Systematic Mapping of Smart Cities

• The paper presents an extensive systematic mapping that categorizes varied conceptual frameworks, modeling techniques, and technological infrastructures in smart cities.
• It highlights smart cities as complex urban systems integrating ICT, IoT, and participatory governance to enhance efficiency and sustainability.
• The study identifies key challenges such as interoperability, scalability, and security, urging standardized, multi-disciplinary frameworks for future research.

A smart city is broadly defined as an urban environment leveraging an integrated suite of digital technologies (notably ICTs, IoT, edge/cloud computing, and data-driven governance) to optimize urban processes, enhance sustainability, and support citizen-centric services. The concept encompasses technical, social, economic, environmental, and governance dimensions, resulting in a complex, dynamic system subject to rapid innovation, evolving research paradigms, and diverse implementation challenges. Systematic mapping studies dissect this field by collecting, categorizing, and analyzing the range of approaches, solutions, models, and open questions that define contemporary smart city research.

1. Conceptual Frameworks and Definitions

Systematic mapping studies reveal the absence of a canonical or universally accepted definition of the smart city. Early definitions focused on cybernetic controls and energy efficiency, later broadening to incorporate economic growth, social well-being, and sustainability ("From elusive to ubiquitous: understanding smart cities" (Barina et al., 2020)). Recent work emphasizes a dual view: smart cities as both cities with pervasive fixed ICT (infrastructure, sensors, cloud/data centers) and a locus of dynamic, variable citizen-ICT interactions (mobile engagement, participatory services).

Researchers extend the Boyd Cohen Smart City Wheel with additional axes (e.g., "Smart Things") and note further layering (perception, network, application; or perceptive, network, platform, behavioral, as in governance studies for developing countries (Tan et al., 2020)). Contemporary models emphasize the importance of integrating social justice and participatory governance (e.g., Critical Systems Heuristics applied in Toronto (McCord et al., 2019)) with digital innovation.

A schematic representation of this multi-factorial conceptualization is common: $$SC = f(\text{Fixed ICT Infrastructure},\ \text{Variable Citizen-ICT Interaction},\ \text{Governance},\ \text{People})$$ with models for benchmarking (e.g., z-score normalization) to compare progress across cities and domains (Kabuya et al., 14 Feb 2024).

2. Modeling Approaches and Taxonomies

Systematic mapping reveals a diversity of modeling strategies and a lack of uniform reference architectures ("A Systematic Mapping Study on Smart Cities Modeling Approaches" (Rossi et al., 22 Aug 2025)). The most employed modeling paradigms are:

• Business Modeling: Canvas-based frameworks capturing value networks, organizational processes, and stakeholder engagement.
• Architectural Modeling: Reference architectures (often IoT-based), big-data system designs, and multi-layered representations encompassing device, network, data, and service layers.
• Ontology-Based Modeling: Formal ontologies articulate semantic relationships, enabling integration and interoperability of heterogeneous data sources.
• Less common paradigms include mathematical, data/3D, behavioral, service-based, and multi-agent models.

Mapping studies classify research by dimensions (e.g., Smart Governance, Smart Environment, Smart People), application fields (city management, ICT, quality, stakeholder engagement), and technology readiness. Most models are conceptual, with few (if any) reaching full operational maturity (TRL 9).

Research is dispersed across many publication venues, reflecting the interdisciplinary and fragmented nature of the field. This motivates calls for hybrid, multi-paradigm models and tool-supported, model-driven engineering (MDE) to drive standardization, integration, and scalability.

3. Technological Foundations and Infrastructures

Smart cities are underpinned by layered digital infrastructures, typically organized as:

• Perception Layer: Sensor networks (RFID, cameras, environmental sensors), data acquisition at the edge.
• Network Layer: Communication backbones (WiFi, Zigbee, cellular, LPWAN, 5G, fiber-optic), supporting massive device connectivity and strict latency requirements ("KIGLIS: Smart Networks" (Bogdoll et al., 2021); "Using 5G in Smart Cities" (Yang et al., 2022)).
• Application Layer: Data analytics, decision support, city services (mobility, waste, energy, public safety).
• Edge and Cloud Computing: Edge/fog deployments enable low-latency, context-aware analytics; cloud layers aggregate and process large-scale urban data (Khan et al., 2019).

Architectural evaluations (e.g., (Fahmideh et al., 2020)) highlight the need for solutions supporting scalability, security, availability, interoperability, and modularity. Comparative studies (e.g., FIWARE vs. IBM vs. ESPRESSO) document varying coverage for event, socio-technical, or physical "city" layers and stress that hybridization may be necessary to reach comprehensive, domain-appropriate designs.

The IoT is foundational, but the integration of edge/cloud paradigms, AI-driven analytics, and network innovations (e.g., SDN/NFV, network slicing, 5G, digital twins) is key for advanced applications and performance guarantees.

4. Applications, Use Cases, and Participatory Methods

Smart cities span a spectrum of applications:

Domain	Representative Use Cases	Key Technologies
Urban Mobility	Real-time routing, smart parking, V2X comms, autonomous driving	5G, edge AI, sensor nets
Environment	Pollution monitoring, flood prediction, smart grids	IoT, GIS, UAVs, blockchain
Governance & Services	Citizen feedback, participatory budgeting, digital governance	Open data platforms, MDE
Health & Social	Telemedicine, epidemic response, well-being metrics	IoT, AI analytics, crowdsourcing
Economy & Industry	Smart supply chains, energy management, industrial IoT, predictive maintenance	Data analytics, automation
Living & Community	Smart homes, adaptive lighting, responsive public spaces	Smart devices, context-aware services

Crowdsourcing and citizen science are increasingly fundamental for data acquisition, feedback, and urban experimentation, leveraging mobile and web platforms for reporting, idea generation, and collaborative urban planning (Vahdat-Nejad et al., 2022, Feher, 2020).

Participatory and empowerment-oriented frameworks call attention to “smartmentality”—the collective, proactive engagement of citizens. Systematic network/content analyses reveal links between engagement, open data, and ethical privacy considerations for durable, adaptive urban systems.

5. Governance, Social Justice, and Inclusive Ecosystem Design

Systematic literature reviews emphasize that technological innovation must be embedded in broader socio-economic, legal, and regulatory reform, especially in developing cities (Tan et al., 2020, Kabuya et al., 14 Feb 2024). Key points:

• Governance: Must be inclusive and coordinated, overcoming agency fragmentation with unified data governance and regulatory clarity for security, privacy, and accountability.
• Social Justice: Public participation mechanisms often remain tokenistic; genuine emancipatory potential depends on shifting boundary judgments, as shown in critical analyses of projects like Sidewalk Toronto (McCord et al., 2019).
• Capacity Building: Research and education are foundational; initiatives should develop intellectual capital and local ontologies, especially where infrastructural deficits persist.
• Frameworks: Holistic models (e.g., urban service, technology, value, organization, governance; SMARTify (Romualdo-Suzuki et al., 2020)) articulate how digital platforms, data, and business strategies coalesce to foster collaboration and resilience.

Integration between the "smart city" and "smart village" paradigms is proposed as an inclusive, context-sensitive approach for developing regions, ensuring that both urban and rural populations benefit from investments in ICT and capacity development (Kabuya et al., 14 Feb 2024).

6. Challenges, Open Issues, and Research Directions

Systematic mapping exposes substantial technical and institutional challenges:

• Interoperability and Integration: The heterogeneity of devices, data formats, standards, and legacy systems impedes seamless data flow and system synergy (Silva et al., 27 Feb 2025, Kumar et al., 2023).
• Scalability and Real-World Validation: Existing conceptual models and prototypes rarely transition to city-wide operational deployments (most remain in TRL 2–8, rarely TRL 9; (Rossi et al., 22 Aug 2025)).
• Security and Privacy: The proliferation of devices heightens cyber-physical vulnerabilities, mandating robust encryption, authentication, privacy-centric design, and resilient credential management (Kumar et al., 2023, Ishaq et al., 2023).
• Data Governance: Defining ownership, standards, and sharing protocols is essential for enabling trustworthy, scalable platforms (Romualdo-Suzuki et al., 2020).
• Stakeholder Alignment: Participation from both private and public sectors—as well as start-ups, citizen groups, and academia—is critical for long-term sustainability and innovation.
• Urban-Rural and International Contexts: Geography crucially shapes both development trajectories and international promotion/policy strategies; research shows divergent models (global promotion, emerging, top-down) tied to region-specific conditions (Marchesani et al., 16 Jul 2025).

Identified gaps include the need for standardized evaluation metrics, multi-paradigm/cross-disciplinary modeling approaches, the deep inclusion of stakeholder and economic dimensions, and more empirical, industrial case studies (Yang et al., 2022, Rossi et al., 22 Aug 2025).

Future work is directed toward:

• Hybrid architectural and modeling frameworks integrating technical, business, and social aspects.
• Advanced analytics (AI, digital twins, blockchain) supporting predictive, adaptive city management.
• Expansion into underexplored domains (sustainable agriculture, wildfire prevention, health).
• Exploiting model-driven engineering and software product lines for modular, configurable deployments.

7. Impact and Synthesis

Mapping studies confirm that smart city research is highly interdisciplinary, methodologically diverse, and characterized by rapid growth and fragmentation. The six core smart city dimensions—economy, mobility, environment, people, living, and governance—must be orchestrated as a cohesive ecosystem where ICT acts as an enabler but not a substitute for human and social capital (Marchesani, 16 Jul 2025).

Successful smart city implementation depends on:

• Holistic integration of digital and social innovation.
• Dynamic orchestration among urban subsystems (synergistic effects).
• Human-centric, inclusive governance and stakeholder engagement.
• Ongoing adaptation and learning based on real-world deployments and feedback.

Smart cities thus represent not merely an automation of urban life, but a reshaping of city ecosystems in which technology, people, and governance dynamically interact to optimize efficiency, equity, and resilience. Systematic mapping studies illuminate both the breadth and depth of these interconnected issues, providing a structured lens for guiding future research, policy, and practice.