures: Implement robust security measures to protect data from cyberattacks and ensure privacy. This argument includes encryption, access controls, and regular security audits.

Standardization efforts: Promote the development of industry standards and protocols to ensure interoperability between different vendors' equipment and facilitate seamless integration.

SG integration: Integrate smart street lighting systems with SGs to optimize energy consumption, enable demand response, and improve grid stability.

Advanced analytics: Utilize advanced analytics and ML to analyze data collected from streetlights and identify patterns, optimize lighting schedules, and predict maintenance needs.

Public-private partnerships: Foster collaboration between public and private sectors to share the costs and expertise needed for implementing smart city projects.

Community engagement: Involve local communities in the planning and implementation of smart city initiatives to ensure that they address the needs and priorities of residents.

Open data platforms: Develop open data platforms to make data collected from smart city systems accessible to the public, fostering transparency, innovation, and citizen engagement.

Cybersecurity standards: Establish robust cybersecurity standards and protocols to protect smart city systems and data from cyberattacks and ensure the privacy of citizens' data.

Digital literacy programs: Implement digital literacy programs to educate citizens about smart city technologies and empower them to participate in the digital economy.

Sustainability focus: Prioritize sustainability in smart city development, focusing on EE, renewable energy integration, and reducing the environmental impact of urban activities.

Scalability and flexibility: Design smart city systems with scalability and flexibility in mind to accommodate future growth, technological advancements, and changing urban needs.

Interdisciplinary collaboration: Encourage collaboration between different disciplines, such as urban planning, engineering, computer science, and social science, to develop holistic and integrated smart city solutions [12].

This illustration showcases the diverse components that work together to form a smart city ecosystem. It emphasizes the interconnected nature of these elements and how they contribute to creating a more efficient, sustainable, and citizen-centric urban environment.

Let's break down each component:

I. Smart government: Symbolized by a government building, this signifies the use of technology to improve government services, increase transparency, and encourage citizen participation. This could include:

II. E-government systems: Online platforms for accessing government services, information, and completing transactions.

III. City operating systems: Integrated systems for managing city infrastructure and services.

IV. Performance management systems: Tools for tracking and evaluating the effectiveness of city services. V. Urban dashboards: Visual displays of key city data for better decision-making.

VI. Smart health: Represented by a heart icon, this signifies the integration of technology in healthcare services to improve accessibility, efficiency, and patient care.

This could involve:

I. Telemedicine: Remote consultations and diagnoses.

II. Remote monitoring: Tracking patient health data remotely.

III. Electronic health records: Digitalized health records for efficient information management.

IV. Data analysis for public health management: Using data to track and manage public health trends.

V. Smart farming/agriculture: Illustrated with fields and a hot air balloon (possibly symbolizing drones for aerial monitoring), this highlights the use of technology in agriculture to optimize production, reduce resource consumption, and enhance food security.

This could include:

I. Precision farming techniques: Using sensors and data to optimize resource usage in agriculture.

II. Sensor-based monitoring: Monitoring crop conditions and environmental factors.

III. Data-driven decision-making for crop management: Using data to optimize crop yields and resource allocation.

IV. SG/energy/utilities: Represented by power lines and an electric plug, this emphasizes the modernization of energy infrastructure for efficient generation, distribution, and consumption of energy.

This could involve:

I. SGs: Modernizing the electricity grid to improve efficiency, reliability, and integrate renewable energy.

II. Smart meters: Devices that provide real-time data on energy consumption.

III. Energy usage apps: Applications that help users monitor and manage their energy consumption.

IV. Smart lighting: Energy-efficient lighting systems that adjust brightness based on conditions.

V. Smart buildings: Illustrated by modern buildings, this signifies the use of technology to optimize building performance, EE, and occupant comfort.

This could include:

I. Building management systems: Systems that control and monitor building functions like lighting, heating, and security.

II. Sensor networks: Using sensors to optimize building performance and resource usage.

III. Smart manufacturing: Represented by a factory, this highlights the integration of technology in manufacturing processes to improve efficiency, productivity, and sustainability.

This could involve:

I. Automation: Automating tasks in manufacturing processes.

II. Robotics: Using robots for increased efficiency and precision.

III. Data analytics: Analyzing data to optimize manufacturing processes.

IV. IoT: Using IoT for real-time monitoring and optimization of manufacturing processes.

V. Smart transportation: Illustrated by various modes of transportation, including a train, cars, and bicycles, this emphasizes the use of technology to optimize transportation networks, reduce congestion, and improve safety.

This could involve:

I. Intelligent transport systems: Using sensors and data analysis to manage traffic flow and optimize transportation networks.

II. Integrated public transport systems: Creating seamless and efficient public transportation systems.

III. Smart/digital citizens: Represented by people holding digital devices, this signifies the active participation of citizens in the smart city ecosystem.

This could involve:

I. Using technology for communication and access to information: Facilitating communication and access to information for citizens.

II. Utilizing e-government services: Encouraging the use of online government services.

III. Contributing to data collection for urban improvement: Engaging citizens in data collection initiatives for urban planning and improvement.

IV. Mobility/Wi-Fi: This element highlights the importance of wireless connectivity as a foundation for a smart city. Pervasive Wi-Fi access enables communication between devices, data transfer, and access to information and services.

V. Open data: This component emphasizes the importance of open and accessible data for citizens, businesses, and researchers. Open data initiatives promote transparency, innovation, and data-driven decision-making for urban development.

This image effectively captures the interconnectedness of these different components in a smart city. It highlights how technology can be leveraged to address various urban challenges and create a more efficient, sustainable, and citizen-centric urban environment.

The convergence of AI and IoT in SC has ushered in a new era of enhanced efficiency, data-driven decision-making, and improved urban living. This integration empowers cities to optimize resource management, enhance sustainability, and better serve their residents.

AI algorithms play a crucial role in analyzing the vast amounts of data generated by IoT devices deployed throughout the city. These algorithms can identify patterns, predict trends, and make intelligent decisions in real-time, leading to optimized energy consumption, improved traffic flow, enhanced public safety, and more efficient city services.

For instance, in the realm of SGs, AI algorithms can predict energy demand, optimize energy distribution, and integrate renewable energy sources seamlessly. This leads to reduced energy consumption, lower costs, and a decreased carbon footprint. In transportation management, AI can analyze traffic patterns to optimize traffic flow, reduce congestion, and improve road safety.

Furthermore, AI and IoT integration enhances public safety through real-time crime prediction and prevention, efficient emergency response, and improved disaster management. In healthcare, AI-powered systems can analyze patient data to provide personalized medicine, predict outbreaks, and improve overall healthcare quality.

The integration of AI and IoT in SC also fosters greater citizen engagement by providing platforms for citizens to report issues, provide feedback, and participate in the decision-making process. This creates a more transparent, responsive, and citizen-centric urban environment. Overall, the synergy between AI and IoT is transforming cities into intelligent, efficient, and sustainable ecosystems that prioritize the well-being of their residents.

Each category is assessed based on a percentage scale, presumably derived from surveys, data analysis, or expert evaluations. The categories include:

Standard of living: This likely encompasses factors such as income levels, housing quality, access to healthcare, and education.

Access to information: This refers to the availability and accessibility of Information And Communication Technologies (ICTs) for citizens, including internet access, digital literacy, and online services.

Dealing with official matters: This likely measures the efficiency and ease of interacting with government agencies and accessing public services.

Care for the environment: This assesses the efforts and initiatives towards environmental sustainability, such as waste management, pollution control, and renewable energy adoption.

Transport/communication: This evaluates the quality and efficiency of transportation and communication infrastructure, including public transport, road networks, and digital connectivity.

Safety: This likely measures the level of safety and security in the urban environment, including crime rates, emergency response capabilities, and disaster preparedness.

EE: This assesses the EE of buildings, infrastructure, and overall energy consumption patterns.

Enterprises/buildings: This likely evaluates the adoption of smart technologies and sustainable practices in businesses and buildings.

Infrastructure: This refers to the quality and modernity of overall urban infrastructure, including transportation networks, utilities, and public facilities.

The graph indicates that Italy generally scores higher in categories like "Standard of Living," "Access to Information," and "Transport/Communication," while Poland shows higher scores in "Dealing with Official Matters" and "Care for the Environment." Portugal appears to have relatively lower scores across most categories. The average value provides a benchmark for comparison.

This figure provides insights into the relative strengths and weaknesses of different countries in their journey towards becoming "smarter" cities. It highlights areas where countries excel and areas where improvements are needed, offering valuable information for policymakers and urban planners to guide their strategies for sustainable urban development.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

In conclusion, the integration of AI and IoT in SC holds immense potential for optimizing urban operations, enhancing sustainability, and improving the quality of life for residents. By leveraging the power of data-driven decision-making, predictive analytics, and intelligent automation, SC can address various challenges and create a more efficient, resilient, and citizen-centric urban environent.

The data used and analyzed during the current study are available from the corresponding author upon reasonable request.

The authors declare no conflicts of interest regarding the publication of this paper.