Key enabling technologies for smart city development: a comprehensive overview

A smart city relies on IoT and sensor technologies to monitor environmental conditions and transmit data for processing. By enabling real-time data collection and informed decision-making, these systems foster efficient, sustainable, and responsive urban environments, thereby enhancing quality of life and supporting broader socio-economic growth [8].

Several IoT-driven models and techniques have been proposed to advance smart city development as discussed in Table 4. Fog computing-based architectures [9] enable scalable, low-latency processing but require complex infrastructure [10], while analytical frameworks for data-driven sustainability [11, 12] leverage sensor-based big data yet lack empirical validation. Approaches, such as fuzzy set qualitative comparative analysis (fsQCA), [13] support business model analysis but face limited generalizability, whereas SCADA-based IoT with big data analytics (BDA) [14, 15] enhances water management at the cost of high infrastructure demands. IoT infrastructures [16] improve urban living but remain vulnerable to security and privacy risks. ML-based parking schemes [17] and waste management models using Cuckoo Search-optimized LSTM [18] enhance efficiency, though their effectiveness depends on real-time data quality and scalability. Edge computing frameworks [19, 20] facilitate localized data processing and short-term energy prediction but struggle with heterogeneous large-scale data. Integrated optimization-simulation frameworks [21] and smart charging (SC) systems improve shared autonomous electric vehicle (SAEV) fleet management yet often assume static demand and unlimited infrastructure. Augmented reality (AR)-based solutions, including traffic monitoring [22], rental portals [23], smart campuses [24], and accessibility systems [25], enhance user experience but face adoption, scalability, and accuracy challenges. Similarly, smart street lighting [26] improves energy efficiency and safety but is susceptible to cyberattacks. Finally, big data–ontology energy management systems [27] and genetic algorithm-based vehicle networks [28] optimize energy use and traffic control, though both face interoperability, scalability, and computational limitations.

The rapid growth of heterogeneous data in modern systems has surpassed the capacity of traditional data management approaches, necessitating advanced technologies for efficient handling and organization. BDA enables predictive analytics, maintenance, and real-time decision-making [29,30,31,32], while IoT infrastructures enhance data collection and support informed urban management [33]. The integration of IoT and BDA ensures effective data processing, storage, and insight generation across smart city services [34], leveraging diverse data sources to improve planning, management, and sustainability. Bibri [35] emphasized BDA as a scalable and synergistic asset for empowering smart cities, forming a critical component of next-generation urban intelligence. Furthermore, IoT–BDA integration optimizes resource utilization and monitors device conditions, with applications in healthcare, transportation, and disaster management [36, 37]. Complementing these insights, McAfee et al. [32] positioned big data as a management revolution, Gao et al. [33] developed blockchain-based secure payment mechanisms for vehicle-to-grid systems, Ahmed et al. [34] examined BDA in IoT, and Bibri [35] highlighted its role in sustainability. Practical applications include underwater safety management [36] and disaster resilience [37], though challenges remain in scalability, infrastructure demands, and real-time deployment.

WSNs are crucial to a smart city framework because they allow for sophisticated sensing, data translation, and transmission. Qureshi et al. [38] proposed a genetic programing–based framework for detecting routing attacks in RPL-enabled industrial IoT, enhancing security in smart city environments but facing scalability and resource limitations. In contrast, Yick et al. [39] provided a seminal survey of WSNs, highlighting key challenges such as energy efficiency, coverage, and data aggregation, though its relevance is limited by the absence of recent IoT and 5G advancements. It plays an essential role in smart city ecosystems as it facilitates resource efficiency and improves the quality of service (QoS) delivered to citizens [40, 41]. Smart urban environments are particularly suited for large-scale monitoring through the integration of mobile ad hoc networks (MANETs) and cost-effective, easily deployable WSNs [42]. Such WSN deployments can support diverse applications, including load balancing, routing, industrial process supervision, energy optimization, and real-time monitoring of environmental and physical conditions [43, 44]. Prior research has also emphasized WSN-based smart city solutions for scheduling and routing, particularly in smart grid applications, with a focus on achieving both energy efficiency and QoS [45].

5G networks are central to digital transformation in smart cities, offering adaptability, high bandwidth, low latency, and massive connectivity that enable real-time data sharing, large-scale IoT integration, and enhanced public services [46]. By supporting ubiquitous sensing and data-rich applications, 5G facilitates sustainable urban development across sectors, such as transportation, energy, healthcare, and manufacturing [47,48,49,50]. Unlike 4G, its performance meets the demands of applications requiring ultra-reliable low-latency communication, including autonomous driving and 4K streaming [50]. However, realizing such services depends on uninterrupted IoT connectivity, which remains limited due to incomplete 5G coverage, high deployment costs, and reliance on non-standalone architectures where 4G cores restrict true 5G speeds. Within this context, Skouby and Lynggaard [46] proposed a four-layer architecture combining 5G, IoT, AI, and CoT, while Le-Dang and Le-Ngoc [16] emphasized IoT infrastructures as the backbone of smart cities. Yang et al. [47] systematically mapped 5G applications, noting opportunities in edge computing and public safety but highlighting limited real-world deployments. Advanced approaches such as Mohammed et al.'s [48] UbiPriSEQ, using deep reinforcement learning to balance privacy, security, energy, and QoS, show promise but face computational challenges. Similarly, Gohar and Nencioni [49] and Bagheri et al. [50] demonstrated 5G's potential in intelligent transportation and autonomous driving, while Hashem et al. [51] and Ogbodo et al. [52] provided broader surveys on urban computing and 5G–LPWAN integration. Collectively, these studies highlight both the transformative potential of 5G for smart cities and the persistent gap between theoretical promise and practical deployment.

AI and ML are central to the development of smart cities, enabling predictive and preventive decision-making by processing and interpreting massive data streams generated through machine-to-machine communication and IoT devices [53, 54]. AI advances urban sustainability and inclusivity by identifying patterns in complex data, supporting real-time decision-making, and aligning with UN Sustainable Development Goals [55, 56]. It is increasingly applied in urban systems, from analyzing sensor and camera data to optimizing infrastructure, transportation, and healthcare services [57,58,59,60]. When combined with other technologies, such as blockchain, cognitive-LPWAN, and fog-to-cloud middleware, AI enhances security, connectivity, and service orchestration while reducing reliance on third parties [61,62,63]. Application-specific studies highlight its versatility: ML and DL improve parking space prediction and dynamic pricing [64], bio-inspired models, such as the Cuckoo Search support smart waste management [65], and AI-driven analytics enhance smart healthcare, and vehicular communications [66, 67]. Despite these advances, significant challenges remain in scalability, privacy, uncertainty management, and real-world validation. Collectively, the literature underscores AI and ML as indispensable enablers of sustainable, data-driven, and inclusive smart cities.

Cloud and edge computing have emerged as foundational enablers of smart city ecosystems, offering scalability, low latency, and resource efficiency. Foundational works by Armbrust et al. [68] and Mell [69] established the principles of cloud computing, while Botta et al. [70] emphasized its integration with IoT to support ubiquitous services. However, cloud-only models face latency, bandwidth, and privacy constraints, motivating edge paradigms such as mobile edge computing (MEC) [71, 72], fog computing [73,74,75], and hybrid cloud–fog architectures [76]. These approaches aim to reduce delay, enhance context-awareness, and support real-time decision-making for applications such as smart transportation [77], industrial IoT [78], and UAV-enabled agriculture [79]. Security and privacy challenges remain critical, as noted by Darwish et al. [80] and Chen et al. [81], particularly in blockchain- and fog-based deployments. While techno-economic analyses [82] and social impact assessments [83] highlight sustainability and cost considerations, systematic surveys underline ongoing gaps in standardization, resource orchestration, and large-scale deployment. Collectively, these studies demonstrate that cloud and edge computing provide the computational backbone for smart cities but require context-specific, secure, and cost-effective implementations. Looking forward, convergence with AI, blockchain, and 6G is expected to drive adaptive, intelligent, and autonomous infrastructures, enabling more resilient, sustainable, and citizen-centric smart urban environments.

Blockchain technology enables shared data management and autonomous peer-to-peer connectivity among IoT devices. Its integration in a smart city context assures that information flows securely, transparently, robustly, immutably, and verified. This technology has enormous potential to be a promising concept for smart city applications [84, 85], as discussed in Table 5. It has become a key component of trust [86, 87] and a creative way to improve the integrity and confidentiality of data in smart cities [88]. It can ease the difficulties in data management and security in smart cities. It is built on a distributed architecture where trusted centralized authority does not oversee transactions; all network members handle them effectively and flexibly [89, 90]. Future smart networks can be framed and upgraded, durability increased, and an expanding number and variety of services can be secured by leveraging blockchain's decentralization, immutability, and accountability [91, 92]. It may offer a practical solution to important security issues and enable the functioning of smart cities [93, 94]. A smart city could be organized into subareas known as smart blocks, which are made up of different IoT devices, such as sensors and cameras, inside a certain area that a block administration monitor. This makes it easier to administer smart city services on a large scale. Rahman et al. [95] discussed a blockchain-enabled sustainable IoT framework for a sharing system defined by the safe sharing of services. Kotobi and Sartipi [96] investigated a blockchain to protect communication among smart city sensors and household appliances to address data security issues with IoT applications in smart cities. According to the research, numerous innovative applications, including smart buildings, smart transportation, and smart healthcare, can be facilitated by blockchain's shared and autonomous design for vulnerability, security, privacy, and fault tolerance [97]. For instance, Buzachis et al. [98] integrated blockchain technology with the Edge of Things, the convergence of edge computing, and IoT to prevent traffic vehicle crashes, especially at junctions. For smart transport, Hirtan et al. [99] proposed a blockchain-based reputation system that safeguards users' privacy, improves commute tracks, and guarantees information security and dependability. A blockchain-based IoT solution was created by Jabbar et al. [100] to enable safe communication in the Internet of Vehicles. According to Nguyen et al. [101], it enables intelligent transport by enabling automobile data sharing. To reduce end-to-end delays, technological advancements can enhance current smart transportation system's QoS by offering a decentralized management platform that allows automobiles and roadside equipment to share and transfer data on a peer-to-peer architecture with no automobile authority. It is presented by Liu et al. [102] as a distributed system to store peer-to-peer transaction data about electric automobiles. The use of blockchain technology for trustworthy power trading authentication and a decentralized energy ledger that does away with the requirement for dependable third parties is also taken into consideration by Gao et al. [103]. In conclusion, blockchain technology and the IoT are anticipated to support novel approaches for the smart city's transport sector. Deploying 5G and a software-defined network (SDN) can enhance the general effect on blockchain technology and the IoT. The SDN method guarantees supply and anonymity while bolstering blockchain security against malevolent actors [104]. The combination of SDN with blockchain offers an effective architecture for sustainable smart cities [105] and enhances cybersecurity in networks of IoT devices [106]. Additionally, it provides several answers to the problems that 5G networks provide. According to Azzaoui et al. [107], the technology is based on AI, and 5G contributes to creating more intelligent, effective, and secure wireless networks.

GIS enhances urban development in a smart city system. It incorporates numerous major difficulties, including accurately locating the underlying entities and getting geographic data to facilitate real-time decision-making. Technologies, such as LiDAR and satellite photography, web mapping, GPS, and GIS, enable intelligent transportation, intelligent parking systems, effective healthcare services, smart navigational devices, and man-aging extra public assets. Geographic technological advances are critical in building smart urban amenities because they facilitate practical cooperation and coordination among the various operations and facets of smart city systems, such as healthcare, emergency services, transportation, agriculture, waste management, tracking services, and navigation [108, 109].

Smart grids are advanced power networks capable of dynamically adjusting and readjusting to deliver energy at low cost and high quality [110]. Since the energy demand of IoT applications is considerable, smart city solutions need to use energy more efficiently and introduce effective energy prediction systems that reflect the dynamics of the IoT environment [111]. In the face of the many challenges related to energy management in smart cities, smart metering solutions have been widely studied in the literature because they facilitate the calculation of the number of utilities needed and enable consumers to track their energy usage, thereby also contributing to improved energy conservation [112]. As an integral part of an automated metering infrastructure, smart metering provides new tools for increasing data availability [113], monitoring energy consumption, and energy forecasting [114]. Lee and Lee [115] posited that information security is an essential aspect of IoT smart meters that needs to be considered during the development of smart grid applications. As a result, subsequent research must focus on the design, installation, and maintenance difficulties connected with smart meters. Although IoT-cloud networks can help save computing resources and reduce service latency [116], cloud data centers are still complex and not highly scalable. As a result, storage facilities must become more intelligent and capable of acquiring real-time information on power supply, use, and demand. The following study should investigate whether IT facilities use forecasting algorithms and optimization techniques to reduce energy costs.

The comprehensive review of the existing research enables us to choose several options that will help smart cities transition to sustainable modes of transport. Here is a discussion of a few research papers on smart city mobility and transportation. Adrian and Fantana believe the analytic hierarchy process (AHP) approach is crucial for resolving logistics and transportation issues. It is important to remember that the AHP algorithm can be applied to various challenges, including urban sustainability difficulties, and as such, it may be used in future research [117]. According to Popescu [118], the Box-Jenkins approach's appealing qualities and multiplicative ARIMA models' ability to analyze time series of traffic accidents adequately describe the evidence. They are helpful for modeling and predicting traffic injury rates. In the meantime, by implicitly altering usage patterns for transportation and mobility solutions in an equitable and ecologically friendly manner, such data analysis and modeling aid in designing and managing a more sustainable urban transportation system. It would be impossible to continue the conversation about evolving transportation technology without considering concerns, such as new transportation networks and behavioral shifts. DYNAMAP, a European Life project, aims to model the real noise caused by car traffic in urban and suburban areas by developing a dynamic acoustic map based on a few low-cost permanent noise monitoring stations [119]. The authors carried several hypothetical replicas that demonstrated the significance of transportation electrified in mitigating global warming. They concluded that while switching to electrified vehicles under the shared socioeconomic path enables better outcomes in terms of the low-carbon transition, transport electrification alone is not helping to reduce the negative environmental impact. Marvin et al. addressed the same issue in their discussion of the evidence-based strategies created to improve traffic management efficacy by transforming control systems and infrastructure assets [120]. To significantly enhance traffic flow parameters and, as a result, reduce the adverse environmental impact, the research focuses on analyzing challenging road network segments in a medium-sized city. Zhang and Chen [121] highlighted significant distinctions among both urban and rural locations to this degree and highlight some sustainable redevelopment criteria, including the geographical context, regional government assistance, and expenditures. Iacobucci et al. [122] suggested flexible and valuable methods to enhance the performance of SAEVs, such as smart batteries with flexible power prices that are easily integrated into an optimizer. Regarding energy efficiency, gas emissions, and noise levels, inland waterway transportation [123] is thought to be the most resource-intensive of all the modes of transportation in an extensive transport system.

AR use in tourism for smart cities has not been thoroughly investigated, hence studies on the subject are unavailable. According to Akdu [124], it can be used in various settings, such as lodging facilities, food and beverage establishments, and museums. When combined with other technologies, such as social networking services (SNSs), virtual reality (VR), beacon technology, geo-tag services, location-based technologies, mobile apps, big data, and cloud computing, AR is giving companies in smart cities new avenues for advertising, creative joint ventures, improved tourism services, and improved methods of managing tourist flows, allowing them to innovate beyond conventional industry boundaries [125]. In the pilot area of Gokova Mugla, Turkey, an AR-based prototype is examined in Demir and Karaarslan [126]. To introduce sightseeing spots, lodging facilities, dining options, tourist attractions, and other significant hubs to domestic and foreign travelers, a mobile application prototype was created utilizing AR in their study. In addition to using location data and image processing techniques to develop AR technology, the mobile application supplied key information about these regions. The use of AR for traffic monitoring in smart cities is examined [32]. The researchers aimed to create a smart traffic control system that can generate route status and traffic information based on automobile strength in smart cities by combining several contemporary technologies, such as AR, cloud computing, ML, and the IoT. Satapathy et al. [127] examined the application of AR visualization for smart rent portals in smart cities. Their research suggests a recommender system controlled and displayed using AR and Vuforia to give users a platform to conduct a cooperative filtering search for rental properties based on preferences. The benefits of a lighting management system with remote control capabilities for smart cities are demonstrated in Jin et al. [128]. These benefits include real-time control and monitoring capabilities, lower energy usage, and operating expenses. Utilizing an AR Interface, Cho et al. [129] shows how this innovative EMS system can allow users to verify operation, energy consumption, renewable energy production, and environmental information of the zone where the systems are situated. Ozcan et al. [130] tried to investigate an AR application for smart campus urbanization utilizing a prototype campus at MuglaSitkiKocman University (MSKU). The application of AR and IoT increases the accessibility of smart city environments for individuals with mobility disabilities [131].

Automated systems and robotics are increasingly central to smart city development, offering transformative applications in governance, social care, and urban monitoring. Drone-based studies, such as those by Choi-Fitzpatrick and Juskauskas [132] and Floreano and Wood [133], demonstrate the potential of autonomous aerial systems for tasks ranging from population estimation to environmental sensing, while raising questions of scalability, safety, and societal acceptance. van Wynsberghe et al. [134] emphasized the ethical and responsible deployment of drones in public service contexts, aligning automation with broader societal needs. In the domain of assistive robotics, Prescott and Caleb-Solly [135] highlighted connected care ecosystems that support independent living, underscoring robotics' role in addressing demographic shifts and social care demands. Parallel to these technological innovations, urban scholarship has examined the implications of automation and digital infrastructures for governance and inclusivity. Barns et al. [136] and Barns [137] explored how data platforms and digital interfaces reconfigure urban governance, while Crampton [138] and Leszczynski with Elwood [139] interrogated the political and participatory dimensions of spatial media. The conceptualization of the “digital skin of cities” by Rabari and Storper [140] frames automated sensing and ubiquitous data collection as central to smart urbanism, though not without tensions of surveillance and equity, further elaborated by Leszczynski's critique of platform urbanism. Collectively, these works situate robotics and automation as both technological drivers and socio-political constructs within smart city ecosystems, highlighting their capacity to enhance efficiency and care, while also necessitating critical governance and ethical oversight.