| 1. | Fog computing-based IoT architecture for smart cities [9] | Enhances urban coordination and efficiency by enabling scalable, low-latency data processing through a layered fog computing network. | Requires complex infrastructure deployment and integration with existing urban systems. |
| 2. | Analytical framework for data-driven smart sustainable cities [12] | To harness sensor-based big data applications for enhancing urban sustainability through optimized planning, operations, and governance | Lacks empirical validation and may not fully address practical implementation challenges or contextual limitations across diverse urban settings. |
| 3. | fsQCA [13] | To identify and analyze diverse business model configurations in IoT platforms for smart city development. | Limited generalizability due to small sample size and reliance on qualitative project data. |
| 4. | SCADA-based IoT and BDA system [15] | To monitor, analyze, and optimize underwater operation safety and water management in smart cities using real-time sensor data. | High infrastructure and technical resource requirements for data processing, storage, and real-time analysis. |
| 5. | IoT infrastructure for smart cities [16] | To enable smart city applications by leveraging IoT architectures and wireless communication technologies for improved urban living and sustainability. | Faces significant security and privacy challenges due to large-scale device deployment and communication vulnerabilities. |
| 6. | Occupancy-driven ML-based on-street parking pricing scheme [17] | To predict parking lot occupancy and dynamically determine parking prices using ML models for efficient parking management in smart cities. | Real-world effectiveness depends on accurate, real-time data availability and model adaptability to dynamic urban conditions. |
| 7. | IoT-based urban waste management system using cuckoo search-optimized LSTM [18] | To optimize waste collection and routing in smart cities by analyzing waste data using a Cuckoo Search-enhanced LSTM model. | Performance depends heavily on the quality of IoT sensor data and may face scalability and real-time processing challenges in larger urban settings |
| 8. | Edge computing framework [19] | It processes IoT data locally to enable real-time situation awareness in smart cities. | It may face challenges handling extremely large-scale heterogeneous data efficiently. |
| 9. | Integrated optimization-simulation framework for scalable SC and relocation of SAEVs [20] | To optimize the real-time relocation and SC of SAEV fleets based on dynamic electricity prices, aiming to reduce charging costs, carbon emissions, and improve operational efficiency by integrating transport and power grid systems | Assumes unlimited charging station capacity and static transport demand, limiting realism and dynamic pricing integration. |
| 10. | SC Framework for SAEV Fleets. | Optimize charging to reduce energy costs and peak demand by shifting SAEV charging to low-price or renewable energy periods. | Assumes fixed trip patterns and may not fully capture real-time demand fluctuations or infrastructure constraints. |
| 11. | IoT-based real-time smart traffic monitoring system with AR [22] | Enhance traffic management, safe navigation, and pollution control by integrating IoT sensors and AR in smart cities. | Dependent on existing IoT infrastructure and may face challenges in scalability and real-time data processing under heavy traffic conditions |
| 12. | Smart rent portal with recommendation system visualized by AR [23] | To simplify rental property search by combining preference-based recommendation with AR visualization for enhanced user experience. | Limited to memory-based recommendation accuracy and depends on user adoption of AR and blockchain technologies. |
| 13. | Smart street lighting system [24] | To reduce energy costs and enhance public safety through wireless networked LED streetlights with centralized and remote control in smart cities. | Vulnerable to cybersecurity threats due to IoT device integration and wireless mesh network exposure. |
| 14. | MSKU campus AR prototype [25] | To enhance smart campus urbanization by visually presenting campus features and real-time external data using AR and image detection. | Limited to specific campus areas and reliant on external data accuracy and availability for real-time information. |
| 15. | AR-IoT accessibility system for motor disabilities [26] | To empower wheelchair users to interact independently with out-of-reach physical items in smart cities using AR and RFID technologies. | Limited by the availability of RFID-tagged inventory and AR interface usability across diverse user impairments. |
| 16. | Big data and ontology-based energy management system [27] | To reduce energy consumption in smart cities by integrating big data, ontology, and multiagent systems for improved interoperability and intelligent energy management. | Complexity in integrating heterogeneous data sources and scalability challenges in real-time energy management across large urban environments. |
| 17. | Edge computing-based short-term energy prediction system [28] | To provide real-time, accurate short-term energy prediction in smart cities by distributing data acquisition, processing, and prediction across edge and central nodes using IoT and DL. | Challenges in handling heterogeneous IoT data and reliance on the efficiency of distributed edge computing infrastructure. |
| 18. | Intelligent vehicle network system and smart city management using genetic algorithms and image perception [29] | To optimize urban traffic and city management by using genetic algorithms and image perception for efficient data processing and energy-saving traffic signal control. | Depends on the accuracy of image perception and requires high computational resources for distributed and parallel processing. |
