Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Corneliu Zaharia
 -- 4007 2023-09-14 14:33:55 |
2 layout
Jessie Wu
 + 4 word(s) 4011 2023-09-15 05:20:26 | |
3 layout
Jessie Wu
 Meta information modification 4011 2023-09-18 10:34:38 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Murroni, M.; Anedda, M.; Fadda, M.; Ruiu, P.; Popescu, V.; Zaharia, C.; Giusto, D. 6G-Enabled Smart Cities. Encyclopedia. Available online: https://encyclopedia.pub/entry/49180 (accessed on 19 May 2024).
Murroni M, Anedda M, Fadda M, Ruiu P, Popescu V, Zaharia C, et al. 6G-Enabled Smart Cities. Encyclopedia. Available at: https://encyclopedia.pub/entry/49180. Accessed May 19, 2024.
Murroni, Maurizio, Matteo Anedda, Mauro Fadda, Pietro Ruiu, Vlad Popescu, Corneliu Zaharia, Daniele Giusto. "6G-Enabled Smart Cities" Encyclopedia, https://encyclopedia.pub/entry/49180 (accessed May 19, 2024).
Murroni, M., Anedda, M., Fadda, M., Ruiu, P., Popescu, V., Zaharia, C., & Giusto, D. (2023, September 14). 6G-Enabled Smart Cities. In Encyclopedia. https://encyclopedia.pub/entry/49180
Murroni, Maurizio, et al. "6G-Enabled Smart Cities." Encyclopedia. Web. 14 September, 2023.
6G-Enabled Smart Cities
Edit
This entry is adapted from the peer-reviewed paper 10.3390/s23177528

The sixth-generation (6G) wireless communication is the successor of fifth-generation (5G) communication. From a technological point of view, it makes use of higher frequency radio bands and provides a higher capacity combined with lower latency, enabling the integration into a single network with increased throughput and reliability. These characteristics make 6G networks ideal for the large-scale adoption of the Internet of Things (IoT) paradigm, especially considering that, in a short time, the devices connected to the IoT infrastructure are expected to reach billions of devices.

smart city smart mobility 6G

1. Introduction

One of the key concepts of sixth-generation (6G)-enabled smart cities (SCs) regards the integration of advanced communication technology, specifically 6G networks, into the development and management of SCs. An SC utilizes various digital technologies and data-driven solutions to enhance the quality of life for its residents, improve infrastructure efficiency, and promote sustainable development. Moreover, 6G networks can facilitate the development of intelligent infrastructure, such as connected sensors and devices, enabling better monitoring and management of urban resources. This includes smart waste management, optimization of water and energy usage, and intelligent urban planning based on data analytics. Furthermore, 6G-enabled SCs are expected to promote inclusivity and improve the quality of life of citizens by enabling better access to services, such as e-healthcare, online education, and digital governance platforms.

2. Terahertz Communications

In ref. [1], the authors assert that 6G needs AI-enabled optimization. Traditional approaches are characterized by prior knowledge and statistical analysis but are ineffective due to the elapsed time from the analysis to the decision-making [2]. AI-enabled 6G network protocols and mechanisms, with their employed self-learning ML and deep learning (DL), algorithms are proposed in order to solve several issues in networking.
The concept of futuristic SCs and the role of 6G network technology in their development are discussed in ref. [3], highlighting the need for dense and AI-centric cities, as well as the requirement for massive device connectivity and data traffic. A 6G network is seen as the solution for these futuristic cities, offering high bandwidth and low latency using terahertz waves. However, the short-range and atmospheric attenuation of terahertz waves present challenges to the 6G network. The study proposed a conceptual terrestrial network architecture called the nested Bee Hive, designed to address the needs of futuristic SCs. Simulations using different pathfinding algorithms have evaluated the performance of this architecture and established the dynamics of communication in a 6G environment.
Ref. [4] suggests overcoming the limitations of current THz antennas and IRS by only considering single-carrier modulation with a small array scale through the use of ultra-massive MIMO. The THz links are subject to high-loss connectivity. In ref. [5], the authors propose a cost-effective method to dynamically optimize the transmission path using reconfigurable intelligent surfaces (RISs), constructed by embedding active elements into passive metasurfaces, proposing metasurfaces with tunable functions, such as pixel-level amplitude modulation and wide-range phase coverage for beam steering. The utilization of ITSs and autonomous driving in the 6G era heavily relies on the exchange of massive amounts of information with ultra-wide bandwidth, high reliability, and low latency to ensure safety and provide a seamless experience. However, signals in high-frequency bands, such as millimeter wave (mmWave) and THz, can encounter obstacles that hinder their propagation. To tackle this issue, IRSs have garnered significant attention due to their ability to intelligently reflect signals and alter their propagation directions, thereby creating smart radio environments. IRSs offer advantages, such as easy deployment and cost-effectiveness, making them promising technologies for ground and aerial vehicular networks. They can amplify signal strength, bolster physical layer security, and enhance positioning accuracy [6].

3. Artificial Intelligence

Adaptive radio communications are technologies that have been around under various forms, such as the cognitive radio concept, for the past 20 years. The next step, the transformation to intelligent radio, can be supported in the 6G context by using advanced AI algorithms, in order to dynamically adapt radio communications to a specific radio environment.
As the data traffic grows in SCs, different modulation methods are being employed in communication systems for efficient and effective data transmission. Modulation recognition is crucial in signal demodulation and decoding, especially in applications such as interference identification, signal recognition, spectrum monitoring, and threat assessment. For instance, methods such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for modulation recognition over additive white Gaussian noise and Rayleigh fading channels were presented in ref. [7]. A CNN-based federated learning approach was proposed in ref. [8], enabling differential privacy for modulation recognition in order to assure the privacy and security of transmitted data.
Apart from the previously presented modulation classification techniques, traffic classification into different classes is another technique to ensure the QoS, enable pricing control, improve resource management, and improve the security of SC applications. In ref. [9], the authors proposed a Tree-RNN to classify network traffic into 12 different classes, thanks to a tree structure that divides the large classification problem into smaller ones, with each class represented by a tree node. Ref. [10] describes a hybrid RNN and CNN-based network to classify traffic from Internet of Things (IoT) devices and services. CNN layers extract complex network traffic features automatically from the input data, eliminating the feature selection process used in classical ML.
Channel estimation is the process of estimating the characteristics of the communication channel to recover the transmitted information from the channel effect. In ref. [1], a DL-based channel estimation process is presented, where the signal, along with pilot signals, is transmitted. The effects of the channel on the pilot signals are determined, and DL then estimates the channel attributes using the interpolated channel. In ref. [11], a deep neural network (DNN)-based approach for channel estimation and symbol detection in an OFDM system is proposed. With offline training using OFDM samples generated from different information sequences under distinct channel conditions, the obtained model is used to recover the transmitted information without estimating the channel characteristics.
The automotive sector represents another highly significant scenario that has the potential for extensive development with the advent of 6G technology. The expansions of IoT, edge computing, and mobile AI have enabled urban authorities to leverage the wealth of data gathered from connected and autonomous vehicles (CAVs). In ref. [12], the authors propose an intelligent hierarchical framework for road infrastructure maintenance that harnesses the advancements in 6G communication technologies, deep learning techniques, and mobile edge AI training methods. The developed framework meets the stringent requirements for training efficient ML applications designed for CAVs and effectively utilizes the expected proliferation of CAVs on future road networks.
Moreover, 6G—considered the next major transformative technology in the telecommunications industry—is attracting significant attention from academia and businesses. The global COVID-19 pandemic has accelerated the adoption of virtual meetings and live video interactions across various sectors, including healthcare, business, and education.
Federated learning (FL) holds promise as a training approach for achieving pervasive intelligence in future 6G communication systems. However, implementing FL in 6G-enabled edge systems is challenging due to the high energy consumption during decentralized training and the limitations of battery-powered, resource-constrained mobile devices. The accumulation of intensive computations and communication costs from local updates during numerous global rounds has created an energy bottleneck, which is exacerbated when dealing with non-identical and independently distributed data. To address these challenges, FedRelay [13] represents a versatile multi-flow relay learning framework that performs local updates relay-by-relay in the training flow through model propagation, introducing a decentralized relay selection protocol that leverages the diversity of cooperative communication networks.
A CNN-based channel estimation framework for massive MIMO systems is proposed in ref. [14]; it utilizes one-dimensional convolution to process the input data. Ref. [15] presents an assisted IoT-oriented MIMO wireless network system optimized for 6G. This system aims to improve the bit error rate (BER) and capacity to overcome key challenges related to wireless communication.

4. Blockchain-Based Solutions

Existing traditional UAV communication methods are insufficient at addressing the high mobility and dynamic characteristics of UAVs, particularly in hostile environments. Therefore, there is a pressing need for an efficient and secure UAV network. Motivated by these factors, ref. [16] presents a comprehensive survey on the architecture, requirements, and use cases of 6G technology in the context of UAV communication. A taxonomy of solutions is proposed based on the applications of UAV communication, and a security solution leveraging blockchain technology and 6G-enabled network connectivity is introduced. A case study illustrating a blockchain-enabled UAV communication system using 6G networks to enhance the security of Industry 4.0 applications is also outlined.
Leveraging blockchain technology for diverse applications in SCs presents significant challenges to the private sector, public sector, and government entities. Providing improved and optimal services to the citizens is complex. Blockchain technology offers a solution for implementing effective policies, fostering trust among policymakers, and reducing data storage costs while ensuring data security. Its decentralized nature provides enhanced security, enabling anyone to verify records. Trust plays a crucial role in financial transactions and other dealings, and blockchain facilitates the restoration and maintenance of trust through its transparent system, allowing people to verify and monitor policy processes. This concept holds particular importance in the realm of banking, which significantly impacts individuals’ lives. Furthermore, the advent of 6G will revolutionize remote communication through the utilization of AI [17]. In ref. [18], the authors investigate the integration of blockchain technology in 6G networks, enabling efficient monitoring and management of resource consumption and sharing. In addition, solutions associated with security and privacy implications in 6G networks are examined, providing valuable insights and directions for future studies on 6G security and privacy. Recognizing the potential paradigm shift and security benefits brought by blockchain, transitioning from a traditional centralized model to a more robust and resilient decentralized model, the authors of [19] propose a multi-tier integrated architecture that combines blockchain and edge computing for 5G and future generations, aimed at addressing security challenges faced by resource-constrained edge devices. Moreover, robust security and privacy measures are necessary for various IoT-enabled industrial applications. The arrival of blockchain technology has revolutionized information sharing by establishing trust on a secure and distributed platform, eliminating the need for third-party authorities [20]. Notably, blockchain technology (BCT) has garnered significant attention due to its ability to address decentralization, transparency, spectrum resource scarcity, privacy, security, interoperability, and confidentiality, as well as its potential in emerging domains, such as industrial IoT (IIoT) and Industry 5.0 applications. The disparity between the requirements for data-intensive disruptive IoT applications and the capabilities of 5G networks has generated a demand for decentralized BCT-based architecture in 6G networks. Ref. [21] presents an extensive survey that explores the integration of blockchain in 6G mobile networks, IoT technologies, and smart industries.

5. Quantum Communication

With the world experiencing rapid technological advancements, there has been a substantial increase in the demand for communication that is ultra-reliable, fast, low-power, and secure. Consequently, researchers have taken a keen interest in the emerging field of QC due to its potential to solve complex computations in a robust and efficient manner. It is anticipated that QC can serve as a critical enabler and a powerful catalyst in significantly reducing computing complexities while enhancing the security of sixth-generation (6G) and future communication systems. The study presented in ref. [22] delves into the fundamentals of QC, with the evolution of quantum communication spanning various technologies and applications; the authors specifically focus on quantum key distribution as a promising application for quantum security. Additionally, the authors investigate various parameters and important techniques to optimize the performance of 6G communication in terms of security, computing, and communication efficiency. Potential challenges that QC and quantum communication may encounter in the context of 6G are highlighted, along with future directions for exploration. In ref. [23], the authors focus on exploring the security risks associated with the new 5G paradigm and propose solutions to address them. Specifically, researchers delve into the role of quantum key distribution as a means to enhance security in the context of 6G. Moreover, the work presented in ref. [24] introduces a novel approach that combines quantum cryptography and CNN for secure communication in SCs. The proposed approach involves the utilization of quantum key distribution (QKD) for the secure key exchange between communicating parties. The key generated through QKD is then employed to encrypt the data using a CNN. This CNN-based encryption adds an extra layer of security to the transmitted data within SCs.
Among the enabling technologies and solutions discussed in ref. [25], research cannot fail to mention quantum optical switching and computing, THz-to-optical conversions, advanced meta-materials for smart radio-optical programmable environments, and AI. This research presents a future application scenario called quantum optical twin, which utilizes the aforementioned quantum optical communication technologies to provide services such as ultra-massive scale communications for connected spaces and ambient intelligence, holographic telepresence, tactile internet, new paradigms of brain–computer interactions, and innovative forms of communication.

6. UAVs and Drone-Based Solutions

Another area that is gaining momentum is that of drones and their varied applications for use. The advent of consumer drones, flying ad hoc networks, low-latency 5G, and advancements beyond 5G have significantly accelerated progress in this field. The authors of [26] describe a continuous actor–critic deep Q-learning (ACDQL) strategy to solve the location optimization problem of UAV-BSs in the presence of mobile endpoints, extending the action space of the reinforcement learning (RL) algorithm from discrete to continuous. In detail, a scheme for the dynamic positioning of a UAV-BS in the case of user mobility is proposed, overcoming previous works presented in the literature, which considered fixed locations for the ground endpoints. The proposed reward function aims to keep the UAV-BS inside the boundaries of the area of interest, and it also intends to maximize the users’ sum data rate.
In ref. [27], the authors propose implementing the Consumer Internet of Drone Things (CIoDT) framework, which facilitates the transfer of emergency messages between smart grid systems and power sources, and ensures reliable network connectivity during a disaster. To achieve this, a realistic mobility model was developed, and an edge-enabled opportunistic MQTT message transfer mechanism was implemented. Additionally, a dedicated network slice was devised to assess routing performance, achieving a message delivery probability of about 0.99 in the quality of service level 2 (QoS2) and an end-to-end latency of 1.19 s in the quality of service level 1 (QoS1). In ref. [28], the authors present a continuous Hopfield neural network (CHNN) algorithm for the optimal link-state protocol (OLSR) routing, which is able to reduce breakage and provide efficient routing services in multiple types of UAVs, which form flying ad hoc networks (FANETs). This is obtained using self-assembled networks to collaborate on complex task-application scenarios, such as SCs. The proposed protocol outperforms the original OLSR protocol in key performance metrics, such as packet delivery rate, control overhead, throughput, and end-to-end delay.
Distributed hash table (DHT)-based routing protocols were initially proposed as valuable solutions in high-mobility cases characterized by frequent changes in the network topology. A novel three-dimensional logical cluster-based DHT routing protocol for mobile ad hoc networks (MANETs) that use logical clustering, as well as an effective replication strategy to reduce the routing overhead and lookup latency introduced by the above problems are proposed in ref. [29]. Ref. [30] proposed a subscriber data management scheme based on the combination of distributed ledger technology (DLT) and DHT technologies for 6G mobile communication systems. Traditional mobile communication systems are structured to serve numerous subscribers simultaneously, following a network-centric design approach. In contrast, this research explores the user-centric design approach, where systems are user-defined, user-configurable, and user-controllable. This approach allows for personalized network services tailored to each user. In ref. [31], the authors introduce a new user-centric network (UCN) architecture that outlines core design principles, relevant network components, and procedures. The UCN architecture employs DLT and DHT for distributed implementation, fostering customization, autonomous data control, and privacy protection.
With their affordability and the ability to integrate transmitters, cameras, and sensors, UAVs have the potential to serve as flying IoT devices, seamlessly connecting with their surroundings and offering enhanced mobility within the network. In ref. [32], an overview of the advantageous applications of UAVs in SCs, particularly in the realm of ITS, while emphasizing the key challenges that may arise, is presented. Additionally, the integration of various AI techniques enhances data delivery through global communication, thereby improving operational performance. Despite being in the early stages, UAV-enabled IoE applications hold immense potential for enhancing 6G networks. This research investigates the recent advancements in methods and mechanisms that enable UAVs to support IoE. It explores the current trends in IoT convergence toward smarter IoE, while also addressing challenges and risks associated with the Internet of Everything [33].

7. Vehicular and Mobile Solutions

As presented in the previous section, MEC could solve challenges where transmitting data over long distances from end devices or edge servers to the cloud incurs great latency and security risks. In ref. [34], the authors analyze how MEC should improve applications and services, even in an SC environment. In contrast to cloud computing, edge computing provides a distributed computing platform for services, computation, and storing. MEC may decrease latency to lower levels and improve storage capability in devices with minimal processing capabilities, thanks to the growing use of smart systems. The incompatibility of various technology in cities is one of the most serious problems. In a scenario where MEC is used, a high number of connected devices might negatively impact the network speed, but they can be useful in identifying abnormal occurrences thanks to user-generated material and the right algorithms.
On the path to 6G, the advancing digitalization of buildings and SCs is becoming increasingly impactful [35]. To economically integrate renewable energy into buildings and SCs, the concept of energy storage and supply based on energy management must be emphasized. There has been a shift in political views; they now recognize the importance of energy efficiency in buildings given their considerable share (35%) of greenhouse gas emissions from final energy consumption. The authors highlight the potential of future mobile communication standards, such as 5G beyond and 6G, in providing specialized solutions for building digitization in edge clouds. The advent of 6G will revolutionize how research communicate and manage billions of interconnected devices in our digital future, spanning from macro- to micro- to nanoscales. Beyond providing lightning-fast connectivity, 6G has the potential to significantly enhance healthcare systems, transportation, logistics, safety measures, privacy, and more. It will enable the rapid processing, storage, and visualization of massive amounts of data, exploring the impact of 5G and 6G technologies on the advancement of SCs that are characterized by their intellect and perceptiveness [36].
The utilization of a microcell structure, employing carrier frequencies significantly higher than current 5G cellular networks, will present substantial challenges to the advancement of cellular communication in future generations. Specifically, the reliable operation of communication systems supporting critical services can be greatly impacted by factors like variations in vehicle velocity, rain-induced attenuation, and depolarization. The inclusion of eCall emergency systems—as a mandatory requirement in motor vehicles sold throughout the European Union since 2018—poses significant challenges to the development of 6G in emergency management, particularly due to the high occurrence of road accidents during heavy rainfall. To address the pressing need for assessing the reliability of 6G-based emergency management systems in SCs, in ref. [37], the authors thoroughly examine the technical aspects of designing and implementing vehicle-to-infrastructure (V2I) communication systems. The aim is to optimize reliability in an SC environment, considering the coexistence of both 5G and 6G network backbones. The main objectives outlined in ref. [38] focus on efficiently monitoring the intricate road environment in SC transportation using cutting-edge 6G digital twins (DTs). The aim is to perceive the complex road conditions within SC traffic, with a specific focus on vehicular networks (VNs) as the research subject. The study explores the potential for multi-sensor collaboration and fusion technology within the network to meet the active control requirements of intelligent vehicles. A segmentation network called C-LNet, which combines LiDAR and camera data, is proposed. C-LNet utilizes a double encoder–single decoder structure, with separate encoders extracting image and LiDAR features. By synchronizing LiDAR point cloud data and image data in the sensor space, the heterogeneous data are effectively unified. Additionally, a multiscale feature fusion-based method is designed to handle multimodal information related to vehicle collaboration. In ref. [39], the development of SCs relies on key solutions, such as 5G, IoT, automotive advancements, energy systems, high-speed digital capabilities, AI, data analytics, satellite communication, optics, and cyber-security. The integration of these systems and resources allows for the merging of mobility, communications, energy, water management, monitoring/control, performance management, predictability, and forecasting. Moreover, 6G enables the rapid sensing, processing, control, and human experience of vast amounts of data. The work aims to analyze the advantages and requirements of 6G technology and its potential impact on the development of smart, intelligent, and advanced cities. The potential of 6G joint communication and sensing (JCAS) in driving innovations and SC services highlights the use of wireless networks and millimeter-wave frequencies for perceptive radio networks and channel-based sensing [40]. The proposed concept focuses on operating in the mm-Wave beam space and measuring inter-cell links to enable more accurate spatial sensory activity monitoring. A simulation-based case study on vehicle detection and classification demonstrates the feasibility of the 6G-driven concept. The enhancements offer new service potentials, including recognizing the trajectory of passing road users.
In current computing systems, like edge computing and cloud computing, many emerging applications and practical scenarios are either unavailable or only partially implemented. This limitation has sparked interest in developing a comprehensive computing paradigm in both academia and industry. However, there is a gap in the existing research, with limited studies on the systematic design and review of such a comprehensive computing paradigm. To bridge this gap, this research introduces a new concept called aerial computing, which combines aerial radio access networks and edge computing. In ref. [41], the authors propose a novel comprehensive computing architecture composed of low-altitude computing (LAC), high-altitude computing (HAC), and satellite computing platforms, along with conventional computing systems. Aerial computing offers several desirable attributes, including global computing service, improved mobility, increased scalability and availability, and simultaneity. The study also explores key technologies that enable aerial computing, such as energy refilling, edge computing, network software, frequency spectra, multi-access techniques, AI, and big data. It discusses various vertical domain applications, such as SCs, smart vehicles, smart factories, and smart grids, which can benefit from aerial computing.
Among the different sectors, emergency services play a crucial role in the realization of future SCs. In this regard, firefighting stands out as a vital component for ensuring security and safety. However, effective firefighting necessitates ultra-reliable and low-latency communications, enabling firefighters to receive real-time guidance through the utilization of distributed sensors, robots, and other technologies. In this context, in ref. [42], the research focuses on the application of edge micro-data centers (EMDCs) to enhance fire prediction and management. A novel three-stage architecture is proposed. The initial stage involves predicting and classifying the occurrence of fires based on sensor data available at the EMDC. The second stage focuses on confirming the fire occurrence using a CNN classification model. Once the fire occurrence has been confirmed, the final stage involves notifying the tenants and streaming a 360-degree monitoring video to the nearby fire station after processing at the EMDC.

8. Augmented and Virtual Reality

The use of AR and VR technologies in the context of vehicular technologies in SCs will also benefit from the new features of 6G networks. In the SC context, vehicles are expected to use local (V2V) and global V2X communications to allow for safe, more efficient, and more comfortable driving, since the vehicle will be able to recognize dangerous situations preemptively, even if these are out of visual range due to a bend, or with other vehicles ahead [43]. For example, in ref. [44], the following situation is presented: Vehicles are moving—one behind the other—when suddenly, a pedestrian crosses the road in front of the first car. The first vehicle camera detects the situation and shares the image of the pedestrian with the vehicle behind it. The vehicle processes the information and shows a visual alert on the windshield along with the image of the pedestrian in AR. This use case requires high reliability, availability, low latency, and a high data rate, all of which can be assured by employing 6G communication techniques [43].

References

  1. Ismail, L.; Buyya, R. Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions. Sensors 2022, 22, 5750.
  2. Letaief, K.B.; Chen, W.; Shi, Y.; Zhang, J.; Zhang, Y.J.A. The Roadmap to 6G: AI Empowered Wireless Networks. IEEE Commun. Mag. 2019, 57, 84–90.
  3. Farooq, M.S.; Nadir, R.M.; Rustam, F.; Hur, S.; Park, Y.; Ashraf, I. Nested Bee Hive: A Conceptual Multilayer Architecture for 6G in Futuristic Sustainable Smart Cities. Sensors 2022, 22, 5950.
  4. Chen, Z.; Ning, B.; Han, C.; Tian, Z.; Li, S. Intelligent Reflecting Surface Assisted Terahertz Communications Toward 6G. IEEE Wirel. Commun. 2021, 28, 110–117.
  5. Yang, F.; Pitchappa, P.; Wang, N. Terahertz reconfigurable intelligent surfaces (RISs) for 6G communication links. Micromachines 2022, 13, 285.
  6. Zhu, Y.; Mao, B.; Kato, N. Intelligent Reflecting Surface in 6G Vehicular Communications: A Survey. IEEE Open J. Veh. Technol. 2022, 3, 266–277.
  7. Yang, C.; He, Z.; Peng, Y.; Wang, Y.; Yang, J. Deep Learning Aided Method for Automatic Modulation Recognition. IEEE Access 2019, 7, 109063–109068.
  8. Shi, J.; Qi, L.; Li, K.; Lin, Y. Signal Modulation Recognition Method Based on Differential Privacy Federated Learning. Wirel. Commun. Mob. Comput. 2021, 2021, 2537546.
  9. Ren, X.; Gu, H.; Wei, W. Tree-RNN: Tree structural recurrent neural network for network traffic classification. Expert Syst. Appl. 2021, 167, 114363.
  10. Lopez-Martin, M.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things. IEEE Access 2017, 5, 18042–18050.
  11. Ye, H.; Li, G.Y.; Juang, B.H. Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems. IEEE Wirel. Commun. Lett. 2018, 7, 114–117.
  12. Hijji, M.; Iqbal, R.; Pandey, A.K.; Doctor, F.; Karyotis, C.; Rajeh, W.; Alshehri, A.; Aradah, F. 6G Connected Vehicle Framework to Support Intelligent Road Maintenance Using Deep Learning Data Fusion. IEEE Trans. Intell. Transp. Syst. 2023, 24, 7726–7735.
  13. Li, P.; Zhong, Y.; Zhang, C.; Wu, Y.; Yu, R. FedRelay: Federated Relay Learning for 6G Mobile Edge Intelligence. IEEE Trans. Veh. Technol. 2023, 72, 5125–5138.
  14. Gao, J.; Hu, M.; Zhong, C.; Li, G.Y.; Zhang, Z. An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation. IEEE Trans. Wirel. Commun. 2022, 21, 1823–1835.
  15. Kamruzzaman, M.; Hossin, A.; Alruwaili, O.; Alanazi, S.; Alruwaili, M.; Alshammari, N.; Alaerjan, A.; Zaman, R. IoT-Oriented 6G Wireless Network System for Smart Cities. Comput. Intell. Neurosci. 2022, 2022, 4436.
  16. Aggarwal, S.; Kumar, N.; Tanwar, S. Blockchain-envisioned UAV communication using 6G networks: Open issues, use cases, and future directions. IEEE Internet Things J. 2020, 8, 5416–5441.
  17. Tekale, S.; Rajagopal, R.; Bhoopathy, V. 6G: Transformation of Smart Cities With Blockchain and AI. In Challenges and Risks Involved in Deploying 6G and NextGen Networks; IGI Global: Hershey, PA, USA, 2022; pp. 220–228.
  18. Samanta, S.; Sarkar, A.; Bulo, Y. Secure 6G Communication in Smart City Using Blockchain. In Proceedings of the Emerging Technologies in Data Mining and Information Security, Singapore, 4–10 May 2023; Dutta, P., Chakrabarti, S., Bhattacharya, A., Dutta, S., Piuri, V., Eds.; pp. 487–496.
  19. Bhat, S.A.; Sofi, I.B.; Chi, C.Y. Edge computing and its convergence with blockchain in 5G and beyond: Security, challenges, and opportunities. IEEE Access 2020, 8, 205340–205373.
  20. Pajooh, H.H.; Demidenko, S.; Aslam, S.; Harris, M. Blockchain and 6G-Enabled IoT. Inventions 2022, 7, 109.
  21. Jahid, A.; Alsharif, M.H.; Hall, T.J. The convergence of Blockchain, IoT and 6G: Potential, opportunities, challenges and research roadmap. J. Netw. Comput. Appl. 2023, 217, 103677.
  22. Ali, M.Z.; Abohmra, A.; Usman, M.; Zahid, A.; Heidari, H.; Imran, M.A.; Abbasi, Q.H. Quantum for 6G communication: A perspective. IET Quantum Commun. 2023, 10, 12391.
  23. Muheidat, F.; Dajani, K.; Tawalbeh, L.A. Security Concerns for 5G/6G Mobile Network Technology and Quantum Communication. Procedia Comput. Sci. 2022, 203, 32–40.
  24. Mohammed, N.J. Quantum cryptography in Convolution neural network approach in Smart cities. J. Surv. Fish. Sci. 2023, 10, 2043–2056.
  25. Manzalini, A. Quantum communications in future networks and services. Quantum Rep. 2020, 2, 221–232.
  26. Parvaresh, N.; Kantarci, B. A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities. IEEE Open J. Commun. Soc. 2023, 4, 700–712.
  27. Mukherjee, A.; De, D.; Dey, N.; Crespo, R.G.; Herrera-Viedma, E. DisastDrone: A Disaster Aware Consumer Internet of Drone Things System in Ultra-Low Latent 6G Network. IEEE Trans. Consum. Electron. 2023, 69, 38–48.
  28. Wei, X.; Yang, H.; Huang, W. Low-delay Routing Scheme for UAV Communications in Smart Cities. IEEE Internet Things J. 2023, 32, 67131.
  29. Tahir, A.; Abid, S.A.; Shah, N. Logical clusters in a DHT-Paradigm for scalable routing in MANETs. Comput. Netw. 2017, 128, 142–153.
  30. Yan, X.; An, X.; Ye, W.; Zhao, M.; Wu, J. A Blockchain-based Subscriber Data Management Scheme for 6G Mobile Communication System. In Proceedings of the 2021 IEEE Globecom Workshops (GC Wkshps), Madrid, Spain, 19–23 June 2021; pp. 1–6.
  31. Yan, X.; An, X.; Ye, W.; Zhao, M.; Xi, Y.; Wu, J. User-Centric Network Architecture Design for 6G Mobile Communication Systems. In Proceedings of the 2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), London, UK, 23–28 January 2023; pp. 305–310.
  32. Lucic, M.C.; Bouhamed, O.; Ghazzai, H.; Khanfor, A.; Massoud, Y. Leveraging UAVs to Enable Dynamic and Smart Aerial Infrastructure for ITS and Smart Cities: An Overview. Drones 2023, 7, 79.
  33. Saini, H.K.; Jain, K.L. A New Way of Improving Network by Smart IoE with UAV. In Proceedings of the 2023 IEEE International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), Berlin, Germany, 4–8 May 2023; pp. 485–489.
  34. Alawadhi, A.; Almogahed, A.; Azrag, E. Towards Edge Computing for 6G Internet of Everything: Challenges and Opportunities. In Proceedings of the 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), Jeddah, Saudi Arabia, 23–25 January 2023; pp. 1–6.
  35. Konhäuser, W. Digitalization in Buildings and Smart Cities on the Way to 6G. Wirel. Pers. Commun. 2021, 121, 1289–1302.
  36. Chhabra, S.; Aiden, M.K.; Sabharwal, S.M.; Al-Asadi, M. 5G and 6G Technologies for Smart City. In Enabling Technologies for Effective Planning and Management in Sustainable Smart Cities; Springer: Cham, Switzerland, 2023; pp. 335–365.
  37. Fong, B.; Kim, H.; Fong, A.C.M.; Hong, G.Y.; Tsang, K.F. Reliability Optimization in the Design and Implementation of 6G Vehicle-to-Infrastructure Systems for Emergency Management in a Smart City Environment. IEEE Commun. Mag. 2023, 61, 148–153.
  38. Cheng, C.; Lv, H.; Lv, Z. Sensing fusion in vehicular network digital twins for 6G smart city. Itu J. Future Evol. Technol. 2022, 3, 342–358.
  39. Kulkarni, R.; Kulkarni, S. How 6G has an influence on smart cities: An overview. Int. J. Eng. Res. Technol. 2021, 10, 832–838.
  40. Häger, S.; Haferkamp, M.; Wietfeld, C. Beam-based 6G Networked Sensing Architecture for Scalable Road Traffic Monitoring. In Proceedings of the 2023 IEEE International Systems Conference (SysCon), Vancouver, BC, Canada, 15–18 June 2023; pp. 1–8.
  41. Pham, Q.V.; Ruby, R.; Fang, F.; Nguyen, D.C.; Yang, Z.; Le, M.; Ding, Z.; Hwang, W.J. Aerial computing: A new computing paradigm, applications, and challenges. IEEE Internet Things J. 2022, 9, 8339–8363.
  42. Gudepu, V.; Pappu, B.; Javvadi, T.; Bassoli, R.; Fitzek, F.H.; Valcarenghi, L.; Devi, D.V.N.; Kondepu, K. Edge Computing in Micro Data Centers for Firefighting in Residential Areas of Future Smart Cities. In Proceedings of the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Male, Maldives, 16–18 November 2022; pp. 1–6.
  43. Guevara, L.; Auat Cheein, F. The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems. Sustainability 2020, 12, 6469.
  44. Rameau, F.; Ha, H.; Joo, K.; Choi, J.; Park, K.; Kweon, I.S. A Real-Time Augmented Reality System to See-Through Cars. IEEE Trans. Vis. Comput. Graph. 2016, 22, 2395–2404.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Telecommunications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Maurizio Murroni
,
Matteo Anedda
,
Mauro Fadda
,
Pietro Ruiu
,
Vlad Popescu
,
Corneliu Zaharia
,
Daniele Giusto
View Times: 387
Revisions: 3 times (View History)
Update Date: 18 Sep 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback