Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions
Abstract
:1. Introduction
- Smart economy: This axis comprises economic competitiveness, such as tourism services and outdoor digital marketing. This includes innovation, entrepreneurial business, and digital currency.
- Smart governance: this axis consists of the services that serve the citizens in a smart city, allowing for the participation of citizens in political opinions and authority performance.
- Smart environment: this axis characterizes natural conditions and resources, e.g., weather, green areas, energy, and water, to reduce bad effects, better utilize resources, and meet nations’ sustainability policies.
- Smart living: this axis considers the quality of life, including many aspects such as culture, safety, security, health, and education.
- Smart mobility: this axis considers transportation aspects, including safe transportation sustainability and infrastructure systems.
- Smart people: this axis is related to human and social technology solutions. It is introduced to get people to be more creative, qualified, and engaged in life-long learning.
2. Methodology
2.1. Eligibility Criteria
- -
- The degree of relevance to the context of communication networking for smart city applications: The selected articles are research and survey works that consider communication networks for smart cities. This included physical, network, and application layers protocol design and algorithms.
- -
- The data of the publications: All included articles were published over the past five years. More than 80% of the considered works were published in the past two years.
- -
- The rank of the journal or conference: All selected articles are conference or journal articles. Also, technical papers of well-known organizations in the field of ICT, including 3GPP and ITU, were considered. The selected conference papers are of ranked conferences in the Scopus database, and the included journal articles are of high-ranked journals in both Scopus and Web of Science (WoS) databases. Most of the selected journal articles were published in a first- or second-quartile (Q1 or Q2) journal, as indexed by the WoS 2023 report.
- -
- The articles should cover all the topics of the survey: Mainly, the articles cover the topics of smart city applications, specifications of communication networks of smart cities, and challenges in designing communications networks for smart city applications. Moreover, the articles covered the considered key enabling technologies of smart cities, including 5G, AI, IoT, blockchain, edge computing, AI, virtualization, and SDN. Figure 2 summarizes the main topics of the considered articles.
2.2. Problem Statement
- Cost of operation: All organizations depend on innovations for managing data, such as cloud storage and virtualization, but these innovations provide higher costs because of the rapidly rising amount of data. New technologies and high-level software and hardware tools are needed to develop efficient solutions; therefore, smart city operators strive to create effective solutions through cost-effective strategies.
- Numerous devices: Smart cities require numerous sensors to collect data from various aspects of urban life. These sensors play a crucial role in transforming cities into intelligent, data-driven ecosystems. The proliferation of numerous devices in smart city development poses significant issues that should be handled to ensure smart cities’ effective and sustainable implementation. Communication networks of smart cities are anticipated to support dense deployment, which introduces many constraints on the design and development of such networks. These constraints include data management, ensuring coverage requirements, achieving network availability, and managing the communication overhead. Ensuring seamless communication and integration among numerous and heterogeneous smart city devices requires standardized interfaces and protocols.
- Scalability: Network scalability refers less to the quality of the network than to its efficiency and capacity to accommodate growth with little impact on performance. The scalability of a network is measured by its ability to accommodate the growing number of users, devices, or services without degrading overall performance.For a network to expand effectively, it must maintain its efficiency, even as its capacity or resources increase. As the network grows to meet rising demand, it is important that its performance stays steady or even improves. Large businesses, data centers, cloud infrastructure, and telecommunications networks are just a few examples of settings where expansion is expected, making scalability a crucial factor in network design.
- Big data (sources, characteristics, and quality): Developers must utilize big data tools to manage, categorize, and maintain heterogeneous data formats, including audio, photos, video, and text, into structured data formats. The complexity and size of data generated by smart city applications and the diversity of end devices pose challenges for software tools in effectively managing and processing such data. It is difficult to handle issues such as data assessment, data management, data development, system architecture, distributed big data mining, data visualization, data compression, and data secrecy.Maintaining some part of the generated data would not be productive if it is irrelevant or redundant. Various resources are essential for data retention and storage. Quality data should only be stored in distributed databases to provide effective data maintenance. The issue for developers is to preserve a higher level of consistency, heterogeneity, and data integrity.
- Security: Smart city sensors are deployed for surveillance, monitoring, and other critical purposes. Thus, protecting smart city data from malicious attacks is critical. The large-scale deployment of devices in a smart city creates an extensive attack surface for potential cyber threats and privacy breaches. Moreover, smart cities integrate various systems through multiple networks, which increases the risk of potential attacks. Thus, it is essential to implement security approaches to save data, devices, and the entire smart city infrastructure from cyber-attacks.
- Infrastructure: Smart cities face several infrastructure challenges that should be resolved for sustainable growth. It employs a wide range of sensors to monitor various aspects, including environmental parameters, traffic, waste management, and energy. Deploying and maintaining these sensors throughout the city’s infrastructure requires careful planning, installation, and maintenance to ensure accurate and continuous data collection. Smart city initiatives often involve retrofitting existing infrastructure with technology-driven solutions. Integrating new technology with legacy infrastructure is challenging and needs coordination. Urban planning should focus on harmonizing physical infrastructure with digital systems to optimize resource utilization and improve overall city functioning.Smart city infrastructure should be designed to accommodate future growth and expansion. As the city evolves and new technologies emerge, the infrastructure must be scalable and adaptable. Integrating different systems and platforms to enable seamless communication and interoperability between various components is also challenging. Also, the infrastructure should be designed to minimize the environmental impact and improve resilience against natural disasters and other unforeseen events. Implementing eco-friendly solutions and disaster preparedness measures can address these challenges.
- Connectivity: Smart cities should rely on robust and reliable connectivity to enable seamless data flow between devices, sensors, and infrastructure. Ensuring full coverage across the city can be a significant challenge, particularly in areas with limited infrastructure or remote locations. Moreover, ensuring connection availability over time with dense deployment requires special consideration while designing the communication networks of smart cities.
2.3. Aim and Objectives
- A.
- Reviewing state-of-the-art smart city applications
- B.
- Providing challenges of the evolution of smart cities
- C.
- Introducing key enabling technologies of future smart cities
3. Challenges with Smart City Applications
- Dense deployment: The development of future smart city applications necessitates the provision of dense deployment, thus imposing numerous limits on network development. With a high density of devices and sensors, there is a high risk of network congestion. This can result in data transmission delays, increased latency, and decreased system efficacy overall. It is essential to ensure efficient network management and traffic prioritization. Dense deployments in IoT-based smart city networks can cause signal interference in regions with limited spectrum availability. This interference can reduce the quality of data transmission and compromise network dependability.Moreover, managing, processing, and analyzing the massive amount of data resulting from dense deployment is a challenging dilemma. For efficient network performance, efficient data storage, processing, and analytics are required. Accommodating rising demands through scalable dense deployments becomes vital as the city and its needs expand. Incorporating scaling considerations into the design of systems is crucial for ensuring their long-term sustainability.
- Security and privacy: This is critical to secure the smart city’s infrastructure against cyberattacks. As cyber dangers grow, it becomes more challenging to maintain an adequate level of protection. With a large number of networking points and devices distributed over smart cities, network security becomes a major problem. The protection of sensitive data and the prevention of unwanted access or cyberattacks are significant priorities. Concerns about privacy and security may be increased by collecting and processing massive volumes of data from various sources. It is a continual challenge to protect sensitive data while allowing users to access it.
- Infrastructure: the infrastructure required to enable smart city applications, such as high-speed Internet access, sensor networks, and power sources, may be costly and time-consuming to build.
- Scalability: Smart city systems must be scalable in terms of both user numbers and data volume. Designing systems that can grow effectively to assure long-term profitability is critical.
- Cost of installation: the cost of installation of smart city applications can vary widely depending on various factors, including the size of the city, the scope of the project, the complexity of the applications, and the existing infrastructure.
- Reliability: Most smart city applications require ultra-reliable transmission of less than 10−5. This is challenging with the existing connections and interfaces. Thus, smart cities should introduce novel ways to ensure data reliability, including edge computing, AI, and SDN.
- Energy efficiency: The growing use of smart city technologies may lead to substantial increases in energy demand. Thus, designing energy-efficient systems and including renewable energy sources is crucial. Moreover, many smart city applications involve deploying battery-operated devices that should be efficiently managed.
- Latency: The delay of communicated data represents challenges with modern communication technologies. Many smart city applications are classified as ultra-reliable low-latency communications (uRLLC), which requires considerable work on network design to meet the latency demands.
4. Software-Defined Network (SDN)
4.1. SDN Architecture
- Data plane:This contains packet-forwarding devices and is linked to the control plane via an appropriate interface. The interface is a communication channel that links the controller and application plane or the control and data planes. A southbound interface (SBI) is an interface to or from the controller and network devices, such as Open-Flow [38]. A northbound interface (NBI) is a channel to or from a controller and upstream SDN-aware applications. The east/westbound interface is the communication protocol used to connect the distributed SDN controllers [39].Figure 7. The general structure of SDN technology [40].
- Control plane:This contains the network operating system (NOS), which orchestrates the operations and the computations. The SDN controller is the network brain, which engages the network application and services, control plane containers, and database in which information is gathered. The SDN controller handles the routing, manages the interference, and allocates the security functions when finding weird attitudes [41].It is the powerful plane of SDN network architecture, which manages and configures network traffic. The main requirement of the SDN controller is the flexibility to configure remotely [41]. It is responsible for getting useful information from devices and communicating with the network’s abstracting view to SDN applications. Many routing schemes are commonly used with the control plane, including open shortest path first (OSPF), routing information protocol (RIP), and enhanced interior gateway routing protocol (EIGRP), which are managed with IPV4 and IPV6 using the control plane [35].When the switch receives a packet and checks the matching on the entry table, it acts when there is a corresponding opening in the table. Otherwise, the switch forwards the packet to the SDN controller, which makes the forwarding decision and sends the switch the corresponding decision [41].
- Application layer:This is above the control plane and is responsible for providing the admin with the network status, accessing the collected data, and offering many other SDN benefits. It is used in business platforms to manage network operations based on the business point of view [35].
4.2. SDN for Smart Cities
5. Network Function Virtualization (NFV)
- -
- NFV reduces the cost of the network infrastructure by using virtual resources, i.e., virtual machines (VMs), and replacing the hardware boxes.
- -
- NFV optimizes end users provisioning resources with high QoS and ensures the performance of virtual network functions (VNFs), e.g., low latency.
- -
- NFV has higher flexibility than traditional networks because network operators use new services over the same hardware platform and can dynamically deliver services to customers based on their demands via NFV performance gradation. Software and hardware do various functions because of their separation.
- -
- NFV decreases energy consumption by merging the network devices.
- -
- NFV does not need additional hardware since the network operator can activate the software over virtual resources, e.g., VMs, and containers.
- -
- NFV increases security since the main architecture components of NFV, e.g., virtual infrastructure manager (VIM), can be embedded with security methods.
- Virtual network function (VNF): VNF is responsible for the software’s functionality to execute network operations specified by the infrastructure of the NFVI on one or multiple VMs.
- Network function virtualization infrastructure (NFVI): The NFVI has software and hardware resources to manage and support carrier networks, processing, virtual storage, and VNFs.
- NFV management and orchestration (NFV-MANO): NFV-MANO provides an architectural framework between services of interfaces of VNFs and NFVI and orchestrates their respective sub-components.
6. Edge Computing
6.1. Cloud Computing and Edge Computing
6.2. MEC for Smart Cities
- Local storage: Edge computing offers the ability to offload massive amounts of data from entities to edge servers. The server provides local storage for these massive amounts of data. It offers various temporary storage approaches for different types of data.
- Local computing: Edge computing helps to offload the process and computation from less complex devices, such as smartphones, to edge servers. MEC servers deploy intelligent computing methods and can implement artificial intelligence (AI) algorithms to facilitate computing processes.
- Local data analysis: MEC provides real-time and critical data analysis of the massive amounts of collected data from different smart city applications. Various data analysis schemes can be deployed locally in a smart way. When edge computing undertakes local data analysis, it minimizes the delay in forwarding data to the remote cloud and waiting for the response.
6.3. Challenges of Deploying MEC for Smart City
7. Fog Computing
- Delay-sensitive and location awareness: Fog nodes communicate with each other since fog nodes are location aware. Thus, fog computing analyzes the information faster than other cloud paradigms and suggests the best path with the lowest delay.
- Real-time response: fog computing analyzes the data at the edge, and thus, applications’ time and latency-sensitivity functions come closer.
- Fog node’s agility and scalability: the network and data load changes when fog computing at groups and levels integrates resources.
- Heterogeneity: fog computing supports all data types as different data collections and processing.
- Geographical distribution: unlike the centralized cloud, fog nodes are distributed geographically.
- Mobility: Fog nodes can support a low level of mobility, which other paradigms cannot achieve. This is due to the small size and weight of fog servers that can be embedded in moving entities.
7.1. Fog Computing for Smart Cities
7.2. Challenges of Deploying Fog Computing for Smart Cities
- (1)
- Virtualization technology selection
- (2)
- Privacy and security
- (3)
- System management
- (4)
- Mobility model
8. Internet of Things (IoT)
- Physical and data link layer
- II.
- Gateway and network layer
- III.
- Transport layer
- IV.
- Application layer
8.1. IoT Connectivity
- Short-range technologiesShort-range communication interfaces provide coverage for a maximum of tens of meters. It is primarily suitable for indoor IoT applications. In the following, we consider the most common short-range interfaces [84,85,86].
- BluetoothBluetooth has long been recognized for its ability to stream enormous amounts of information. It is the optimal solution for personal IoT devices, such as activity trackers and wearables. Additionally, Bluetooth is made with low-power IoT gadgets. It is ideally suited for tools that offer small data values. These systems are configured to save power if data are not transmitted. Due to the narrow communication range and heavy battery consumption, it is used in a restricted number of industrial projects. However, these cons have been tackled in Bluetooth Low Energy (BLE).
- ZigBeeZigbee is a wireless communication technology designed for low-power, short-range, and low-data-rate applications. It is based on the IEEE 802.15.4 standard, which defines the protocol’s physical (PHY) and medium access control (MAC) layers. This standard operates in the 2.4 GHz band, although some regional variations use other frequencies. It fits well for many indoor IoT applications, including smart homes and smart lighting systems.
- IEEE 802.11 (Wi-Fi)Wi-Fi is the optimal choice when transferring massive files, with a transfer rate 20 to 30 times higher than Bluetooth. Wi-Fi was not made for IoT networks; however, there are two currently improved IEEE standards, namely, 802.11ah and 802.11ax, for IoT applications. Sensors, which are critical components of IoT networks, run on batteries and send small quantities of data across vast areas. As a result, they require a different type of connection.
- NFCNFC has a very short communication range, typically around a few centimeters. This close proximity requirement ensures that NFC communication is intentional and secure. It is a vital solution for secure and straightforward communication between electronic devices, e.g., smartphones. NFC operates at radio frequencies of 13.56 MHz, which is within the high-frequency (HF) band. This frequency band is unlicensed. NFC is commonly used for contactless payments, enabling users to make transactions by tapping their NFC-enabled payment cards, smartphones, or wearable devices on a compatible point-of-sale (POS) terminal.
- Longe-range technologies
- Unlicensed technologies
- (1)
- IEEE 802.11ah (Wi-Fi HaLow): IEEE 802.11ah technology enhances Wi-Fi by providing greater range and lower power connectivity. Wi-Fi HaLow satisfies the needs of the IoT to enable a range of use cases in commercial, industrial, residential, and public settings.Wi-Fi HaLow supports the low-power connectivity required for applications such as wearables and sensor networks. Its range is longer than many other IoT technology solutions in tough locations where the ability to pass through walls or other barriers is a key factor. It offers a more reliable connection.
- (2)
- IEEE 802.11ax.: Wi-Fi 6 (IEEE 802.11ax) is the new generation of Wi-Fi technology focusing on efficiency and performance. Wi-Fi 6 technology is all about better and more efficient use of the existing radio frequency medium. It was introduced for video streaming, online gaming, and high-bandwidth applications.
- (3)
- Low-power wide-area network (LPWAN): It is becoming increasingly well-liked in the industrial and research communities due to its low-power, long-range, and low-cost communication properties. Rural areas allow for long-distance communication up to 40 km, but in urban areas, it is only 1–5 km. Furthermore, it has a long battery life (over ten years) and is quite affordable. Therefore, IoT applications that send small amounts of data over large distances are ideal for LPWAN.LPWAN is in its infancy, and its maximum capabilities and drawbacks will not be visible until the networks are established on a larger scale. Furthermore, the LPWAN currently supports no more than 20% of the people worldwide. This decreased adoption rate impedes LPWAN from being the optimal solution in the upcoming five years. Nevertheless, LPWAN accessibility is rapidly increasing, and by 2022, it is predicted that it will cover 100% of the world’s population.Sigfox communication was introduced for the low-cost M2M application areas in which broad coverage is necessary. The Sigfox wireless interface permits any communications with a low power usage level. Therefore, it is optimal for remote devices requiring a power supply for prolonged periods without changing their batteries.
- Energy-associated communications, such as smart metering.
- Transportation, which may include automobile management.
- Home and consumer goods.
- B.
- Licensed technologies
- (1)
- NB-IoT: NB-IoT is based on narrowband radio technology and is standardized by the third-generation partnership project (3GPP). Under permitted frequency bands, NB-IoT can coexist with the GSM (global system for mobile communications) and LTE (long-term evolution) (e.g., 700 MHz, 800 MHz, and 900 MHz). The frequency band NB-IoT has a span of 200 KHz [87].With a high data rate and low latency, narrowband IoT (NB-IoT) is a long-range communication technology enabling several IoT devices and applications. In addition, NB-IoT is a price-efficient solution with a long battery life and better coverage [87].
- (2)
- LTE CAT 1 and LTE CAT 4: Both are popular LTE IoT communication technologies. The key difference between LTE Cat 4 and LTE Cat 1 is their data transmission rate and prices. LTE Cat 4, with a maximum downlink rate of 150 Mbps and an uplink rate of 50 Mbps, has a better intel high data rate market by simultaneously communicating a greater volume of data, whereas the LTE Cat 1 IoT solution presents its advantages with its amazing cost performance in the medium-rate market [84].The LTE Cat 1, along with the LTE Cat 4, also relies on the same existing 4G LTE network, which means adopting LTE Cat 1 communication technology will cost no extra deployment investment on the network operator’s side. Moreover, taking advantage of the technological maturity and global coverage of the 4G network, LTE Cat 1 has a strong and reliable network foundation to empower various IoT applications and scenarios [84].
8.2. IoT for Smart Cities
9. Artificial Intelligence (AI)
9.1. AI for Smart Cities
- (1)
- AI-optimized hardware
- (2)
- Speech recognition
- (3)
- Deep learning
- (4)
- Robotic process automation
- (5)
- Image/visual recognition
9.2. Available Smart City Datasets
- I.
- Intelligent transportation applications
- -
- Traffic flow optimization.
- -
- Demand forecasting for transit systems.
- -
- Route planning and recommendations.
- -
- Predicting congestion and traffic incidents.
- II.
- Environmental monitoring applications.
- -
- Air/water quality monitoring.
- -
- Predict pollution levels.
- -
- Issue warnings for extreme weather or hazardous conditions.
- III.
- Smart grid applications
- -
- Detect leaks and outages.
- -
- Optimize resource allocation.
- -
- Implement demand-side management strategies.
- IV.
- Public services
- -
- Improve city services.
- -
- Respond more quickly to citizen requests.
- -
- Assess citizen satisfaction.
- V.
- Surveillance applications
- -
- Automated incident detection.
- -
- Object tracking and recognition.
- -
- Pedestrian flow analysis.
9.3. Challenges of Deploying AI for Smart Cities
- Infrastructure and cost: AI systems need powerful hardware, databases, and energy to function, raising the overall cost.
- Privacy concerns: people might feel offended and worried about their privacy and personal space.
- Risk of socialization: When developing such cities, inclusive urbanization, which addresses the growing vulnerability of the slums and poor population, must be prioritized. No population should be left out of the big data collection for the AI systems; it must be ensured. Including members of all ages, genders, classes, and socioeconomic groups in society is crucial.
10. Blockchain
- Transparency: A blockchain’s decentralized nature ensures that all transactions within a smart city network are recorded in an immutable and transparent manner. This transparency helps identify unauthorized or fraudulent activities, improving security.
- Data integrity and authentication: By leveraging a blockchain, smart cities can ensure the integrity and authenticity of their data. Through cryptographic techniques and consensus algorithms, a blockchain can verify the origin and validity of data, preventing tampering or unauthorized modification.
- Secured identity management: Blockchain technology provides a robust framework for managing digital identities securely. Individuals and devices can verify and store their identities on a blockchain, ensuring that only authorized entities can access sensitive information or perform specific actions.
- Enhanced IoT security: IoT devices, an integral part of smart cities, can introduce security vulnerabilities. A blockchain can establish a decentralized and secure network for IoT devices, eliminating the need for a central point of control and reducing the risk of cyberattacks.
- Peer-to-peer transactions: A blockchain’s inherent capabilities allow secure peer-to-peer transactions without intermediaries. This eliminates the need for trust in centralized authorities.
- Data consent: Various stakeholders need access to specific data in a smart city ecosystem. Blockchain-based solutions can enable controlled data sharing with the use of smart contracts. Individuals or organizations can grant permission for data access, ensuring privacy while fostering collaboration.
11. Current Studies and Future Directions
11.1. Current Research
11.2. Research Directions
- A.
- Developing AI/ML algorithms to assist smart city applications
- Algorithms for overcoming resource and hardware limitations.In order for smart city applications to run smoothly on devices, mainly IoT devices, with limited processing power, it is crucial to build AI/ML algorithms that are both lightweight and efficient with resources. This involves optimizing algorithms for edge computing environments to save end nodes’ resources. This includes deploying techniques, such as model quantization and pruning, to reduce the size and complexity of AI/ML models so they can be implemented for devices with limited resources.Another way to overcome resource limitations is to seek an efficient way of using them. AI/ML can play a major role in this direction by designing novel methods for dynamically allocating resources according to demand to optimize performance while reducing resource wastage. This includes using reinforcement learning to develop adaptive resource allocation schemes for IoT-based smart city applications.
- Algorithms for enhancing communication network efficiency.AI/ML can significantly improve the network performance of different smart city applications, mainly to meet the demands of 5G. Algorithms for controlling and predicting network traffic are critical components, allowing for the intelligent distribution and allocation of network resources based on consumption patterns. Another critical aspect is the development of algorithms for assessing data reliability. AI/ML algorithms can analyze and verify data integrity, identifying and mitigating potential issues, including data corruption and tampering. This increases the reliability of communications over smart cities in a way that meets the 5G demands.Another direction is the development of adaptive machine learning models that can change with smart city settings as they change. This means that the models should keep learning and adjusting based on new data to work in dynamic circumstances. In addition, this direction can include AI/ML algorithms that prioritize important data flow, guaranteeing that critical services receive the necessary bandwidth and resources during instances of heightened demand. Also, AI/ML algorithms can assist in developing QoS schemes that adaptively modify data prioritization according to the real-time demands of various applications.Furthermore, AI/ML can increase the network availability of smart city applications. AI/ML algorithms can be used for predicting potential network failures or disruptions, allowing for proactive measures to maintain network availability. This meets the novel demands of zero-touch networks that can assist many smart city applications.
- B.
- Deploying distributed edge computing for smart city applicationsThis includes novel structures of edge units and novel interfaces with other network parts. Moreover, the development of associated algorithms with the deployment of edge computing is an important direction. These algorithms should meet the demands of future smart cities. The main directions regarding distributed edge deployment for smart cities can be summarized as follows:
- Implementing a microservices-based architecture for edge nodes, breaking smart city applications into modular, independently deployable services. This enhances scalability, maintainability, and flexibility.
- Utilizing containerization technologies, e.g., docker, to encapsulate each microservice, ensuring consistency across edge units and easing deployment and scaling processes.
- Employing orchestration solutions, e.g., Kubernetes, to manage and scale the deployment of containers across distributed edge units seamlessly.
- Establishing standardized APIs for communication between edge units and other network components.
- Design edge computing infrastructure that can easily scale to accommodate the growing demands of smart city applications.
- Using 5G technologies to connect edge units and central data centers at high speeds and low latency.
- Implementing load-balancing algorithms to distribute computational workloads efficiently between edge units, preventing resource bottlenecks and optimizing overall system performance.
- Developing algorithms for intelligent data offloading between edge units and centralized cloud resources, optimizing data processing based on different parameters, including network congestion and application requirements.
- Introducing the paradigm of green edge computing by integrating energy-efficient computing practices into the design, utilizing low-power hardware, and implementing algorithms that optimize energy consumption.
- C.
- Managing the massive network traffic via intelligent coreThis includes developing SDN networks efficiently for handling massive traffic of different smart city applications. Also, this direction includes developing associated network algorithms to facilitate the operation of SDN controllers. Moreover, developing API for network operators to facilitate managing smart city networks.An SDN has many remaining issues that can be considered for massive deployment networks. The research on this part of smart city networks can involve the following:
- Creating a distributed control plane architecture that enables effective resource management and coordination in response to the changing needs of different applications for smart cities. A multi-controller scheme should be deployed for such core networks.
- Developing novel ways for communication between SDN controllers and avoiding communication overhead.
- Developing intelligent schemes, i.e., AI-based schemes, for load balancing between SDN controllers.
- Using network slicing to make virtualized, separate parts of the communication infrastructure. This will allow different smart city applications personalized and the best communication paths. This includes creating methods for dynamic network slicing, enabling the effective allocation and deallocation of resources according to the evolving demands of various applications over time.
- Developing adaptive routing protocols that dynamically adjust to network topology change and traffic patterns. This includes integrating machine learning algorithms for anomaly detection in network behavior, allowing the SDN infrastructure to adapt to emerging patterns.
- Developing intelligent algorithms for proactively detecting, predicting, and diagnosing network faults and failures to ensure ultra-high network availability.
- Developing novel reliable APIs for SDN networks using existing solutions, e.g., OpenAPI and Swagger.
- D.
- Developing novel NFV approaches for future smart cities able to meet the required network scalability and cost efficiencies
- E.
- Innovating novel frameworks for integrating all smart city applications over a single platform
- F.
- Ways to assist dense deployment and ultra-reliable latency communications required by most smart city applications
12. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Verhulsdonck, G.; Weible, J.L.; Helser, S.; Hajduk, N. Smart Cities, Playable Cities, and Cybersecurity: A Systematic Review. Int. J. Hum. Comput. Interact. 2023, 39, 378–390. [Google Scholar] [CrossRef]
- Singh, T.; Solanki, A.; Sharma, S.K.; Nayyar, A.; Paul, A. A Decade Review on Smart Cities: Paradigms, Challenges and Opportunities. IEEE Access 2022, 10, 68319–68364. [Google Scholar] [CrossRef]
- Jia, Q.-S.; Panetto, H.; Macchi, M.; Siri, S.; Weichhart, G.; Xu, Z. Control for Smart Systems: Challenges and Trends in Smart Cities. Annu. Rev. Control 2022, 53, 358–369. [Google Scholar] [CrossRef]
- Alhalafi, N.; Veeraraghavan, P. Exploring the Challenges and Issues in Adopting Cybersecurity in Saudi Smart Cities: Conceptualization of the Cybersecurity-Based UTAUT Model. Smart Cities 2023, 6, 1523–1544. [Google Scholar] [CrossRef]
- Pandya, S.; Srivastava, G.; Jhaveri, R.; Babu, M.R.; Bhattacharya, S.; Maddikunta, P.K.R.; Mastorakis, S.; Piran, M.J.; Gadekallu, T.R. Federated Learning for Smart Cities: A Comprehensive Survey. Sustain. Energy Technol. Assessments 2023, 55, 102987. [Google Scholar] [CrossRef]
- Alshamaila, Y.; Papagiannidis, S.; Alsawalqah, H.; Aljarah, I. Effective Use of Smart Cities in Crisis Cases: A Systematic Review of the Literature. Int. J. Disaster Risk Reduct. 2023, 85, 103521. [Google Scholar] [CrossRef]
- Singh, S.K.; Jeong, Y.S.-S.; Park, J.H. A Deep Learning-Based IoT-Oriented Infrastructure for Secure Smart City. Sustain. Cities Soc. 2020, 60, 102252. [Google Scholar] [CrossRef]
- Akhter, F.; Khadivizand, S.; Siddiquei, H.R.; Alahi, M.E.E.; Mukhopadhyay, S. IoT Enabled Intelligent Sensor Node for Smart City: Pedestrian Counting and Ambient Monitoring. Sensors 2019, 19, 3374. [Google Scholar] [CrossRef]
- Bohli, J.-M.; Skarmeta, A.; Victoria Moreno, M.; Garcia, D.; Langendorfer, P. SMARTIE project: Secure IoT data management for smart cities. In Proceedings of the 2015 International Conference on Recent Advances in Internet of Things (RIoT), Singapore, 7–9 April 2015; pp. 1–6. [Google Scholar]
- Qiao, L.; Li, Y.; Chen, D.; Serikawa, S.; Guizani, M.; Lv, Z. A survey on 5G/6G, AI, and Robotics. Comput. Electr. Eng. 2021, 95, 107372. [Google Scholar] [CrossRef]
- Mukhopadhyay, S.; Suryadevara, N.K. Smart Cities and Homes: Current Status and Future Possibilities. J. Sens. Actuator Netw. 2023, 12, 25. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. Int. J. Surg. 2021, 88, 105906. [Google Scholar] [CrossRef] [PubMed]
- Sánchez-Corcuera, R.; Nuñez-Marcos, A.; Sesma-Solance, J.; Bilbao-Jayo, A.; Mulero, R.; Zulaika, U.; Azkune, G.; Almeida, A. Smart Cities Survey: Technologies, Application Domains and Challenges for the Cities of the Future. Int. J. Distrib. Sens. Netw. 2019, 15, 155014771985398. [Google Scholar] [CrossRef]
- Ismagilova, E.; Hughes, L.; Dwivedi, Y.K.; Raman, K.R. Smart Cities: Advances in Research—An Information Systems Perspective. Int. J. Inf. Manage. 2019, 47, 88–100. [Google Scholar] [CrossRef]
- Jawhar, I.; Mohamed, N.; Al-Jaroodi, J. Networking Architectures and Protocols for Smart City Systems. J. Internet Serv. Appl. 2018, 9, 26. [Google Scholar] [CrossRef]
- Simmhan, Y.; Ravindra, P.; Chaturvedi, S.; Hegde, M.; Ballamajalu, R. Towards a Data-Driven IoT Software Architecture for Smart City Utilities: IoT Software Architecture (Under review). Softw. Pract. Exp. 2018, 48, 1390–1416. [Google Scholar] [CrossRef]
- Haque, A.K.M.B.; Bhushan, B.; Dhiman, G. Conceptualizing Smart City Applications: Requirements, Architecture, Security Issues, and Emerging Trends. Expert Syst. 2022, 39, e12753. [Google Scholar] [CrossRef]
- Molokomme, D.N.; Onumanyi, A.J.; Abu-Mahfouz, A.M. Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges. J. Sens. Actuator Netw. 2022, 11, 47. [Google Scholar] [CrossRef]
- Dileep, G. A Survey on Smart Grid Technologies and Applications. Renew. Energy 2020, 146, 2589–2625. [Google Scholar] [CrossRef]
- Syed, D.; Zainab, A.; Ghrayeb, A.; Refaat, S.S.; Abu-Rub, H.; Bouhali, O. Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications. IEEE Access 2021, 9, 59564–59585. [Google Scholar] [CrossRef]
- Kirimtat, A.; Krejcar, O.; Kertesz, A.; Tasgetiren, M.F. Future Trends and Current State of Smart City Concepts: A Survey. IEEE Access 2020, 8, 86448–86467. [Google Scholar] [CrossRef]
- Bellini, P.; Nesi, P.; Pantaleo, G. IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies. Appl. Sci. 2022, 12, 1607. [Google Scholar] [CrossRef]
- Javed, A.R.; Shahzad, F.; ur Rehman, S.; Zikria, Y.B.; Razzak, I.; Jalil, Z.; Xu, G. Future Smart Cities: Requirements, Emerging Technologies, Applications, Challenges, and Future Aspects. Cities 2022, 129, 103794. [Google Scholar] [CrossRef]
- Kim, H.; Choi, H.; Kang, H.; An, J.; Yeom, S.; Hong, T. A Systematic Review of the Smart Energy Conservation System: From Smart Homes to Sustainable Smart Cities. Renew. Sustain. Energy Rev. 2021, 140, 110755. [Google Scholar] [CrossRef]
- Al Dakheel, J.; Del Pero, C.; Aste, N.; Leonforte, F. Smart Buildings Features and Key Performance Indicators: A Review. Sustain. Cities Soc. 2020, 61, 102328. [Google Scholar] [CrossRef]
- Muthanna, A.; Shamilova, R.; Ateya, A.A.; Paramonov, A.; Hammoudeh, M. A Mobile Edge Computing/Software-defined Networking-enabled Architecture for Vehicular Networks. Internet Technol. Lett. 2020, 3, e109. [Google Scholar] [CrossRef]
- Ameur, A.I.; Lakas, A.; Yagoubi, M.B.; Oubbati, O.S. Peer-to-Peer Overlay Techniques for Vehicular Ad Hoc Networks: Survey and Challenges. Veh. Commun. 2022, 34, 100455. [Google Scholar] [CrossRef]
- Afrin, T.; Yodo, N. A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System. Sustainability 2020, 12, 4660. [Google Scholar] [CrossRef]
- Singh, A.K.; Anand, A.; Lv, Z.; Ko, H.; Mohan, A. A Survey on Healthcare Data: A Security Perspective. ACM Trans. Multimed. Comput. Commun. Appl. 2021, 17, 1–26. [Google Scholar] [CrossRef]
- Tawalbeh, L.; Muheidat, F.; Tawalbeh, M.; Quwaider, M.; Abd El-Latif, A.A. Edge Enabled IoT System Model for Secure Healthcare. Measurement 2022, 191, 110792. [Google Scholar] [CrossRef]
- Rana, D.S.; Dhondiyal, S.a.; Chamoli, S.K. Software Defined Networking (SDN) Challenges, Issues and Solution. Int. J. Comput. Sci. Eng. 2019, 7, 884–889. [Google Scholar] [CrossRef]
- Volkov, A.; Proshutinskiy, K.; Adam, A.B.M.; Ateya, A.A.; Muthanna, A.; Koucheryavy, A. SDN load prediction algorithm based on artificial intelligence. In Communications in Computer and Information Science; Springer International Publishing: Cham, Switzerland, 2019; pp. 27–40. ISBN 9783030366247. [Google Scholar]
- Alam, I.; Sharif, K.; Li, F.; Latif, Z.; Karim, M.M.; Biswas, S.; Nour, B.; Wang, Y. A Survey of Network Virtualization Techniques for Internet of Things Using SDN and NFV. ACM Comput. Surv. 2021, 53, 1–40. [Google Scholar] [CrossRef]
- Ateya, A.A.; Muthanna, A.; Vybornova, A.; Algarni, A.D.; Abuarqoub, A.; Koucheryavy, Y.; Koucheryavy, A. Chaotic Salp Swarm Algorithm for SDN Multi-Controller Networks. Eng. Sci. Technol. Int. J. 2019, 22, 1001–1012. [Google Scholar] [CrossRef]
- Rahouti, M.; Xiong, K.; Xin, Y. Secure Software-Defined Networking Communication Systems for Smart Cities: Current Status, Challenges, and Trends. IEEE Access 2021, 9, 12083–12113. [Google Scholar] [CrossRef]
- Kaur, K.; Mangat, V.; Kumar, K. A Comprehensive Survey of Service Function Chain Provisioning Approaches in SDN and NFV Architecture. Comput. Sci. Rev. 2020, 38, 100298. [Google Scholar] [CrossRef]
- Rabet, I.; Selvaraju, S.P.; Fotouhi, H.; Alves, M.; Vahabi, M.; Balador, A.; Björkman, M. SDMob: SDN-Based Mobility Management for IoT Networks. J. Sens. Actuator Netw. 2022, 11, 8. [Google Scholar] [CrossRef]
- Boukraa, L.; Mahrach, S.; El Makkaoui, K.; Esbai, R. SDN southbound protocols: A comparative study. In Lecture Notes on Data Engineering and Communications Technologies; Springer International Publishing: Cham, Switzerland, 2023; pp. 407–418. ISBN 9783031151903. [Google Scholar]
- Latif, Z.; Sharif, K.; Li, F.; Karim, M.M.; Biswas, S.; Wang, Y. A Comprehensive Survey of Interface Protocols for Software Defined Networks. J. Netw. Comput. Appl. 2020, 156, 102563. [Google Scholar] [CrossRef]
- Jenifa, A. Software-Defined Networking (SDN) Explained in 5 Minutes or Less. Available online: https://geekflare.com/software-defined-networking/ (accessed on 10 September 2023).
- Ateya, A.; Muthanna, A.; Gudkova, I.; Abuarqoub, A.; Vybornova, A.; Koucheryavy, A. Development of Intelligent Core Network for Tactile Internet and Future Smart Systems. J. Sens. Actuator Netw. 2018, 7, 1. [Google Scholar] [CrossRef]
- Priyadarsini, M.; Mittal, P.; Bera, P. Smart city renovation using SDN framework. In Proceedings of the 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), Bengaluru, India, 7–11 January 2020. [Google Scholar]
- Rbii, E.; Jemili, I. Leveraging SDN for smart city applications support. In Communications in Computer and Information Science; Springer International Publishing: Cham, Switzerland, 2020; pp. 95–119. ISBN 9783030658090. [Google Scholar]
- Manisha, A.; Suresh Reddy, G.; Sahoo, K.S. Software-defined industrial IoT for smart city applications. In Software-Defined Networking for Future Internet Technology; Apple Academic Press: New York, NY, USA, 2021; pp. 237–253. ISBN 9781003145721. [Google Scholar]
- Abounassar, E.M.; El-Kafrawy, P.; Abd El-Latif, A.A. Security and interoperability issues with internet of things (IoT) in healthcare industry: A survey. In Studies in Big Data; Springer International Publishing: Cham, Switzerland, 2022; pp. 159–189. ISBN 9783030854270. [Google Scholar]
- Muhizi, S.; Ateya, A.A.; Muthanna, A.; Kirichek, R.; Koucheryavy, A. A novel slice-oriented network model. In Developments in Language Theory; Springer International Publishing: Cham, Switzerland, 2018; pp. 421–431. ISBN 9783319986531. [Google Scholar]
- Basu, D.; Datta, R.; Ghosh, U. Softwarized network function virtualization for 5G: Challenges and opportunities. In Internet of Things and Secure Smart Environments, 1st ed.; Ghosh, U., Ed.; Chapman & Hall: London, UK, 2020; pp. 147–192. ISBN 9780367276706. [Google Scholar]
- Zong, Y.; Feng, C.; Guan, Y.; Liu, Y.; Guo, L. Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges. Sensors 2020, 20, 2655. [Google Scholar] [CrossRef]
- Ramakrishnan, J.; Shabbir, M.S.; Kassim, N.M.; Nguyen, P.T.; Mavaluru, D. A Comprehensive and Systematic Review of the Network Virtualization Techniques in the IoT. Int. J. Commun. Syst. 2020, 33, e4331. [Google Scholar] [CrossRef]
- Yang, G.; Shin, C.; Yoo, Y.; Yoo, C. A Case for SDN-based network virtualization. In Proceedings of the 2021 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Houston, TX, USA, 3–5 November 2021; pp. 1–8. [Google Scholar]
- Paolino, M.; Carrozzo, G.; Betzler, A.; Colman-Meixner, C.; Khalili, H.; Siddiqui, S.; Sechkova, T.; Simeonidou, D. Compute and network virtualization at the edge for 5G smart cities neutral host infrastructures. In Proceedings of the 2019 IEEE 2nd 5G World Forum (5GWF), Dresden, Germany, 30 September–2 October 2019; pp. 560–565. [Google Scholar]
- Rangsietti, A.K.; Kodali, S.S.P. SDN-enabled Network Virtualization and Its Applications. Softw. Defin. Netw. 2022, 231–277. [Google Scholar] [CrossRef]
- Ateya, A.A.; Alhussan, A.A.; Abdallah, H.A.; Al Duailij, M.A.; Khakimov, A.; Muthanna, A. Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks. Comput. Syst. Sci. Eng. 2023, 45, 1775–1787. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, Z.; Xia, S.; Chen, H.-H. A Load-Balanced Re-Embedding Scheme for Wireless Network Virtualization. IEEE Trans. Veh. Technol. 2021, 70, 3761–3772. [Google Scholar] [CrossRef]
- Sunyaev, A. Cloud computing. In Internet Computing; Springer International Publishing: Cham, Switzerland, 2020; pp. 195–236. ISBN 9783030349561. [Google Scholar]
- Lv, Z.; Xiu, W. Interaction of Edge-Cloud Computing Based on SDN and NFV for next Generation IoT. IEEE Internet Things J. 2020, 7, 5706–5712. [Google Scholar] [CrossRef]
- Pham, Q.-V.; Fang, F.; Ha, V.N.; Piran, M.J.; Le, M.; Le, L.B.; Hwang, W.-J.; Ding, Z. A Survey of Multi-Access Edge Computing in 5G and beyond: Fundamentals, Technology Integration, and State-of-the-Art. IEEE Access 2020, 8, 116974–117017. [Google Scholar] [CrossRef]
- Khakimov, A.; Elgendy, I.A.; Muthanna, A.; Mokrov, E.; Samouylov, K.; Maleh, Y.; El-Latif, A.A.A. Flexible Architecture for Deployment of Edge Computing Applications. Simul. Model. Pract. Theory 2022, 114, 102402. [Google Scholar] [CrossRef]
- Osama, M.; Ateya, A.A.; Ahmed Elsaid, S.; Muthanna, A. Ultra-Reliable Low-Latency Communications: Unmanned Aerial Vehicles Assisted Systems. Information 2022, 13, 430. [Google Scholar] [CrossRef]
- Hong, C.-H.; Varghese, B. Resource Management in Fog/Edge Computing: A Survey on Architectures, Infrastructure, and Algorithms. ACM Comput. Surv. 2020, 52, 1–37. [Google Scholar] [CrossRef]
- Galletta, A.; Ruggeri, A.; Fazio, M.; Dini, G.; Villari, M. MeSmart-pro: Advanced Processing at the Edge for Smart Urban Monitoring and Reconfigurable Services. J. Sens. Actuator Netw. 2020, 9, 55. [Google Scholar] [CrossRef]
- Mahmood, O.A.; Abdellah, A.R.; Muthanna, A.; Koucheryavy, A. Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT. Information 2022, 13, 328. [Google Scholar] [CrossRef]
- Zhou, S.; Wei, C.; Song, C.; Pan, X.; Chang, W.; Yang, L. Short-Term Traffic Flow Prediction of the Smart City Using 5G Internet of Vehicles Based on Edge Computing. IEEE Trans. Intell. Transp. Syst. 2022, 24, 1–10. [Google Scholar] [CrossRef]
- Sarker, I.H. Smart City Data Science: Towards Data-Driven Smart Cities with Open Research Issues. Internet Things 2022, 19, 100528. [Google Scholar] [CrossRef]
- Rani, R.; Kashyap, V.; Khurana, M. Role of IoT-Cloud Ecosystem in Smart Cities: Review and Challenges. Mater. Today 2022, 49, 2994–2998. [Google Scholar] [CrossRef]
- Khan, L.U.; Yaqoob, I.; Tran, N.H.; Kazmi, S.M.A.; Dang, T.N.; Hong, C.S. Edge-Computing-Enabled Smart Cities: A Comprehensive Survey. IEEE Internet Things J. 2020, 7, 10200–10232. [Google Scholar] [CrossRef]
- Puliafito, A.; Tricomi, G.; Zafeiropoulos, A.; Papavassiliou, S. Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies. Sensors 2021, 21, 3349. [Google Scholar] [CrossRef] [PubMed]
- Ang, K.L.-M.; Seng, J.K.P. Embedded Intelligence: Platform Technologies, Device Analytics, and Smart City Applications. IEEE Internet Things J. 2021, 8, 13165–13182. [Google Scholar] [CrossRef]
- Wang, Z.; Hu, J.; Min, G.; Zhao, Z.; Wang, J. Data-Augmentation-Based Cellular Traffic Prediction in Edge-Computing-Enabled Smart City. IEEE Trans. Industr. Inform. 2021, 17, 4179–4187. [Google Scholar] [CrossRef]
- Mahmud, R.; Ramamohanarao, K.; Buyya, R. Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions. ACM Comput. Surv. 2021, 53, 1–43. [Google Scholar] [CrossRef]
- Laroui, M.; Nour, B.; Moungla, H.; Cherif, M.A.; Afifi, H.; Guizani, M. Edge and Fog Computing for IoT: A Survey on Current Research Activities & Future Directions. Comput. Commun. 2021, 180, 210–231. [Google Scholar] [CrossRef]
- Sabireen, H.; Neelanarayanan, V. A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges. ICT Express 2021, 7, 162–176. [Google Scholar] [CrossRef]
- Singh, J.; Singh, P.; Gill, S.S. Fog Computing: A Taxonomy, Systematic Review, Current Trends and Research Challenges. J. Parallel Distrib. Comput. 2021, 157, 56–85. [Google Scholar] [CrossRef]
- Ren, J.; Zhang, D.; He, S.; Zhang, Y.; Li, T. A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet. ACM Comput. Surv. 2020, 52, 1–36. [Google Scholar] [CrossRef]
- Shakarami, A.; Shakarami, H.; Ghobaei-Arani, M.; Nikougoftar, E.; Faraji-Mehmandar, M. Resource Provisioning in Edge/Fog Computing: A Comprehensive and Systematic Review. J. Syst. Arch. 2022, 122, 102362. [Google Scholar] [CrossRef]
- Zahmatkesh, H.; Al-Turjman, F. Fog Computing for Sustainable Smart Cities in the IoT Era: Caching Techniques and Enabling Technologies—An Overview. Sustain. Cities Soc. 2020, 59, 102139. [Google Scholar] [CrossRef]
- Habibi, P.; Farhoudi, M.; Kazemian, S.; Khorsandi, S.; Leon-Garcia, A. Fog Computing: A Comprehensive Architectural Survey. IEEE Access 2020, 8, 69105–69133. [Google Scholar] [CrossRef]
- Pau, G.; Arena, F. Smart City: The Different Uses of IoT Sensors. J. Sens. Actuator Netw. 2022, 11, 58. [Google Scholar] [CrossRef]
- Javadzadeh, G.; Rahmani, A.M. Fog Computing Applications in Smart Cities: A Systematic Survey. Wirel. Netw. 2020, 26, 1433–1457. [Google Scholar] [CrossRef]
- EdgeX Foundry. Available online: https://www.edgexfoundry.org/ (accessed on 2 August 2023).
- Zhang, C. Design and Application of Fog Computing and Internet of Things Service Platform for Smart City. Future Gener. Comput. Syst. 2020, 112, 630–640. [Google Scholar] [CrossRef]
- Sobin, C.C. A Survey on Architecture, Protocols and Challenges in IoT. Wirel. Pers. Commun. 2020, 112, 1383–1429. [Google Scholar] [CrossRef]
- Falayi, A.; Wang, Q.; Liao, W.; Yu, W. Survey of Distributed and Decentralized IoT Securities: Approaches Using Deep Learning and Blockchain Technology. Future Internet 2023, 15, 178. [Google Scholar] [CrossRef]
- 3GPP TR 38.913. Study on Scenarios and Requirements for Next Generation Access Technologies, 2017. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2996 (accessed on 5 December 2023).
- Makarevich, A. IoT Connectivity Options: Comparing Short-, Long-Range Technologies. IoT World Today. Available online: https://www.iotworldtoday.com/metaverse/iot-connectivity-options-comparing-short-long-range-technologies (accessed on 8 September 2023).
- Bayılmış, C.; Ebleme, M.A.; Çavuşoğlu, Ü.; Küçük, K.; Sevin, A. A Survey on Communication Protocols and Performance Evaluations for Internet of Things. Digit. Commun. Netw. 2022, 8, 1094–1104. [Google Scholar] [CrossRef]
- Muthanna, A.; Ateya, A.A.; Balushi, M.A.; Kirichek, R. D2D Enabled Communication System Structure Based on Software Defined Networking for 5G Network. In Proceedings of the 2018 International Symposium on Consumer Technologies (ISCT), St. Petersburg, Russia, 11–12 May 2018. [Google Scholar]
- Pliatsios, A.; Kotis, K.; Goumopoulos, C. A Systematic Review on Semantic Interoperability in the IoE-Enabled Smart Cities. Internet Things 2023, 22, 100754. [Google Scholar] [CrossRef]
- Kumar, V.; Gunner, S.; Spyridopoulos, T.; Vafeas, A.; Pope, J.; Yadav, P.; Oikonomou, G.; Tryfonas, T. Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities: A Systematic Review. arXiv 2023, arXiv:2302.11009. [Google Scholar]
- Whaiduzzaman, M.; Barros, A.; Chanda, M.; Barman, S.; Sultana, T.; Rahman, M.S.; Roy, S.; Fidge, C. A Review of Emerging Technologies for IoT-Based Smart Cities. Sensors 2022, 22, 9271. [Google Scholar] [CrossRef] [PubMed]
- Peralta Abadía, J.J.; Walther, C.; Osman, A.; Smarsly, K. A Systematic Survey of Internet of Things Frameworks for Smart City Applications. Sustain. Cities Soc. 2022, 83, 103949. [Google Scholar] [CrossRef]
- Allam, Z.; Dhunny, Z.A. On Big Data, Artificial Intelligence and Smart Cities. Cities 2019, 89, 80–91. [Google Scholar] [CrossRef]
- Kamruzzaman, M.M. New opportunities, challenges, and applications of edge-AI for connected healthcare in smart cities. In Proceedings of the 2021 IEEE Globecom Workshops (GC Wkshps), Madrid, Spain, 7–11 December 2021; pp. 1–6. [Google Scholar]
- Khang, A.; Rani, S.; Sivaraman, A.K. AI-Centric Smart City Ecosystems: Technologies, Design and Implementation; Khang, A., Rani, S., Sivaraman, A.K., Eds.; CRC Press: London, UK, 2022; ISBN 9781000798524. [Google Scholar]
- Kaginalkar, A.; Kumar, S.; Gargava, P.; Niyogi, D. Review of Urban Computing in Air Quality Management as Smart City Service: An Integrated IoT, AI, and Cloud Technology Perspective. Urban Clim. 2021, 39, 100972. [Google Scholar] [CrossRef]
- Luusua, A.; Ylipulli, J.; Foth, M.; Aurigi, A. Urban AI: Understanding the Emerging Role of Artificial Intelligence in Smart Cities. AI Soc. 2023, 38, 1039–1044. [Google Scholar] [CrossRef] [PubMed]
- Singh, S.; Sharma, P.K.; Yoon, B.; Shojafar, M.; Cho, G.H.; Ra, I.-H. Convergence of Blockchain and Artificial Intelligence in IoT Network for the Sustainable Smart City. Sustain. Cities Soc. 2020, 63, 102364. [Google Scholar] [CrossRef]
- Serban, A.C.; Lytras, M.D. Artificial Intelligence for Smart Renewable Energy Sector in Europe—Smart Energy Infrastructures for next Generation Smart Cities. IEEE Access 2020, 8, 77364–77377. [Google Scholar] [CrossRef]
- Ma, M.; Preum, S.M.; Ahmed, M.Y.; Tärneberg, W.; Hendawi, A.; Stankovic, J.A. Data Sets, Modeling, and Decision Making in Smart Cities: A Survey. ACM Trans. Cyber-Phys. Syst. 2020, 4, 1–28. [Google Scholar] [CrossRef]
- Cammers-Goodwin, S. Open Data Insights from a Smart Bridge Datathon: A Multi-Stakeholder Observation of Smart City Open Data in Practice. Smart Cities 2023, 6, 676–691. [Google Scholar] [CrossRef]
- Smart Cities Index Datasets. Available online: https://www.kaggle.com/datasets/magdamonteiro/smart-cities-index-datasets (accessed on 4 August 2023).
- Trees, with Species and Dimensions (Urban Forest). Available online: https://data.melbourne.vic.gov.au/explore/dataset/trees-with-species-and-dimensions-urban-forest/information/ (accessed on 4 August 2023).
- Chicago Average Daily Traffic Counts. Available online: https://www.kaggle.com/datasets/chicago/chicago-average-daily-traffic-counts (accessed on 4 August 2023).
- The Land & Transport Singapore (LTSG) Dataset. Available online: https://github.com/BlueSkyLT/siteselect_sg (accessed on 4 August 2023).
- Los Angeles GeoHub Datasets. Available online: https://geohub.lacity.org/ (accessed on 4 August 2023).
- Crime in Los Angeles. Available online: https://www.kaggle.com/datasets/cityofLA/crime-in-los-angeles (accessed on 4 August 2023).
- City of Chicago Crime Dataset. Available online: https://data.cityofchicago.org/Public-Safety/Crimes-2022/9hwr-2zxp/data (accessed on 4 August 2023).
- Opentraffic. Available online: https://github.com/opentraffic (accessed on 4 August 2023).
- Citi Bike Trip Data. Available online: https://citibikenyc.com/system-data (accessed on 4 August 2023).
- London Bike Sharing Dataset. Available online: https://www.kaggle.com/datasets/hmavrodiev/london-bike-sharing-dataset (accessed on 4 August 2023).
- Beijing Multi-Site Air-Quality Data Set. Available online: https://www.kaggle.com/datasets/sid321axn/beijing-multisite-airquality-data-set (accessed on 4 August 2023).
- Smart Data Hub. Available online: https://www.smartdatahub.io/ (accessed on 4 August 2023).
- Xu, J.; Wang, C.; Jia, X. A Survey of Blockchain Consensus Protocols. ACM Comput. Surv. 2023, 55, 1–35. [Google Scholar] [CrossRef]
- Ullah, Z.; Naeem, M.; Coronato, A.; Ribino, P.; De Pietro, G. Blockchain Applications in Sustainable Smart Cities. Sustain. Cities Soc. 2023, 97, 104697. [Google Scholar] [CrossRef]
- Jin, S.; Chang, H. The Trends of Blockchain in Environmental Management Research: A Bibliometric Analysis. Environ. Sci. Pollut. Res. Int. 2022, 30, 81707–81724. [Google Scholar] [CrossRef] [PubMed]
- Khawaja, S.; Javidroozi, V. Blockchain Technology as an Enabler for Cross-sectoral Systems Integration for Developing Smart Sustainable Cities. IET Smart Cities 2023, 5, 151–172. [Google Scholar] [CrossRef]
- Chentouf, F.Z.; Bouchkaren, S. Security and Privacy in Smart City: A Secure e-Voting System Based on Blockchain. Int. J. Electr. Comput. Eng. 2023, 13, 1848. [Google Scholar] [CrossRef]
- Islam, A.; Shin, S.Y. BHMUS: Blockchain based secure outdoor health monitoring scheme using UAV in smart city. In Proceedings of the 2019 7th International Conference on Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia, 24–26 July 2019. [Google Scholar]
- Dong, Y.; Guo, S.; Liu, J.; Yang, Y. Energy-Efficient Fair Cooperation Fog Computing in Mobile Edge Networks for Smart City. IEEE Internet Things J. 2019, 6, 7543–7554. [Google Scholar] [CrossRef]
- Lv, Z.; Chen, D.; Lou, R.; Wang, Q. Intelligent Edge Computing Based on Machine Learning for Smart City. Future Gener. Comput. Syst. 2021, 115, 90–99. [Google Scholar] [CrossRef]
- El-Sayed, H.; Chaqfeh, M. Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities. Sensors 2019, 19, 1073. [Google Scholar] [CrossRef]
- Haj Qasem, M.; Abu-Srhan, A.; Natoureah, H.; Alzaghoul, E. Fog Computing Framework for Smart City Design. Int. J. Interact. Mob. Technol. 2020, 14, 109. [Google Scholar] [CrossRef]
- Santos, J.; Wauters, T.; Volckaert, B.; De Turck, F. Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks. Entropy 2017, 20, 4. [Google Scholar] [CrossRef]
- Mattos, D.M.F.; Velloso, P.B.; Duarte, O.C.M.B. An Agile and Effective Network Function Virtualization Infrastructure for the Internet of Things. J. Internet Serv. Appl. 2019, 10, 1–12. [Google Scholar] [CrossRef]
- Johnsen, F.T.; Zielinski, Z.; Wrona, K.; Suri, N.; Fuchs, C.; Pradhan, M.; Furtak, J.; Vasilache, B.; Pellegrini, V.; Dyk, M.; et al. Application of IoT in military operations in a smart city. In Proceedings of the 2018 International Conference on Military Communications and Information Systems (ICMCIS), Warsaw, Poland, 22–23 May 2018. [Google Scholar]
- Del Esposte, A.D.M. Design and Evaluation of a Scalable Smart City Software Platform with Large-Scale Simulations. Future Gener. Comput. Syst. 2019, 93, 427–441. [Google Scholar] [CrossRef]
- Sreekanth, G.R.; Ahmed, S.A.N.; Sarac, M.; Strumberger, I.; Bacanin, N.; Zivkovic, M. Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes. Comput. Syst. Sci. Eng. 2021, 41, 751–765. [Google Scholar] [CrossRef]
- Farooqi, A.M.; Alam, M.A.; Hassan, S.I.; Idrees, S.M. A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation. Appl. Sci. 2022, 12, 2083. [Google Scholar] [CrossRef]
- Bekkali, A.E.; Essaaidi, M.; Boulmalf, M. A Blockchain-Based Architecture and Framework for Cybersecure Smart Cities. IEEE Access 2023, 11, 76359–76370. [Google Scholar] [CrossRef]
- Javed, A.R.; Ahmed, W.; Pandya, S.; Maddikunta, P.K.R.; Alazab, M.; Gadekallu, T.R. A Survey of Explainable Artificial Intelligence for Smart Cities. Electronics 2023, 12, 1020. [Google Scholar] [CrossRef]
- Alahi, M.E.E.; Sukkuea, A.; Tina, F.W.; Nag, A.; Kurdthongmee, W.; Suwannarat, K.; Mukhopadhyay, S.C. Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends. Sensors 2023, 23, 5206. [Google Scholar] [CrossRef] [PubMed]
Category | Area | Keywords/Strings |
---|---|---|
A | Smart city | A1—Smart city A2—Smart city applications A3—Smart city challenges A4—5G use cases A5—Sustainable development |
B | Key technologies | B1—Key technologies of future smart cities B2—SDN for smart city networks B3—Benefits of deploying SDN for smart city networks B4—Challenges with SDN-based smart cities B5—NFV for smart cities B6—Challenges with NFV B7—Mobile edge computing for smart city applications B8—Challenges with MEC B9—Internet of things-based smart cities B10—Internet of things connectivity B11—Challenges with IoT-based smart cities B12—Fog computing for smart cities B13—Challenges with fog-based smart cities B14—Blockchain for smart city applications B15—AI-based smart cities B16—Benefits of deploying AI for different smart cities B17—Challenges of implementing AI for smart cities B18—Smart city datasets |
C | Applied research | C1—Smart healthcare C2—Smart parking C3—Smart city networking C4—MEC-enabled smart city C5—Fog-enabled smart city C6—Smart grid C7—Smart homes C8—Smart city networking |
Smart City Application | Latency | Availability | Reliability | Mobility | Deployment Scenarios |
---|---|---|---|---|---|
Smart grid | Moderate–low | Ultra-high | High | Zero–low | Dense–ultra-dense deployment |
Smart homes | Low–ultra-low | High–ultra-high | High–ultra-high | Zero–low | Indoor deployment, dense deployment |
Smart building | Moderate–low | Ultra-high | High–ultra-high | Zero | Urban deployment |
Smart parking | Moderate–low | High–ultra-high | Medium–high | Low–moderate | Dense deployment |
Smart traffic | Low–ultra-low | High–ultra-high | High | Low–high | Dense deployment |
Smart healthcare | Ultra-low | Ultra-high | Ultra-high | Low–high | Dense deployment |
Industry automation | Ultra-low | Ultra-high | Ultra-high | Zero–low | Dense deployment |
Value Added | SDN for Smart Cities |
---|---|
Resources optimization | With an SDN, network resources can be dynamically allocated based on demand and usage patterns. This resource optimization ensures efficient bandwidth use and minimizes latency, allowing smart cities to operate smoothly and have real-time responses. |
Enhancing network security | An SDN enables robust security policies and network segmentation. Segmented networks can isolate sensitive data and critical infrastructure from potential threats, preventing unauthorized access and mitigating security risks. |
Flexibility and scalability | An SDN allows for centralized network management, making scaling and adapting smart city networks easier as the city’s requirements evolve. Adding or removing network elements can be done quickly and efficiently, ensuring the network remains flexible to accommodate future growth and changing needs. |
Ensuring QoS | In smart cities, various applications have different network demands, such as traffic management, surveillance, and healthcare. An SDN allows for implementing different QoS. |
Ease of integration between distributed and centralized clouds | An SDN facilitates seamless integration between cloud and edge computing infrastructures. This integration enables the deployment of edge services closer to the end users, reducing the latency and enhancing the overall performance of smart city applications. |
Centralized management | An SDN provides a centralized control plane, enabling operators to manage the entire smart city network. Centralized control enhances network visibility, simplifies management, and allows for dynamic and real-time adjustments to optimize the network performance. |
Cost efficient | An SDN’s simplified network management and resource optimization lead to cost savings regarding operational expenses (OPEXs) and capital expenditures (CAPEXs). Efficient network utilization reduces the need for additional hardware, optimizing infrastructure investments. |
Rapid service deployment | Smart city services often need to be deployed and updated quickly. An SDN enables rapid service deployment through automated configuration and provisioning, reducing the time and effort required to roll out new applications and features. |
Network innovation | An SDN allows developers and researchers to innovate and create custom applications and services for smart city systems. The programmability of an SDN fosters a culture of continuous improvement and drives innovation in urban services and applications. |
Real-time monitoring and management of network traffic | An SDN enables real-time analytics and monitoring of network traffic and performance. These insights help administrators to make data-driven decisions, identify areas for improvement, and enhance the overall smart city system efficiency. |
Value Added | NFV for Smart Cities |
---|---|
Ease of maintenance/migration | NFV facilitates the live migration of VMs. This is particularly valuable for maintaining continuous service availability and performing maintenance tasks. |
Ease of recovery | NFV simplifies disaster recovery processes. VMs and their configurations can be easily backed up, replicated, and restored in case of hardware failures or other disruptions, ensuring business continuity for critical smart city services. |
Efficient use of hardware | Multiple services and applications can share the same physical hardware without conflicts. This resource sharing increases the utilization of hardware components and reduces the need for dedicated resources for each service, leading to cost savings. |
Resources optimization | NFV efficiently allocates computing resources between various smart city applications. This leads to reduced hardware requirements, energy consumption, and cost. Moreover, it achieves higher computing efficiency in terms of overhead. |
Ease in integrating new services | NFV accelerates the deployment of new services and applications. VMs can be quickly provisioned, configured, and deployed, allowing smart cities to roll out innovative services faster and respond to emerging needs promptly. |
Ease of testing new services | NFV offers a controlled environment for testing and development. New services or updates can be tested in isolated VMs, reducing the risk of disrupting the production environment. |
Flexibility | NFV environments are highly adaptable. Smart cities can reconfigure and repurpose virtual resources as needed, enabling them to respond quickly to changing requirements and technological advancements. |
Resource scaling | NFV enables the dynamic scaling of resources based on demand. As the usage of smart city services fluctuates, virtualized environments can scale up or down to accommodate changing workloads, ensuring optimal performance and responsiveness. |
Enhanced security | NFV provides a strong separation between different applications and services running on the same physical infrastructure. This isolation enhances security by minimizing the potential for breaches and unauthorized access. |
Resource partitioning | NFV allows resources to be partitioned and allocated based on specific requirements. This ensures that critical services receive the necessary resources while preventing resource contention that could lead to performance degradation. |
Ease of integrating innovative technologies | As smart cities continue to evolve, NFV provides a foundation for incorporating emerging technologies. It allows for seamless integration of new applications and services, ensuring that the city’s infrastructure remains adaptable and forward looking. |
Value Added | MEC for Smart City | Applications |
---|---|---|
Real-time interaction | This is one motivation for using edge computing over cloud computing for smart cities. MEC improves the QoS and implements low latency for delay-sensitive services, including unmanned aerial vehicles (UAVs), tactile Internet, remote surgery, and vehicular accident prevention. Also, edge computing provides decision making and data analysis in real time. |
|
Local processing | MEC reduces traffic between cells and the core network and increases spectrum efficiency. |
|
Delay reduction | MEC pushes resources closer to the network’s edge, lowering data transmission times. This low latency is critical for applications such as real-time traffic control, self-driving cars, and emergency response systems. |
|
Improved spectral efficiency | By performing data processing tasks in proximity to the source of the data, MEC mitigates the need to transmit substantial amounts of data to far data centers. Consequently, this approach optimizes the use of network capacity and alleviates congestion on the network. This is crucial for real-time and multimedia-based smart city applications. |
|
Improving the quality of service and experience (QoS and QoE) | MEC facilitates meeting various requirements of the QoS and QoE of heterogeneous smart city applications. Multimedia services need a high bandwidth and low latency, where the service providers do not control and distribute the contents. MEC facilitates implementing new applications and services that allow the service provider to specify certain QoS criteria. Service providers should be aware of customers and contextual information requirements, such as their interests and preferences. Then, the information can be allocated to attract customers and improve their QoE. |
|
Predicting network demands | MEC provides the network with the required resources to supply the network or the user’s demands. Accurately predicting a demanding network helps to improve network performance if the demand is executed locally at the edge. It also allocates resources effectively in an optimal way. |
|
Improved security and privacy | Edge data processing can preserve user privacy by limiting the transmission of sensitive data to centralized cloud servers through the network core. This is especially important for applications involving personal or sensitive information. |
|
Virtualization and service orchestration | MEC enables the implementation of various virtualization schemes at the edge of the network, which assist in introducing novel network services at the edge. MEC enables service orchestration, allowing for the efficient and dynamic allocation of computing resources based on the requirements of different applications and services in the smart city network. |
|
Energy efficiency | MEC reduces energy costs by providing energy resources near devices. This empowers the energy performance of smart city devices. |
|
Efficient data management | Data analysis and management techniques can be easily implemented over the MEC platform, reducing the load on the core network and providing data analysis results at the edge. |
|
High data rate | Transferring the massive data of various smart city applications to edge clouds is important. Introducing remote servers at the edge gives a pass of data offloading, which achieves higher data rates. |
|
High availability | Incorporating MEC into the network architecture improves the overall availability of services, reduces the impact of network disruptions, and enhances the user experience. MEC guarantees the resources’ availability, which encourages pushing data and applications to the edge. This increases network availability. |
|
Customized services | MEC allows smart cities to tailor services and applications to local needs and preferences, resulting in a more responsive and citizen-centric urban environment. |
|
No. | Data Group | Characteristics | MEC Benefit | Applications |
---|---|---|---|---|
I | Hard real-time data | This has a threshold latency; the edge servers handle services and applications with hard real-time requirements and provide efficient low latency because of their vicinity to UEs. | Achieving the ultra-low latency required |
|
II | Soft real-time data | This tolerates some predefined and bounded latency; the edge servers implement tasks for applications with soft real-time requirements. Data will be moved to the cloud when the response time exceeds this. | Achieving the low latency required |
|
III | Non-real-time data | This can tolerate latency and is non-sensitive to time. Their tasks are moved to the cloud for load balancing with non-real-time issues. | Achieving load balancing |
|
Value Added | Fog Computing for Smart City | Applications |
---|---|---|
Reduced delay |
|
|
Improved spectral efficiency |
|
|
Assisting scalability requirements |
|
|
Improved security and privacy |
|
|
Increased system availability and reliability |
|
|
Service orchestration |
|
|
Energy efficiency |
|
|
Efficient data management |
|
|
Assists network resilience |
|
|
Improved user experience |
|
|
Offline functionality |
|
|
No. | Step | Implementation Procedures |
---|---|---|
1 | Use cases identification |
|
2 | Design the appropriate topology |
|
3 | Data processing and analytics |
|
4 | Hardware selection |
|
5 | Software setup |
|
6 | Offloading scheme |
|
7 | Network security |
|
8 | Integration |
|
9 | Monitoring |
|
10 | Testing |
|
RFID | NFC | Zigbee | Bluetooth | Characteristics |
---|---|---|---|---|
ISO/IEC 13157 | 802.15.4 | 802.15.1 | IEEE 1451 | IEEE specification |
Varies | 424 kbps | 250 kbps | 1 Mbps | Data rate |
Varies | 13.56 MHz | 2.4 and 2.48 GHz | 2.4 GHz | Frequency band |
1 m | 4 cm | 10–100 m | 10 m | Communication range |
Low | Low | Low | Low | Cost |
No battery (passive tags) | Intermediate | High | Ultra-high | Power usage (battery life) |
Tracking items, E-passport | Smart tags for medical applications | Smart metering | Smart home application | IoT applications |
LTE CAT-1 | LTE CAT-4 | NB-IoT | IEEE 802.11ah | IEEE 802.11ax | LoRaWAN | Sigfox | |
---|---|---|---|---|---|---|---|
Frequency band | Licensed band | Unlicensed band | |||||
Data rate | DL: 10 Mbps UL: 5 Mbps | DL: 150 Mbps UL: 50 Mbps | <150 kbps | 433 to 6933 Mbps | 600 to 9608 Mbps | <10 kbps | 100 b/s |
Range | Limited to cellular cell | Limited to cellular cell | Limited to cellular cell | Up to 1 km | 2.4/5/6 GHz | Up to 15 km | Up to 50 km |
Power consumption | High to ultra-high | Low | Low | Low to ultra-low | |||
Cost | High | Low | |||||
Security | High to ultra-high | Low to ultra-low | |||||
Application | Automotive transportation | Monitoring asset tracking | Smart grid communication | Smart sensors and meters | Smart metering | Smart building (smart lighting) | Smart building (electrical plugs) |
Benefit | IoT-Based Smart Cities |
---|---|
Improved network efficiency | The IoT enables the integration and automation of various city systems, which enables real-time monitoring, data analysis, and optimization, leading to more efficient resource utilization and cost savings. Moreover, IoT devices often use low-power communication protocols, which reduces the strain on network resources. |
Increased scalability | The IoT paradigm supports a high level of scalability. This scalability allows smart cities to keep up with the increasing number of sensors, devices, and applications without compromising network performance. |
Improved citizen engagement | The IoT empowers citizens to actively participate in city processes through real-time data access and feedback mechanisms. This fosters community and encourages collaboration between residents and city authorities. |
Data-based decision making | The IoT generates a vast amount of data, which provides valuable insights for urban planning and policymaking. Data-based decision making helps city authorities to understand the needs and preferences of citizens, identify trends, and optimize resource allocation for effective governance. |
Remote management | The IoT networks enable the remote management and configuration of devices. This feature reduces the need for physical intervention and allows administrators to optimize network settings without disrupting operations. |
Support economic growth | Smart cities attract businesses, startups, and innovation hubs. IoT technologies create opportunities for new services, products, and industries, contributing to economic growth and job creation. |
Dynamic allocation of resources | The IoT networks can dynamically allocate network resources based on the demands of different applications. This resource management ensures that critical applications receive the required bandwidth, enhancing the overall network performance. |
Support of heterogeneous interfaces | IoT networks utilize various communication technologies, including cellular, Wi-Fi, and low-power wide-area networks (LPWANs). This diverse connectivity ecosystem ensures that devices can connect seamlessly, even in areas with varying coverage. |
Application Category | How Can It Benefit from IoT? |
---|---|
Infrastructure management | IoT sensors can monitor the condition of critical infrastructure in real time. These data enable proactive maintenance, early detection of faults, and efficient allocation of repair resources, enhancing public safety and reducing downtime. |
Sustainability | The IoT can help cities become more sustainable by monitoring and controlling energy consumption, reducing waste production, and optimizing resource allocation. Smart grids, for example, can balance energy demand and supply, integrate renewable energy sources, and reduce carbon emissions. |
Public safety | The IoT enables the implementation of smart surveillance systems, including video analytics, facial recognition, and sensor-based monitoring. These systems can enhance public safety by detecting and responding to incidents, managing crowd control, and providing timely emergency alerts. |
Healthcare | IoT devices can remotely monitor patients’ health conditions, leading to the early detection of health issues and timely interventions. |
Quality of life | The IoT can improve the life quality of citizens by introducing smart services. For example, smart lighting can adjust the brightness according to ambient light conditions, reducing energy consumption. |
Traffic management | IoT devices are introduced to traffic applications, including intelligent traffic and connected vehicles. Real-time data on public transportation can provide accurate navigation information to commuters, reducing travel time and fuel consumption. The main benefit of the IoT here is the ease of deployment. |
Environmental monitoring | IoT data can monitor environmental conditions, leading to better pollution control, efficient water usage, and preservation of natural resources. |
Application | AI Deployment |
---|---|
Smart grid | As the population grows, so does the need for electricity and other forms of energy. New infrastructures and various construction infrastructures require electricity, which we must supply. The need is for the intelligent management of energy resources. Energy resources must be used responsibly and effectively. AI can manage energy consumption more efficiently in buildings and public spaces. |
Infrastructure maintenance | AI can predict maintenance needs; prioritize repairs; and prevent failures of infrastructure, including bridges, roads, and buildings. |
E-health | The city’s population grows as urbanization progresses, which raises the demand for amenities and healthcare facilities. Modern mobile devices and intelligent technology are combined with healthcare to create smart healthcare. Smart wearables like fitness bands, trackers, tools for assessing one’s health, and apps are in use. These gadgets monitor the wearer’s well-being and can offer solutions. Doctors, researchers, and other healthcare professionals can analyze the data generated by these devices to provide better, more individualized diagnoses and treatments. AI can monitor public health in cities by analyzing data from public health records. This data can be used to detect outbreaks of diseases and predict disease spread, allowing officials to take quick action to prevent the spread of infections. |
Smart transportation | Smart transportation uses the Internet, sensors, and actuators to make travel faster, safer, and more convenient. AI can analyze these data and automate different processes inside intelligent transportation systems. AI-powered traffic management systems have optimized traffic, reduced congestion, and improved safety. AI can predict traffic patterns and adjust traffic signals. |
Smart parking | Finding parking spaces and making the most of parking lots are essential due to the significant vehicle growth in the city. Cities can share parking information about available and occupied spots through a public portal or app. Users can access this information and quickly move to the designated spot. |
Water management | AI can manage water resources more efficiently by analyzing data from sensors placed in reservoirs, pipes, and other infrastructure. It can predict water demand and optimize distribution. |
Smart lighting | Energy conservation and increasing the efficiency and adaptability of lighting equipment were the main goals of implementing smart lighting in the smart city paradigm. An intelligent, wireless, decentralized local network includes a smart lighting system. It has seamless access to the Internet, a data center, and several other cloud-based management platforms. |
Public safety | AI can assist public safety by identifying potential risks and responding quickly to emergencies. |
Tourism | AI can enhance the city tourism experience by providing personalized recommendations to visitors based on their preferences and behavior. This can help visitors to discover new attractions and experiences while reducing overcrowding at popular tourist spots. |
Systems for monitoring pollution | Pollution is at its highest point when urbanization accelerates, and people are moving to cities at previously unheard-of rates. To reduce pollution, the government must develop concepts and technologies. The state of the environment must be kept under observation. Installed and deployed smart devices are required to monitor the city’s soil, water, and air quality. People and the government can take corrective actions that will help to improve environmental health based on the readings from these sensors. |
Dataset | Description |
---|---|
Smart cities index datasets [101] |
|
Melbourne urban forest dataset [102] |
|
Chicago traffic tracker dataset [103] |
|
Land and Transport Singapore (LTSG) datasets [104] |
|
Los Angeles GeoHub datasets [105] |
|
Los Angeles crime dataset [106] |
|
City of Chicago crime dataset [107] |
|
Open traffic [108] |
|
Citi bike trip dataset [109] |
|
London bike sharing dataset [110] |
|
Beijing air quality dataset [111] |
|
Smart data hub [112] |
|
Benefit | Blockchain-Based Smart City |
---|---|
Transparency and trust | A blockchain provides an immutable and transparent ledger, ensuring that data cannot be tampered with or altered. This enhances trust and accountability within the city infrastructure, allowing citizens to trace how resources are allocated, ensuring transparency in decision-making processes, and minimizing corruption. |
Efficiency and automation | Smart contracts on a blockchain can automate various processes, e.g., energy distribution, traffic management, and waste management, improving efficiency and reducing administrative costs. Smart contracts can automate and enforce agreements between different entities within a smart city ecosystem. It eliminates the need for intermediaries, reduces administrative costs, and ensures transparent and efficient transactions. |
Supply chain management | A blockchain can enhance supply chain traceability in smart cities. By recording every transaction and movement of goods on a distributed ledger, stakeholders can have real-time visibility of the entire supply chain. This improves efficiency, prevents fraud, and promotes sustainable practices by monitoring the environmental impact of goods and services. |
Enhanced security | A blockchain provides a decentralized and tamper-proof system, ensuring the integrity and security of data. This prevents cyber-attacks and unauthorized access to sensitive information, ensuring a safer environment for smart city residents. |
Citizen engagement and governance | A blockchain can give citizens more control over their data, giving them transparency, privacy, and the ability to participate in decision-making. A blockchain can enable secure and transparent voting systems, ensuring integrity in the democratic processes within a smart city. It can also facilitate decentralized identity systems, allowing citizens to control their data while ensuring privacy and security. |
Data sharing and interoperability | A blockchain facilitates secure and decentralized data sharing between various stakeholders within a smart city ecosystem. It can promote interoperability and collaboration between different service providers and government agencies. |
Economic growth and innovation | Adopting blockchain technology can attract investments, foster entrepreneurship, and promote innovation within a smart city ecosystem. It can create new economic opportunities, attract tech startups, and enable the development of decentralized applications that cater to specific urban needs. |
Challenge | Discussion |
---|---|
Scalability | Blockchain networks often face scalability challenges when dealing with many transactions and data, which may pose difficulties when trying to handle the scale and complexity of a smart city. |
Interoperability | Integrating different existing systems with a blockchain might be challenging due to compatibility issues and the need to establish common standards and protocols for seamless communication. |
Regulatory | Smart cities must navigate complex legal and regulatory frameworks to ensure compliance with existing laws. Blockchain-specific regulations and standards are still evolving in many jurisdictions. |
Privacy concerns | While a blockchain provides transparency, it may pose challenges in terms of privacy, as personal data on a blockchain might be accessible to all participants. Striking the right balance is crucial to protect personal information. |
Energy consumption | Blockchain networks can be energy-intensive due to consensus mechanisms like proof-of-work. This can be a concern regarding energy consumption and environmental sustainability. |
User adoption | Encouraging widespread adoption of blockchain technology may be challenging due to citizens’ lack of awareness and understanding. Promoting digital literacy, conducting public awareness campaigns, and demonstrating the tangible benefits of a blockchain in areas like transparent governance and efficient services can aid in overcoming this challenge. |
Ref. | Key Enabling Technology | Distributed Edge Computing | KPI | Smart City Application | ||||||
---|---|---|---|---|---|---|---|---|---|---|
IoT | Blockchain | SDN | NFV | UAV | AI | Fog | MEC | |||
[8] | √ | × | × | × | × | × | × | × |
| Ambient monitoring |
[7] | √ | × | √ | × | × | √ | √ | × |
| Industrial application |
[118] | √ | √ | × | × | √ | × | × | √ |
| Health monitoring |
[119] | × | × | × | × | × | × | √ | × |
| General |
[120] | √ | × | × | × | √ | × | √ |
| General | |
[121] | √ | × | √ | × | × | × | × | √ |
| Intelligent transportation systems (ITSs) |
[122] | √ | × | × | × | × | × | √ | × |
| Parking and monitoring system |
[123] | √ | × | × | √ | × | × | √ | × |
| Air monitoring |
[124] | √ | × | × | √ | × | × | √ | × |
| General |
[55] | √ | √ | √ | √ | × | × | × | √ |
| General |
[125] | √ | × | × | × | × | × | × | × |
| Military operations |
[126] | √ | × | × | √ | × | × | × | × |
| Smart parking |
[127] | √ | √ | √ | √ | × | × | √ | √ |
| General |
[128] | √ | × | √ | × | × | × | √ | √ |
| Smart transportation |
[129] | √ | √ | × | √ | × | √ | × | × |
| Smart grid |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ali, S.A.; Elsaid, S.A.; Ateya, A.A.; ElAffendi, M.; El-Latif, A.A.A. Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions. Future Internet 2023, 15, 398. https://doi.org/10.3390/fi15120398
Ali SA, Elsaid SA, Ateya AA, ElAffendi M, El-Latif AAA. Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions. Future Internet. 2023; 15(12):398. https://doi.org/10.3390/fi15120398
Chicago/Turabian StyleAli, Shrouk A., Shaimaa Ahmed Elsaid, Abdelhamied A. Ateya, Mohammed ElAffendi, and Ahmed A. Abd El-Latif. 2023. "Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions" Future Internet 15, no. 12: 398. https://doi.org/10.3390/fi15120398