Smart City Products and Their Materials Assessment Using the Pentagon Framework

Abstract

Smart cities are complex urban environments that rely on advanced technology and data analytics to enhance city services’ quality of life, sustainability, and efficiency. As these cities continue to evolve, there is a growing need for a structured framework to evaluate and integrate products that align with smart city objectives. This paper introduces the Pentagon Framework, a comprehensive evaluation method designed to ensure that products and their materials meet the specific needs of smart cities. The framework focuses on five key features—smart, sustainable, sensing, social, and safe—collectively called the Penta-S concept. These features provide a structured approach to categorizing and assessing products, ensuring alignment with the city’s goals for efficiency, sustainability, and user experience. The Smart City Pentagon Framework Analyzer is also presented, a dedicated web application that facilitates interaction with the framework. It allows product data input, provides feedback on alignment with the Penta-S features, and suggests personality traits based on the OCEAN model. Complementing the web application, the Smart City Penta-S Compliance Assistant API, developed through ChatGPT, offers a more profound, personalized evaluation of products, including the life cycle phase recommendations using the IPPMD model. This paper contributes to the development of smart city solutions by providing a flexible framework that can be applied to any product type, optimizing its life cycle, and ensuring compliance with the Pentagon Framework. This approach improves product integration and fosters user satisfaction by tailoring products and their materials to meet specific user preferences and needs within the smart city environment. The proposed framework emphasizes citizen-centric design and highlights its advantages over conventional evaluation methods, ultimately enhancing urban planning and smart city development.

1. Introduction

The ISO 37122 standard defines a smart city as one that accelerates its delivery of social, economic, and environmental sustainability outcomes while effectively addressing challenges such as climate change, rapid population growth, and political and economic instability [1]. This is accomplished by transforming its approach to societal engagement through collaborative leadership, interdisciplinary integration across city systems, and using data-driven insights and modern technologies to enhance services and improve the quality of life for all residents, businesses, and visitors [2]. This transformation delivers immediate and long-term benefits without disadvantaging any group or harming the environment.

Smart cities leverage advanced technology and data analytics to improve urban life, focusing on enhancing efficiency, sustainability, and service quality. This multidisciplinary approach addresses technological advancements, security concerns, data governance, and sustainability initiatives. By integrating technologies like the Internet of Things (IoT), artificial intelligence (AI), and 5G networks, smart cities effectively manage assets and resources, fostering a future where connected citizens, communities, and cities benefit from improved quality of life, increased sustainability, and greater efficiency [3].

Emerging technologies will shape smarter, more responsive urban environments tailored to residents’ needs. Figure 1 highlights emerging technologies in smart cities, outlining their applications and associated benefits. Each technology enhances various aspects of urban living, such as real-time infrastructure monitoring, improved healthcare delivery, and environmental sustainability. The benefits include increased efficiency, reduced energy consumption, enhanced mobility and safety, and greater biodiversity in urban environments.

Smart home systems, for instance, will optimize energy usage by automating processes enhancing comfort while reducing costs. In healthcare, wearable devices and telemedicine enable proactive, personalized care through real-time health monitoring and remote consultations. A core aspect of smart cities is smart tourism, which leverages Information and Communication Technologies (ICT) to offer tailored services, enhancing visitor satisfaction [4].

Public services will also benefit from data-driven strategies to boost efficiency and address community needs. For example, adopting technologies such as autonomous vehicles and smart traffic management systems will improve urban mobility and reduce congestion [5]. Smart infrastructure equipped with IoT sensors will facilitate real-time monitoring, enabling early issue detection and prevention.

Zaman et al. propose a semantic framework for IoT-based smart cities to support these advancements, enhancing data sharing and integration [6]. Meanwhile, Albouq et al. emphasize the importance of IoT interoperability, advocating for standardized protocols to enable seamless communication between devices [7].

State of the Art

The strategic implementation of advanced technologies aims to improve the quality of life for connected citizens and foster sustainable urban environments that are resilient and adaptable [8]. Sustainability is central to future urban development, emphasizing environmental, social, and economic strategies to create resilient and livable cities. Key concepts include the circular economy for waste reduction, resilience against challenges like climate change, sustainable mobility through eco-friendly transportation, and smart governance using data-driven decision making to enhance transparency and resource management [9,10,11].

For instance, Silva et al. highlight energy management and green infrastructure [12] while providing a roadmap for integrating sustainable practices into urban planning. Sancino et al. focus on the role of visionary leadership and collaboration with smart city governance through public-private partnerships [13]. Also, Xu et al. explore smart cities as self-organizing systems that integrate physical, social, and digital dimensions, stressing the importance of governance and interdisciplinary approaches to address societal challenges, emphasizing the need for multidisciplinary integration and addressing societal challenges [14]. Kitika et al. explore how people in Chiang Mai, Thailand, engage with smart activities, proposing two categories for redefining a smart city: “Smart community”, a network of converging lifestyles, and “Smart district”, areas with high-speed internet and social participation [15]. Furthermore, a Smart Territory initiative in Chihuahua, Mexico, showcases the integration of technology and entrepreneurship for urban development [16].

The development of frameworks for assessing smart cities is essential for providing standardized methods for evaluating their development, performance, and sustainability. These frameworks help measure critical areas like energy efficiency, infrastructure, transportation, governance, and quality of life, ensuring cities meet sustainability, innovation, and inclusivity objectives. They provide insights for decision makers, fostering improvements and long-term planning. For instance, Shao et al. propose a sustainable development framework using a Z-fuzzy Multi-Criteria Decision-Making approach, while Zhang et al. apply Maslow’s hierarchy to city design [17]. The LEED for Cities and Communities program and the Smart Sustainable Cities Assessment Framework by UN-Habitat evaluate sustainability across multiple pillars [18,19]. Also, tools like BREEAM API and Urban Footprint aid urban planning [20,21].

Recent research emphasizes personality traits in enhancing user experience in smart city solutions, with Kapoor proposing a behavioral framework [22] and Wang et al. discussing mobility optimization [23]. In addition to the technological and environmental focus on developing sustainable smart cities [24], recent research also highlights the importance of integrating personality traits to enhance the user experience and ensure more personalized, user-centered smart solutions. For instance, Gupta et al. [25] propose a behavioral framework that incorporates personality traits such as Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism, which determine how individuals interact with smart technologies in urban environments. By understanding these traits, smart city applications can better cater to diverse user needs and improve engagement.

Similarly, Li et al. [26] suggest that smart cities should integrate a shareable smart city framework with indicators that assess environmental and infrastructural elements and emphasize the social dimensions of smart cities. Shi et al. [27] propose that personality-based customization should be part of smart city assessments to enhance user engagement and satisfaction, aligning with the growing trend of incorporating user-specific feedback into urban planning.

Recent developments in various fields have shown a clear trend towards incorporating “S” features—such as sensing, smart, and sustainable technologies—in agrifood products [28]. Additionally, the design of robots integrates sensing, smart, sustainable, and social attributes. Furthermore, 3D food printers have evolved into sensing, smart, sustainable, social, and safe technologies [29]. The overarching goal of creating solutions that prioritize end-users’ needs, particularly within the context of smart cities, drives these advancements. A balanced approach is imperative in smart city development, requiring consideration of technological, environmental, and social aspects to create holistic urban solutions. This paper introduces the Pentagon Framework, a web application and a compliance feedback API, which includes five essential features: smart, sensing, sustainable, social, and safe design. This framework is designed to evaluate products holistically, supporting early stage evaluations and continuous improvement, ensuring that products can adapt over time to achieve sustainability goals. Additionally, the Pentagon Framework integrates personality traits through a matching algorithm, allowing solutions tailored to individual preferences, which increases user engagement and satisfaction.

This paper also considers the future developments of the Pentagon Framework, which aims to assess the environmental impact of materials in smart city products with a focus on recyclability and disposal challenges. This approach addresses sustainability from a product life cycle perspective. These contributions are unique in providing a multidimensional assessment that is not widely covered in current frameworks for smart city evaluations.

This paper is structured as follows: Section 2 explores enhancing urban living through the Pentagon Framework, presenting a citizen-centric approach to smart cities and the Smart C3 model for integrating these concepts. Section 3 details the product evaluation of 23 rapid prototypes using the Penta-S concept. Section 4 provides a discussion to clarify the reference framework and its implications, and Section 5 concludes with this study’s main findings and suggestions for future research.

2. Materials and Methods: The Pentagon Framework

Technologically advanced products require integrated components to align with development goals, societal needs, and user privacy. The multiple S concept introduced in [29] includes five key features—smart, sensing, sustainable, social, and safe—providing a comprehensive framework for urban development.

• Smart: Uses technology for intelligent systems to optimize urban services and improve quality of life, such as expert risk assessment models and advanced control systems [30].
• Sensing: Involves sensors and data technologies for monitoring urban systems and supporting decision making, like using optical fiber sensors for railway monitoring [31].
• Sustainable: Focuses on environmental stewardship and integrating sustainability into urban planning, as highlighted by Tura and Ojanen [32].
• Social: Emphasizes social inclusion, quality of life, and equitable access with concepts like the ’societal smart city’ introduced by Alizadeh and Sharifi [33].
• Safe: Ensures safety in urban areas, integrating measures into healthcare, transport, and planning to enhance security [34].

The Pentagon (or Penta-S) Framework takes a phased approach, from individual to city-level needs, focusing on solutions that improve daily life. Figure 2 depicts a pentagon to evaluate and balance each aspect when assessing smart city products or systems. The blue pentagon represents the ideal scenario where all the “S” features are fully achieved. In contrast, the orange polygon depicts the actual performance of the case study, where not all “S” features are met.

The centroid of the irregular pentagon illustrated in Figure 2 is calculated to highlight its deviation from the ideal case, marking the geometric center of the irregular shape. The centroid (geometric center) of a pentagon can be calculated using the coordinates of its vertices to calculate its centroid. The pentagon is defined by five vertices, each with coordinates (x1, y1), (x2, y2), (x3, y3), (x4, y4), and (x5, y5). Thus, the centroid C (xc, yc) is given by Equations (1)–(3):

$$A={\displaystyle \frac{1}{2}}{\sum }_{i=1}^5{(x}_i{y}_{i+1}-y_i{x}_{i+1}),$$

(1)

$$x_c={\displaystyle \frac{1}{6A}}{\sum }_{i=1}^5{{(x}_i+x_{i+1}\left)\right(x}_i{y}_{i+1}-y_i{x}_{i+1}),$$

(2)

$$y_c={\displaystyle \frac{1}{6A}}{\sum }_{i=1}^5{{(y}_i+y_{i+1}\left)\right(x}_i{y}_{i+1}-y_i{x}_{i+1}),$$

(3)

where A is the area, and (x_n+1, y_n+1) is equal to (x₁, y₁) to close the polygon. The centroid coordinates are in the Cartesian system. Polar coordinates are given by

$$r_c=\sqrt{x_c^2+y_c^2},{\theta }_c={tan}^{-1}\left({\displaystyle \frac{y_c}{x_c}}\right)$$

(4)

where r_n represents the distance from the centroid to the origin, and θ_c is the angle formed by this line. Additionally, a third parameter is determined by calculating the ratio of the area (R) of the case study to the area of the ideal pentagon. This relation is computed using Equation (5):

$$R={\displaystyle \frac{A_{evaluated}}{A_{ideal}}}$$

(5)

A pentagon can maintain the same centroid and evaluated area while varying its S-dimensions. Figure 3a shows how the green (evaluated) and red polygons share a centroid but differ in features, with the safe feature higher in the green polygon and the sensing feature greater in the red one. Figure 3b illustrates that the green polygon has a higher sensing value, while the red one excels in sustainable, smart, and social features, highlighting that S-dimensions can vary even with the same centroid. Maintaining a consistent centroid reflects a fixed cost, allowing different feature profiles based on priorities. A shift in centroid indicates changes in price or resource allocation.

The centroid acts as a cost point where different Penta-S feature values coexist without affecting overall cost, but any movement implies increased investment in new features.

2.1. Enhancing Urban Living with the Pentagon Framework: A Citizen-Centric Approach to Smart Cities

Traditional cities focus on basic infrastructure and standardized public services, lacking advanced technology, personalization, and system integration, leading to inefficiencies in healthcare, energy, and safety services. In contrast, cities that adopt the Pentagon Framework prioritize personalized solutions and community cohesion through integrated public services and advanced technologies. For example, smart homes enhance energy efficiency [35], Wireless Body Area Networks (WBANs) improve health outcomes [36], and smart grids promote efficient energy use [37]. Also, Penta-S cities incorporate advanced transportation, sustainable waste management, IoT for infrastructure monitoring, and 5G for communication [38]. They emphasize sustainability with green infrastructure, energy-efficient tech, and waste reduction, while traditional cities often rely on less sustainable practices, resulting in greater environmental impact. Conventional security measures lack real-time capabilities, whereas Penta-S cities utilize advanced, coordinated safety systems. Overall, Penta-S cities enhance the quality of life through personalized services, social inclusion, and advanced technologies, leading to improved living standards and city performance compared to traditional cities [39], as shown in Figure 4.

Introducing the Smart C³ Model

The Pentagon Framework centers on citizens in city design, employing real-time data from IoT devices, apps, and sensors to enhance city management. This citizen-centric model facilitates real-time service adjustments, fostering engagement and supporting sustainable urban growth [40]. IGI Global defines a smart citizen as one who utilizes technology to engage with the smart city, tackle local challenges, and participate in decision-making processes [41]. In this context, smart citizens connect their homes to the wider community and city through various devices. Li et al. [42] characterize a smart community as a network of smart homes, amenities, and green spaces that enable social interaction among residents interconnected through technologies like powerline communication, Bluetooth, Wi-Fi, phone lines, and Ethernet. Also, the Smart C3 model, introduced by Ponce et al. [40], consists of a three-layer structure:

• Smart Citizen: An individual who utilizes technology to engage within a smart city, address local issues, and participate in decision making.
• Smart Community: A network that interacts through connected products, where community members adopt various management methods and a shared community philosophy with multi-network integration.
• Smart City: A framework that uses data and modern technologies to enhance services and improve the quality of life for residents now and in the future.

This model is illustrated in Figure 5, which shows how continuous data are shared via cloud services and IoT devices. The cloud platform supports urban functions such as public safety with advanced surveillance and emergency response, recreation by enhancing cultural spaces, and energy through smart grids and renewable sources. It facilitates telemedicine, remote health monitoring, and data-driven healthcare while improving education with online resources and better learning environments. Environmental monitoring benefits from real-time air and water quality tracking, waste reduction, and climate change mitigation. Additionally, the cloud enhances transportation with smarter traffic management and housing through smart building technologies, boosting living standards and energy efficiency.

There is a need to develop products that meet the urban functions of smart cities, addressing the complex and interconnected demands of modern urban environments. As smart cities increasingly depend on technology to optimize and efficiently manage these functions, significant investment in both software and hardware is required to create smart, sensing, sustainable, social, and safe products. These products make smart cities more appealing and livable and replace traditional solutions by enhancing resource management and service delivery. Penta-S products enable informed decision making and improved service fulfillment through integration with other systems and data utilization.

The model includes three layers—smart citizens, smart communities, and smart cities—highlighting urban functions’ fluid, interconnected nature. It aims to understand how smart technologies interact to support cities with a flexible, multi-layered approach that adapts to socioeconomic, infrastructure, and governance contexts. This adaptability ensures the model can scale and evolve to meet dynamic urban needs without oversimplification.

2.2. Product Development Framework for Smart Cities

This paper suggests adjusting the Integrated Product, Process, and Manufacturing System Development Reference Model Framework (IPPMD) proposed by Molina et al. [30] to align with smart cities within the Pentagon Framework. The aim is to provide a systematic approach to designing and developing technologies that effectively address urban challenges. The framework consists of four stages, as illustrated in Figure 6a, with specific activities to ensure that the resulting technologies are integrated, efficient, and sustainable:

• Product Ideation: This stage emphasizes generating ideas aligned with the Penta-S framework to meet smart city stakeholders’ needs. It involves identifying innovative concepts incorporating smart, sustainable, sensing, social, and safe elements using tools like megatrends analysis, empathy maps, and ethnographic studies [28]. Customer needs are analyzed through the Jobs-To-Be-Done (JTBD) framework and a needs–satisfiers matrix. Promising ideas are selected for development using Pugh charts for comparison and storyboarding to illustrate functionalities [40].
• Conceptual Design and Specification: This stage defines the product concept and target specifications by aligning customer needs with technical requirements. It includes functional decomposition to outline key functionalities, documentation of technical specifications, and concept generation to propose and evaluate multiple ideas. Tools like Quality Function Deployment (QFD) and morphological matrices assist in selecting the best options [28].
• Detailed and Engineering Design: This phase transforms the product concept into a detailed design emphasizing smart, sensing, social, sustainable, and safe solutions. Key activities include creating detailed models (e.g., CAD and circuit designs), refining layouts, and developing technical drawings. Simulations and testing ensure the design meets specifications and standards, including failure analysis and environmental impact assessments [28].
• Prototyping: This stage involves developing and testing prototypes to validate design performance and sustainability. Functional prototypes are created using 3D models and microcontrollers, then integrated and tested under lab conditions. Techniques like FMEA (Failure Mode and Effects Analysis) and LCA (Life Cycle Assessment) ensure functionality and enable refinement [28].

An essential addition to these stages is incorporating user feedback gathered through surveys, interviews, and usability testing to understand preferences and pain points. Feedback is analyzed to identify themes, prioritize issues, and evaluate suggestions, guiding design adjustments to align with user needs. This iterative, user-centered approach improves product usability, satisfaction, and success.

2.3. Products’ Life Cycle Phases and Influential Factors

Figure 6b categorizes the life cycle phases of smart city technologies, highlighting critical stages in integrating Penta-S products. Each phase focuses on sustainability and efficiency, with recommended activities outlined to ensure these goals:

• Supply Chain: This phase involves responsibly sourcing raw materials and components and prioritizing sustainable and ethical practices. Efficient logistics and transportation methods are also emphasized to minimize environmental impact.
• Manufacturing: In this phase, the focus is on producing Penta-S products using energy-efficient processes, minimizing waste, and adopting environmentally friendly materials. Manufacturing should also incorporate advanced technologies to optimize production and reduce carbon footprints.
• Use: During the use phase, the smart city infrastructure integrates Penta-S products, allowing them to perform their intended functions. The design ensures long-term efficiency, durability, and adaptability, allowing the products to respond dynamically to changing urban needs while maintaining energy efficiency.
• End of Life: When Penta-S products reach the end of their usable life, this phase involves safe disposal, recycling, or repurposing to reduce waste. Emphasis is placed on reclaiming valuable materials and minimizing the environmental impact of disposal.
• Lifetime Over: This final phase considers the entire lifespan of the Penta-S product, evaluating its overall impact and seeking opportunities for improvement. Lessons learned from this phase can inform the development of future products, ensuring ongoing sustainability and efficiency in smart city technologies.

The life cycle phases of Penta-S products for smart cities emphasize smart, sensing, social, sustainable, and safe features, as outlined in

Table 1. Overview of life cycle phases for Penta-S products, including Pentagon Framework features.

Phases	Sustainable	Social	Smart	Sensing	Safe
Supply Chain	Source sustainable raw materials	Promote fair labor practices	Implement smart logistics for tracking	Use sensors to monitor the supply chain	Ensure secure data transmission
	Optimize transportation to reduce emissions	Ensure transparency in source	Optimize inventory management with AI	Track the environmental impact of the supply chain	Implement security measures to prevent theft
	Minimize packaging waste	Support local suppliers	Use blockchain for supply chain transparency	Monitor storage conditions	Ensure safe handling and storage of materials
Manufacturing	Use low-impact, recyclable materials	Ensure high social impact through inclusive design	Integrate smart manufacturing processes	Use sensors for real-time monitoring of production	Implement robust cybersecurity in manufacturing
	Implement energy-efficient processes	Promote local employment opportunities	Automate quality control	Implement IoT for process optimization	Ensure worker safety and ergonomics
	Design for minimal environmental footprint	Consider worker safety and ergonomics	Utilize advanced analytics for efficiency	Monitor resource usage and waste	Secure data handling in manufacturing
Use	Ensure low energy consumption and reduce CO2 emissions	Design for user accessibility and inclusivity in UX	IoT functionalities. Remote monitoring and control	Collect real-time data and user data for improvement	Ensure data privacy, security, and compliance.
	Promote renewable energy	Ensure health and safety in product use	AI for predictive maintenance	Monitor product performance	Implement safety protocols in product design
	Efficient waste management	Foster community engagement	Provide smart UI	Track environmental impact	Safety protocols for design
End of life	Design for easy disassembly and recycling	Ease safe disposal and recycling	Integrate smart recycling technologies	Monitor the disassembly process for safety	Ensure safe disposal methods
	Use materials that are easy to recycle	Ensure community awareness about disposal methods	Employ AI to optimize recycling processes	Use sensors to detect recyclable materials	Implement safe handling of hazardous materials
	Minimize waste generation	Support social initiatives for recycling and reuse	Implement smart waste management systems	Monitor waste streams for efficiency	Ensure safety in end-of-life processing
Lifetime over	Low-impact, recyclable materials	Promote community recycling	AI to predict and manage waste streams	Monitor the environmental impact of waste	Safe disposal to prevent environmental pollution
	Energy-efficient processes	Disposal methods education	Smart disposal solutions	Track hazardous material disposal	Hazardous materials safe handling
	Minimal environmental footprint design	Collaborate with local governments for waste management	Implement smart waste collection systems	Monitor landfills and recycling centers	Prevent illegal dumping and manage e-waste safely

. This approach ensures technologies are developed, utilized, and disposed of sustainably. Covering supply chain, manufacturing, use, and end-of-life stages, it addresses environmental impacts, optimizes resources, and enhances urban technology effectiveness, supporting long-term sustainability and responsible innovation.

2.4. Personalization to Improve the Usability of Penta-S Products in the Use Phase

Incorporating social features in the use phase of Penta-S products enhances user experience and effectiveness. Ensuring accessibility fosters a user-centric and equitable smart city environment while tailoring products to citizens’ needs improves quality of life. Personalization, guided by the OCEAN model (“Big Five”) of personality traits [43]—openness (exploration), conscientiousness (organization), extraversion (sociability), agreeableness (empathy), and neuroticism (emotional sensitivity)—further refines this approach.

Therefore, a classification for Penta-S products based on the OCEAN model aligns product features with key personality traits:

• Openness: Products are innovative, encouraging creativity and exploration.
• Conscientiousness: Products emphasize organization, reliability, and efficiency for users valuing structure.
• Extraversion: These products foster social interaction, connectivity, and energy, engaging dynamic users.
• Agreeableness: Products promote cooperation, empathy, and community engagement, appealing to users who prioritize harmony.
• Neuroticism: Products offer calming, supportive features to help users manage stress or anxiety.

2.5. Building the Pentagon Framework Features for the Integration of Developed Rapid Prototypes into a Smart City

This section introduces developed proof of concepts by the Enabling Technologies Group from Tecnologico de Monterrey Campus Ciudad de Mexico [44]. The products can be seamlessly integrated into a smart city concept by considering the Penta-S features and utilizing a smart cloud platform, as shown in Figure 7. This platform would act as a central hub, allowing real-time data exchange and control for various technologies. The smart cloud platform would enhance collaboration between these technologies, optimize resource usage, and provide real-time data analytics, creating a more connected, efficient, and sustainable smart city ecosystem.

The smart cloud platform categorizes services such as public safety, recreation, energy, healthcare, education, environmental monitoring, transportation, and housing. Additionally, two maps are presented: one showcasing proof of concepts tailored to local community services, like schools, homes, and public facilities, and another adapted to city services, including hospitals, corporate buildings, apartment complexes, and museums.

In-house projects were analyzed to validate the Pentagon Framework, focusing first on the product, then each S feature, and finally on the associated personality trait. While the analysis included all products in Figure 7, detailed information was omitted to emphasize the Pentagon Framework proposal. The following sections highlight the analysis of four projects:

• Didactic Solar umbrella. This outdoor public installation features an umbrella with flexible solar panels, energy sensors, and weather monitoring tools on a table with benches. It collects, stores, and monitors solar energy, providing real-time data and automated reports via a remote terminal. Serving both functional and educational purposes, it demonstrates solar energy management principles while offering a practical resource for charging low-power devices like phones and laptops, as shown in Figure 8a [2].
  • Social: Encourages renewable energy, community engagement, and hands-on solar learning.
  • Sustainable: Umbrella with solar panels reduces carbon footprint and supports sustainability.
  • Sensing: Monitors solar energy in real-time for performance and data optimization.
  • Smart: Cloud services are used for energy management, offering real-time, personalized insights.
  • Safe: Follows strict safety standards, ensuring a secure and trustworthy environment.
  • Associated personality traits: Extraversion and neuroticism. Environmentally conscious individuals are likely to be interested in sustainability, eager to learn about renewable energy, and appreciate convenient amenities for daily use.
• Electric bicycle with regenerative charge. This solar-powered bicycle prototype, shown in Figure 8b, features regenerative charging for urban use. Solar panels store energy in two batteries via a circuit managing power flow between the motor, solar cells, and pedals. It powers the bicycle and charges devices like phones and laptops, offering an eco-friendly commuting solution that promotes renewable energy [2].
  • Social: Provides a sustainable, eco-friendly transportation option for community well-being.
  • Sustainable: Uses solar energy to reduce environmental impact and resource dependency.
  • Sensing: Monitors battery levels and energy use for real-time data and efficiency.
  • Smart: Optimizes energy use and navigation with cloud services and adaptive features.
  • Safe: Ensures rider and pedestrian safety with protocols and accident prevention systems.
  • Associated personality traits: Extraversion and neuroticism. Eco-conscious individuals will likely be open to innovation, motivated by environmental benefits, and appreciate efficient, multifunctional transportation solutions.
• Fruit inspection using artificial vision. This AI-powered vision system for urban agriculture uses advanced cameras and AI algorithms to monitor crops and detect diseases, as shown in Figure 8c with tomato recognition. Being flexible and retrainable, it identifies early infections, including those hard to detect with conventional methods, enhancing proactive crop management and productivity.
  • Social: Enhances agricultural productivity and crop quality for farming communities.
  • Sustainable: Uses eco-friendly methods to minimize resource use and environmental impact.
  • Sensing: Employs cameras for real-time data on fruit health and conditions.
  • Smart: Uses AI to detect diseases and measure fruit parameters efficiently.
  • Safe: Ensures safety for farmers and consumers by preventing inaccurate assessments.
  • Associated personality traits: Extraversion and neuroticism. Innovative farmers and urban agriculturalists are interested in advanced technology to improve crop health and yield.
• Robots designed for teaching mathematics in elementary schools. This educational prototype uses LEGO Mindstorms and LabVIEW to improve elementary math teaching, as implemented in Xalapa, Veracruz, Mexico. Students interact with math-programmed robots, take exams in LabVIEW, and receive evaluations via a fuzzy system, as shown in Figure 8d. LabVIEW’s AI optimizes questions and feedback, boosting motivation and interest in robotics, computer science, and teamwork. Results from 80 students and five teachers highlight its effectiveness as an innovative tool for urban schools [2].
  • Social: Makes math interactive and fun, fostering collaboration and positive interactions.
  • Sustainable: Promotes resource efficiency, reducing waste, and supporting ecological balance.
  • Sensing: Uses sensors for real-time feedback and personalized learning experiences.
  • Smart: Applies AI and robotics in LabVIEW to adapt lessons and engagement.
  • Safe: Ensures a safe learning environment, protecting students’ privacy.
  • Associated personality traits: Agreeableness and neuroticism. Enthusiastic educators, students, and those motivated by educational advancement and practical application of STEM concepts.

2.6. Feature Extraction Methodology and Dataset Development

This study employed ChatGPT, a large language model trained by OpenAI [45], to assist in feature extraction based on the Pentagon Framework. ChatGPT was utilized to perform a qualitative analysis of product descriptions, helping us systematically extract the most relevant features within each of the five S dimensions.

2.6.1. Smart Features and Categorization Levels

For the smart feature, it was identified how each product employs advanced technologies such as AI, IoT, cloud computing, and autonomous systems. ChatGPT analyzed product descriptions and extracted key features related to:

• Automation and optimization: How the product enhances its efficiency using automated processes.
• Data-driven decision making: Use of AI or machine learning to provide insights or optimize systems.
• Real-time feedback and control: Integrating cloud-based systems or IoT for adaptive control.

These features were then categorized according to the Penta-S Framework criteria for the smart dimension:

• High (3): Direct contributions to advanced control systems, intelligent optimization, or complex decision making using AI or neural networks.
• Medium (2): Efficiency improvements, data-driven decisions, or monitoring systems that do not directly involve advanced AI or complex control mechanisms.
• Low (1): Indirect contributions to urban optimization or quality of life enhancement, often through basic infrastructure, information collection, or automation without significant intelligence or adaptiveness.

2.6.2. Sensing Features and Categorization Levels

Sensing features focus on how each product incorporates real-time data acquisition and monitoring systems. ChatGPT-extracted features that represent the product’s capability to are as follows:

• Collect real-time data: Use of sensors to gather environmental or operational data.
• Monitor conditions: Continuous tracking of energy use, environmental changes, or user behaviors.
• Provide feedback for optimization: How the sensors provide input for system adjustments.

The features were then evaluated using the following levels:

• High (3): Advanced, complex sensing systems with significant real-time data integration crucial for decision making.
• Medium (2): More advanced sensors or real-time data applications beyond simple monitoring.
• Low (1): Basic sensors or data collection methods with limited complexity.

2.6.3. Sustainable Features and Categorization Levels

Sustainable features evaluate the products’ contributions to environmental sustainability and resource efficiency. ChatGPT-extracted features reflect the following:

• Energy efficiency: How the product minimizes energy consumption or uses renewable energy sources.
• Resource management: Efforts to reduce waste and optimize resource use.
• Long-term viability: Technologies that support sustainable urban growth and environmental stewardship.

Sustainability features were categorized using the following criteria:

• High (3): Technologies that have long-term impact and contribute fundamentally to urban or environmental sustainability, such as renewable energy systems.
• Medium (2): Solutions that provide moderate efficiency or eco-friendliness, such as resource management or energy-saving measures.
• Low (1): Single-component features with limited or minimal impact on sustainability.

2.6.4. Social Features and Categorization Levels

The social dimension involves understanding how the product enhances the quality of life, promotes inclusivity, and engages communities. ChatGPT-extracted features describe the following:

• Community engagement: How the product fosters participation or interaction within a community.
• Quality of life improvements: Enhancing comfort, accessibility, or convenience for city residents.
• Inclusivity: Addressing social justice or providing equal access to services.

The features were then evaluated using the following levels:

• High (3): Broad and transformative impact on the urban community, promoting large-scale social inclusion.
• Medium (2): Moderate community engagement or societal improvement involving small to medium-sized groups or communities.
• Low (1): Localized or limited impact, focusing on individual benefits or smaller-scale improvements.

2.6.5. Safe Features and Categorization Levels

For safe features, the focus was on identifying how the product ensures security and safety for users and infrastructure. ChatGPT-extracted features are related to the following:

• Safety protocols: Implementation of safety standards and measures to reduce risk.
• Security systems: Integration of data security, physical safety, or accident prevention technologies.
• Risk mitigation: How the product proactively prevents hazards or ensures user safety.

These features were classified using the criteria below:

• High (3): Comprehensive systems providing a wide-reaching effect on urban infrastructure or large populations.
• Medium (2): Safety measures affecting larger groups or extended areas.
• Low (1): Basic safety specific to a narrow or isolated aspect of urban life, focusing on individual protection.

The features extracted by ChatGPT were organized into a structured dataset, linking each product to specific characteristics for each S dimension, with at least three to four relevant features identified per dimension. Figure 9 and Figure 10 show the extracted Penta-S features. The dataset was further enriched by generating around 300 keywords related to each dimension, enhancing the analysis of the products’ technological, sensing, sustainable, social, and safety aspects. These expanded datasets capture the full range of relevant attributes for smart city solutions and are available in ref. [46].

2.7. Classification of Embodied Energy Levels for Materials for the Sustainable Dimension

Embodied energy is the total energy required to produce materials, from extraction to manufacturing. In sustainability, choosing materials with lower embodied energy helps reduce environmental impact [47]. We classified materials using a three-level approach based on energy data from material databases and industry standards [48,49,50,51], with levels determined by energy consumption in megajoules per kilogram (MJ/kg).

• Low Embodied Energy (Low Level): Materials that require less than 50 MJ/kg for production. These materials, such as wood or bamboo, are typically natural or minimally processed, with lower environmental impacts due to low energy requirements.
• Medium Embodied Energy (Medium Level): Materials with embodied energy values ranging from 50 to 200 MJ/kg. This range includes commonly used materials like concrete, brick, and certain recycled materials, which balance durability and moderate energy intensity.
• High Embodied Energy (High Level): Materials that require more than 200 MJ/kg for production. These materials include advanced metals and polymers, such as stainless steel, titanium alloys, and high-performance plastics, which are energy-intensive due to their complex manufacturing processes.

Embodied energy data for each material were sourced from the literature, industry reports, and databases. Materials like wood and bamboo are classified as low-energy due to their natural origin and minimal processing. At the same time, intensive manufacturing makes high-energy stainless-steel and titanium alloys. Recycled materials, such as aluminum and steel, were classified as low energy because recycling significantly reduces energy use, making them more sustainable alternatives.

2.8. Similarity-Based Personality Trait Matching

In the social aspect of the framework, personalization during the use phase enhances user experience by adapting products to individual needs and preferences. Incorporating personality traits makes products more intuitive, enjoyable, and practical. For example, openness is reflected in creative designs like the Didactic Solar Umbrella or immersive VR for architectural exploration. Conscientiousness aligns with precision-focused projects like energy management with digital twins or AI vision for fruit inspection. Extraversion connects to socially engaging projects, such as collaborative learning with digital tools or robotic math teaching. Agreeableness supports well-being-focused innovations like robotic arms for limb loss or OSA detection systems. Neuroticism may relate to stress-reducing or safety-enhancing projects like the ROBOCOV platform or energy-efficient tailored interfaces. Figure 11 and Figure 12 detail these smart community and city projects.

A Jaro–Winkler-based similarity algorithm was developed to extend the concept beyond predefined smart city projects. Using the stringdist package in R Language, it calculates the similarity between the user-entered product name and description ($N_u$, $D_u$) and those of predefined projects ($N_p$, $D_p$). The algorithm computes similarity scores for product names $S_N$, and descriptions $S_D$ using the Jaro-Winkler method.

A function, $calculate\_similarity(a, b)$, computes the textual similarity between two strings, returning the similarity ratio $S\left(a,b\right)$, as shown in Equation (6):

$$S\left(a,b\right)=1-stringdist(a,b, method=“\mathrm{jw}”),$$

(6)

The function $stringdist(a,b,method=\mathrm{“}\mathrm{jw}”)$ calculates the Jaro–Winkler distance between strings $a$ and $b$. Subtracting this result from 1 gives the similarity score $S\left(a,b\right)$, ranging from 0 (no similarity) to 1 (perfect match). Inputs are normalized to lowercase for consistent matching.

After calculating similarity scores for the product name and description, the overall similarity score, $S_{overall}$, is determined as their average, as shown in Equation (7):

$$S_{overall}={\displaystyle \frac{S\left(N_u,N_p\right)+S\left(D_u,D_p\right)}{2}},$$

(7)

where $S\left(N_u,N_p\right)$ is the similarity between the user-entered product name and the predefined project name, and $S\left(D_u,D_p\right)$ is the similarity between the user-entered product description and the predefined project description.

The algorithm evaluates all predefined projects P₁, P₂, …, P_n, calculating $S_{overall}$ for each. The project with the highest similarity score is identified as the best match, as shown in Equation (8):

$$P^{*}=arg\underset{i}{\max }{S}_{overall}\left(P_i\right),$$

(8)

where P* is the project with the highest similarity score. Its associated personality traits are returned as the traits most relevant to the user’s product. This similarity-based approach suggests personality traits aligned with the user’s product, helping developers understand its resonance with personality dimensions and providing feedback for improvement. The system identifies traits associated with predefined solutions similar to the product and presents them to refine design and target audience alignment.

3. Results

3.1. The Smart City Pentagon Framework Analyzer Interface

After processing Penta-S features and associated personality traits, a front-end application was developed to enable user interaction with the framework. This application allows users to input product data, analyze its alignment with the Smart City Pentagon Framework, and receive personality trait suggestions. Key features include the following:

• Input fields for product name, description, and features.
• Buttons will initiate analysis and display personality-based solutions.
• Interactive sliders to adjust personality traits and explore solution recommendations.
• Dynamic feedback with suggestions for enhancing product features to meet smart, sensing, sustainable, social, and safe criteria.

The Smart City Pentagon Framework Analyzer [52], shown in Figure 13, evaluates products against the five dimensions of the Pentagon Framework. Users input the product’s name, description, and critical features (Figure 13a), and the system provides feedback on its alignment with these dimensions (Figure 13b). For detailed feedback, the “Enhance your product’s Penta-S features” button links to the Smart City Pentagon Compliance Assistant API in ChatGPT [53].

The system also suggests personality traits, such as openness, conscientiousness, extraversion, agreeableness, or neuroticism, based on textual similarity between user-provided data and predefined smart city projects, helping developers refine their products for specific personality types and enhance personalization.

Additionally, a button suggests predefined projects (among the 23 available) with similar characteristics, offering users ideas for product improvement or refinement.

3.2. Smart City Penta-S Compliance Assistant API

The Smart City Penta-S Compliance Assistant (Figure 14) is an advanced API developed through ChatGPT to expand on the insights from the Smart City Pentagon Framework Analyzer. While the Analyzer provides a preliminary evaluation of product alignment with the Pentagon Framework, the API offers detailed and personalized feedback.

This tool supports product developers, designers, and urban planners in optimizing their products for compliance with the Pentagon Framework across different life cycle phases. It integrates the IPPMD framework to ensure systematic product development and aligns feedback with personality traits from the OCEAN model. This approach enables product refinement to meet urban, societal, environmental, and technical standards while enhancing user experience through tailored personalization.

The Smart City Penta-S Compliance Assistant guides users in refining their products for Penta-S compliance by gathering and analyzing key product details, including the following:

• Penta-S Analysis: Current alignment with smart, sensing, sustainable, social, and safe dimensions.
• Compliance Suggestions: Recommendations for improving alignment with the Pentagon Framework.
• Personality Traits: Characteristics of the target audience based on the OCEAN model.

The API generates detailed feedback in three steps:

• Key Suggestions for Refinement: Highlights the most critical improvements needed.
• Product Life Cycle Phase Recommendations: A table detailing how each life cycle phase—supply chain, manufacturing, use, end of life, and lifetime over—can be optimized for Penta-S compliance.
• Personality Trait Alignment: Tailors product recommendations to align with target audience traits, enhancing the user experience during the use phase.

The API delivers concise, actionable, phase-specific feedback, seamlessly integrating the Pentagon Framework into product development. It ensures holistic compliance by evaluating and optimizing products across all life cycle phases, from supply chain management to end-of-life disposal, aligning them with smart city requirements. Additionally, the tool offers personalized feedback based on the target audience’s personality traits, enhancing user-centric design and market success. Structured guidance is provided in a clear table format, organized by life cycle phase and Penta-S element, enabling users to easily track and implement improvements.

3.3. Identification of Penta-S Features of the Presented Prototypes

Nine prototypes were evaluated to demonstrate how the API assesses the product. Thus,

Table 2. The evaluation is obtained from the product description.

Product Penta-S Evaluation	Penta-S Level
Didactic solar umbrella	Smart (33.33%): Level 1: weather monitoring, automated reports Sensing (56.41%): Level 1: sensors, monitoring, data, energy sensor. Level 2: solar energy, energy sensors, real-time data, environment, real time, environmental, system, information, energy management Sustainable (100%): Level 3: solar energy Social (83.33%): Level 2: public space. Level 3: education Safe (66.67%): Level 2: public space
Electric bicycle	Smart (100%): Level 3: AI Sensing (60%): Level 1: flow. Level 2: solar energy, real time, capture, system Sustainable (91.67%): Level 2: eco-friendly. Level 3: solar energy, renewable energy, sustainable Social (66.67%): Level 2: community Safe (66.67%): Level 2: renewable energy
Fruit inspection	Smart (100%): Level 3: AI, artificial vision Sensing (60%): Level 1: monitoring. Level 2: crop health, camera, real time, system Sustainable (66.67%): Level 2: eco-friendly Social (66.67%): Level 2: crop health Safe (33.33%): Level 1: safety
Robot for teaching	Smart (100%): Level 3: AI Sensing (58.33%): Level 1: feedback. Level 2: environment, real time, system Sustainable (0%): No relevant keywords found Social (100%): Level 3: education Safe (33.33%): Level 1: feedback
Tailored interfaces	Smart (80%): Level 1: cloud services. Level 2: energy control. Level 3: AI analysis, AI, tailored Sensing (60%): Level 1: data, temperature. Level 2: real-time data, environment, real time, environmental, system, analysis, control, energy management Sustainable (100%): Level 3: sustainable Social (93.33%): Level 2: community. Level 3: tailored, personalized, gamified, gamification Safe (83.33%): Level 2: risk management. Level 3: energy control
Adaptive Rooftop Shading	Smart (100%): Level 3: AI Sensing (55.56%): Level 1: data, light. Level 2: environment, consumption, environmental, system Sustainable (100%): Level 3: energy saving, sustainable Social (50%): Level 1: comfort. Level 2: community Safe (100%): Level 3: city infrastructure
ISO3721, AI, and gamification	Smart (66.67%): Level 1: energy monitoring. Level 2: energy efficiency. Level 3: AI Sensing (58.33%): Level 1: monitoring, data. Level 2: energy use, environment, real time, environmental, system, energy management Sustainable (66.67%): Level 2: eco-friendly, energy efficiency, pet Social (66.67%): Level 1: awareness. Level 2: engagement, community. Level 3: gamified Safe (66.67%): Level 2: energy efficiency
Garment detection	Smart (91.67%): Level 2: energy efficiency. Level 3: AI, computer vision, tailored Sensing (66.67%): Level 2: energy use, camera, consumption, system Sustainable (66.67%): Level 2: energy efficiency Social (66.67%): Level 1: comfort. Level 3: tailored Safe (66.67%): Level 2: risk management, energy efficiency
Human–machine interfaces	Smart (100%): Level 3: AI Sensing (59.26%): Level 1: monitoring, data. Level 2: environment, camera, real time, tracking, capture, system, control Sustainable (100%): Level 3: glass Social (55.56%): Level 1: independence. Level 2: mobility, accessibility Safe (33.33%): Level 1: safety

shows the product radar chart with the associated level based on the product descriptions.

Enhancing the Adaptive Rooftop Shading System project with the Smart City Pentagon Compliance Assistant has the following suggestions:

• Smart (Level 3)—Maintain: Integrate AI algorithms that utilize real-time data alongside historical data for dynamic adjustments. This will optimize shading patterns based on solar conditions, ensuring maximum efficiency and comfort. Enhance the AI system to learn and predict weather patterns, thus preemptively adjusting the shading system to accommodate upcoming changes.
• Sensing (Level 2—Improve): Upgrade sensors to measure light intensity, ambient temperature, and humidity. This would allow the system to better understand environmental conditions and adjust shading more accurately. Incorporate real-time energy consumption monitoring, allowing for data-driven insights on the shading system’s impact on energy savings. Consider adding air quality sensors to measure the impact of shading on local urban heat island effects, contributing valuable data for city infrastructure planning.
• Sustainable (Level 3)—Maintain: Ensure that the materials used in the shading system are recyclable and durable to support long-term sustainability goals. Explore integrating solar panels within the shading structures to generate renewable energy, enhancing the product’s contribution to energy self-sufficiency.
• Social (Level 2—Improve): Develop community engagement features, such as an app where residents can track the energy savings and indoor comfort levels achieved by the system. This would promote awareness and community pride in sustainable practices. Implement accessibility features, ensuring the system’s benefits reach underserved communities or buildings that require retrofitting.
• Safe (Level 3)—Maintain: Reinforce the system’s safety protocols by including remote monitoring capabilities that alert city infrastructure teams if any malfunction or damage occurs, ensuring rapid response. Conduct stress testing of materials to withstand extreme weather conditions, enhancing reliability and long-term safety in urban environments.

Figure 15 shows the API’s table for each S feature’s product life cycle phase recommendation. The suggestions regarding the associated personality traits are as follows:

• Extraversion: The Adaptive Rooftop Shading System aligns with extraversion through community-oriented features, such as an app that allows residents to engage with and see the impact of the system, fostering a sense of connection and collective achievement in sustainable practices. The system’s visibility and benefits also create a shared city infrastructure experience.
• Neuroticism: The product supports neuroticism by reducing stress associated with high indoor temperatures in hot climates. Its AI-driven, automated system adjusts shading without user intervention, enhancing comfort and reassurance. Furthermore, the remote monitoring system ensures the product’s reliability, giving users peace of mind about its safety and effectiveness.

Finally, the API provides an updated product description: The Adaptive Rooftop Shading System is an AI-powered, sustainable solution for urban environments that reduces energy consumption through intelligent shading management. It integrates real-time data with historical solar data to dynamically adjust shading based on current conditions, ensuring optimal performance throughout the year. The system uses recyclable materials and integrates solar panels, enhancing its sustainable impact in the community and ensuring long-term value for city infrastructure. Figure 16 shows the two pentagon features radar chart for the initial assessment (Figure 16a) and the new product description assessment (Figure 16b) with the following associated levels:

○

Smart (100%): Level 3: AI

○

Sensing (61.11%): Level 1: data. Level 2: real-time data, environment, consumption, real time, system

○

Sustainable (100%): Level 3: sustainable

○

Social (66.67%): Level 2: community

○

Safe (100%): Level 3: city infrastructure

The enhanced radar charts illustrate the Adaptive Roof Shading system’s performance evolution across the five smart city dimensions: smart, sensing, sustainable, social, and safe. Initially (Figure 16a), the system achieved 62% of ideal performance, with sensing and social features imbalances. Following targeted improvements (Figure 16b), performance increased to 71%, particularly in sensing and sustainable dimensions. A slight centroid shift indicates minor resource reallocation while maintaining overall balance. These enhancements, guided by API feedback, demonstrate a more efficient and holistic solution.

4. Discussion

Smart city development leverages advanced technologies to create sustainable, efficient urban environments that enhance residents’ quality of life. However, traditional product frameworks often fall short of addressing the unique challenges posed by smart cities. The Penta-S framework (smart, sustainable, sensing, social, safe) comprehensively integrates advanced technologies into smart cities, addressing their unique challenges. It evaluates product alignment with smart city principles, offering insights to guide improvements. Its key strength is enhancing decision making through a structured assessment of a product’s status across five dimensions. However, this requires developers to have sufficient knowledge of the product’s design, as the framework’s feedback relies on the product’s title, description, and keywords, potentially limiting accuracy. By facilitating early life cycle analysis, the framework helps developers efficiently align products with smart city goals.

While aligning with state-of-the-art frameworks, it faces limitations in addressing diverse urban contexts and its dependence on advanced technologies. Silva et al. emphasize energy management, waste reduction, and green infrastructure as critical components of sustainable smart cities [12]. While the sustainable dimension of the Pentagon Framework captures these aspects, it must better account for implementation barriers, especially in resource-constrained cities. The framework must be expanded to address these limitations by considering differences in resource availability and the adaptability of technology across various socioeconomic contexts.

4.1. Key Challenges and Proposed Solutions

• Technological dependency. Relying on advanced systems like IoT and AI poses challenges in developing regions with limited infrastructure. Antwi-Afari et al. note that gaps in human resources and sustainable resource consumption hinder progress in these areas [10]. A phased implementation strategy, introducing scalable, low-cost technologies, could help overcome these barriers and enable progressive adoption.
• Data privacy and security. Other authors have mentioned security and privacy as critical in smart cities due to increased connectivity and cyber threats. Cui et al. highlight key challenges and opportunities, emphasizing robust measures to protect sensitive data and infrastructure [54]. Choenni et al. stress the need for effective data governance frameworks to manage data generated by smart applications [55]. Wang et al. highlight the growing reliance on big data in smart city management, raising concerns about data privacy and security [23]. While the Pentagon Framework includes “Safe” as a key dimension, it should further address the challenges of extensive data collection. Smart cities, relying on sensor networks, IoT devices, and analytics, face significant risks of data breaches and misuse. To mitigate these risks, the framework must incorporate privacy-preserving technologies, such as differential privacy and encrypted data exchanges.

Additionally, integrating large-scale data across platforms can create vulnerabilities if not securely managed. The framework should include strict ethical guidelines and privacy protocols, ensuring that sensitive data—such as location, behavior, and financial information—is handled securely. To reduce re-identification risks, techniques like anonymization and secure multi-party computation should be applied.

• Human-centric design. The framework’s social dimension emphasizes inclusivity and personalization, aligning with Gupta et al.’s behavioral approach integrating personality traits [25]. However, practical implementation requires adapting solutions to diverse demographic and cultural contexts. Enhancing personalization strategies for large urban populations could ensure broader acceptance and usability.
• Governance and collaboration. Gracias et al. [9] stress the importance of public-private partnerships and visionary leadership for smart city governance. These elements should be incorporated to strengthen the Pentagon Framework, ensuring that smart city projects are technologically sound and politically and socially sustainable. Nam & Pardo [11] highlight smart cities as self-organizing systems, emphasizing cross-sector collaboration. The Pentagon Framework already integrates sustainability, sensing, and safety dimensions with social concerns but could further promote cooperation among government, private sector, and civil society. By fostering partnerships that bring together diverse expertise—from data scientists to urban planners—the framework can help cities develop more innovative and holistic solutions.
• Sustainability and environmental impact. The sustainable dimension aligns with frameworks like CITYkeys, promoting energy efficiency and renewable solutions [56]. However, trade-offs between innovation and environmental impact, such as e-waste from IoT devices, must be addressed. For instance, producing and maintaining sensors and IoT devices could increase electronic waste and energy consumption. Incorporating circular economy principles in designing and deploying technologies would ensure environmental benefits outweigh potential negatives. Figure 17 illustrates challenges like sourcing recyclable materials, advanced electronics, and rare materials. The focus areas for improvement are product longevity, recycling, reducing hazardous materials, and resource efficiency.
• Comparative analysis and adaptability. While Zoghi’s model focuses on neighborhood-level readiness [57], the Pentagon Framework scales evaluations to the city level, targeting specific products for urban sustainability. This product-based approach allows for more targeted assessment of the technologies being implemented, providing decision makers with actionable insights into the effectiveness and scalability of smart solutions. The Pentagon Framework’s adaptability, compared to global frameworks focused on sustainability and environmental metrics [1,18,56], offers a dynamic model with five key dimensions, making it suitable for various urban contexts. However, aligning it with globally recognized standards like LEED would enhance its applicability across cities and provide decision makers with benchmarks to assess the success of smart city initiatives.
• Emerging technologies and use cases. Integrating emerging trends would strengthen the framework’s ability to address the evolving needs of modern urban environments. Yaqoob et al. [58] explore the metaverse’s potential for urban transformation, while Bouazzi et al. [59] highlight LoRaWAN’s role in improving healthcare. Incorporating these technologies into the Pentagon Framework would make it more effective and adaptable, ensuring it stays relevant in managing evolving smart city needs.

4.2. Advantages and Limitations of the Developed Tools

The Smart City Pentagon Framework Analyzer offers an intuitive platform for developers to input product data and receive dynamic feedback aligned with the Pentagon Framework’s core dimensions. With features like personality trait sliders and solution exploration tools, the interface is user-friendly and adaptable, making it ideal for evaluating products across varied urban contexts and ensuring compatibility with developing and developed cities’ needs.

Additionally, the Smart City Penta-S Compliance Assistant API complements the Analyzer by delivering in-depth compliance assessments. It provides phase-specific recommendations to optimize products throughout their life cycle, from design to disposal, fostering sustainability. Incorporating the OCEAN model, the API personalizes feedback based on target audience traits, enhancing user satisfaction and product success across diverse markets.

Despite their advantages, these tools have limitations. The web-based Smart City Pentagon Framework Analyzer may face accessibility challenges in regions with limited internet infrastructure. Additionally, the API’s effectiveness depends on the quality of input data; incomplete or poorly defined product details can result in less accurate recommendations, emphasizing the importance of thorough product documentation.

Scalability and integration with existing smart city frameworks also present challenges. Aligning the Analyzer and API with globally recognized standards, such as LEED for Cities or the Smart Sustainable Cities Assessment Framework [18], could enhance their applicability and ensure universally accepted recommendations. Incorporating these standards would provide users with benchmarks to measure their products against international sustainability and smartness metrics, ensuring broader compliance across diverse urban contexts.

4.3. Anticipated Penta-S Characteristics for Product Materials

There are standard materials in the described products—flexible solar panels, batteries, electronic components, video cameras, servo motors, control systems, wearable components, and VR devices—each face significant challenges. For instance, solar panels and batteries often involve non-recyclable or toxic materials, while electronic components and advanced imaging equipment contribute to electronic waste and hazardous disposal issues. Servo motors and actuators are difficult to recycle, and control systems can be energy-intensive. Wearable and 3D-printed components, as well as VR devices, may also pose recycling challenges. These issues highlight the urgent need for sustainable design and resource-efficient practices to mitigate environmental impact and improve material management.

Materials defined by the Pentagon Framework represent a progressive approach, surpassing traditional functionalities by being innovative, efficient, ethically produced, eco-friendly, responsive, technologically integrated, and user-safe. Therefore, the identified materials can be described according to the Pentagon Framework presented (see Figure 18).

5. Conclusions

The development of smart cities requires a comprehensive approach that combines advanced technologies to create efficient and sustainable urban environments that improve residents’ quality of life. Traditional methods for product development often fail to address the complexities involved in smart city projects, which must balance various factors like sustainability, technological innovation, and social inclusion. The Pentagon Framework (S5: smart, sustainable, sensing, social, safe) offers a well-rounded method for evaluating and developing products for smart cities, ensuring they are advanced, environmentally friendly, socially inclusive, and secure. This framework employs a structured methodology to assess products across five critical dimensions, allowing for early evaluations and continuous improvement during their life cycle. Tools such as the Smart City Pentagon Framework Analyzer and the Penta-S Compliance Assistant API enhance the framework’s effectiveness by providing real-time feedback and specific recommendations tailored to each stage of the product life cycle.

Key Contributions of the Pentagon Framework include the following:

• A balanced assessment across various dimensions considering technological, environmental, and social aspects.
• Support for early evaluations and ongoing enhancements, enabling products to adapt over time to meet sustainability goals.
• Integrating personality traits through a matching algorithm allows for solutions tailored to individual user preferences, increasing engagement.

6. Future Work

Future developments of the Pentagon Framework will address the environmental impact of materials used in smart city products, particularly regarding recyclability and disposal challenges. The framework aims to assess products for long-term viability and energy efficiency by adopting circular economy principles. Incorporating genetic algorithms will facilitate a thorough analysis of product features, ensuring enhancements in one area do not negatively affect others. While the framework offers substantial advantages, it also faces challenges, such as dependence on web-based platforms and the necessity for accurate input data. It could further benefit from alignment with widely recognized sustainability standards to enhance its global applicability. The Pentagon Framework serves as a valuable tool for creating solutions for smart cities by focusing on comprehensive evaluation, sustainability, and personalized design. Future iterations will aim to align with globally recognized sustainability standards, improving its applicability across diverse urban environments. The Pentagon Framework will continue to evolve, integrating new technologies and advanced optimization techniques, ensuring its effectiveness in addressing the complex challenges of modern smart cities.