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
Van-Hai Bui
 -- 2983 2023-10-26 17:20:05 |
2 only format change
Alfred Zheng
 Meta information modification 2983 2023-10-27 03:40:39 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Al Amin, A.; Hong, J.; Bui, V.; Su, W. Applications of the Smart Grid Served by 6G. Encyclopedia. Available online: https://encyclopedia.pub/entry/50847 (accessed on 19 November 2024).
Al Amin A, Hong J, Bui V, Su W. Applications of the Smart Grid Served by 6G. Encyclopedia. Available at: https://encyclopedia.pub/entry/50847. Accessed November 19, 2024.
Al Amin, Ahmed, Junho Hong, Van-Hai Bui, Wencong Su. "Applications of the Smart Grid Served by 6G" Encyclopedia, https://encyclopedia.pub/entry/50847 (accessed November 19, 2024).
Al Amin, A., Hong, J., Bui, V., & Su, W. (2023, October 26). Applications of the Smart Grid Served by 6G. In Encyclopedia. https://encyclopedia.pub/entry/50847
Al Amin, Ahmed, et al. "Applications of the Smart Grid Served by 6G." Encyclopedia. Web. 26 October, 2023.
Applications of the Smart Grid Served by 6G
Edit
This entry is adapted from the peer-reviewed paper 10.3390/a16100474

A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs.

6G smart grid wireless communication

1. Introduction

The smart city consists of a number of key enablers, which are smart education, smart healthcare, a smart economy, smart factories, smart warehouses, smart transportation, etc. [1][2]. The key enablers of smart cities require uninterrupted and efficient power supplies to fulfill their requirements [1][2][3]. Moreover, to effectively pursue all the functionalities of a smart grid system, a suitable wireless communication is required that can provide a high data rate, reliability, flexibility, low latency, massive connectivity, security, and the necessary adaptive functionalities [1][4]. Among the various applications of the smart grid, massive connectivity and monitoring, secured communication for resource allocations and operations, and time-critical operations are the vital factors in the case of an effective, uninterrupted, and reliable power supply for smart cities [5][6]. Future 6G wireless communication is a suitable solution for the communication of smart grid systems by providing all the required key performance indicators (KPIs) in terms of communication to successfully perform the abovementioned smart grid applications [7][8][9].

1.1. Basics of the Smart City

The term “smart city” signifies a developed urban locality that ensures sustainable development with regard to the economy, as well as enhanced living standards by prevailing various key features such as industry, economy, education, health care, transportation, government, people, and living standards [10]. The smart city is a unique way through which to solve urban problems [11].
Smart applications are integrated into smart cities—whose daily activities are related to the modern technologies in the world [12]. Cities should have interconnected technology and management, along with peaceful and interactive living standards [11][12]. Therefore, the smart city can promptly overcome the worst scenarios and destructive crises [13]. According to Figure 1, the smart city is based on ten major key enablers. Furthermore, all the key enablers are equally important for the feasible advancement of smart cities [1][14]. The major smart city key enablers are briefly described as follows:

1.1.1. The Smart Grid

The smart grid is a future electrical network encompassing four major domains: bulk generation, transmission, distribution, and consumer segments. Moreover, uninterrupted communication between the domains is essential for the smart grid system [15]. Furthermore, the Internet of Things (IoT) and artificial intelligence (AI) are also integrated with the power grid in the case of the smart grid system [15]. In addition, the smart grid is not only a sustainable, cost and power-effective power supply solution, but it also provides high-quality, low losses, as well as a safe and secure power supply [16][17][18].

1.1.2. Smart Education

Distance learning is a common method of teaching in the current education system [19][20]. Moreover, future distance learning, or education systems, require a higher quality of Internet service, along with real-time interactions and lower power consumption [20]. Moreover, the improvement of the quality of remote learning and education will enhance the number of quality human resources, improve employment productivity, support influential institutions, and secure sustainable economic advancement [21].

1.1.3. Smart Transportation

Smart transportation is another key enabler for a sustainable smart city [22]. Smart transportation requires different communication technologies, such as vehicle-to-everything (V2X), vehicle-to-grid (V2G), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-cloud (V2C), vehicle-to-device (V2D), etc. Smart transportation avoids collisions, saves energy, and improves road-safety, as well as increases the efficiency of traffic [23]. Thus, smart transportation can play a crucial role in the economic advancement of the smart city.

1.1.4. Smart Healthcare

Smart healthcare is a crucial key enabler for smart cities [24][25]. Smart healthcare can provide intra-hospital observation, remote patient monitoring, virtual healthcare, long-distance surgery, remote learning for doctors and nurses, remote diagnoses, emergency services, and remote consultations [26]. These applications required new technologies like virtual reality (VR) and augmented reality (AR) which require real-time, reliable, low latency, and flexible communication networks [1][26]. Smart healthcare ensures precise and high-quality medical care for the residents of smart cities.

1.1.5. Smart Farming

Smart farming introduces information and communication technology (ICT) in the agricultural and farming sectors of smart cities [27]. The IoT, sensors, actuators, unmanned aerial vehicles (UAV), robots, geopositioning, big data, and other ICT applications are to be introduced into the cultivation process of the agricultural sector [27]. Several evolving technologies are to be introduced into smart farming like smart machines, crop sensors, cloud computing, as well as the IoT for the smart control, analysis, and planning of farming [28]. The process of smart farming consists of smart analysis and planning, smart sensing and monitoring, smart control, and storing big data in cloud devices [29]. Smart farming not only reduces labor requirements, but it also enhances the caliber of production [30].

1.1.6. Smart Social Networking

The popularity of social platforms and people from different categories are connected through online social networking (OSN) [31]. The data from OSN provide social, cultural, and economic information, which are utilized by the commercial industries, authorities, policymakers, and governments of smart cities to realize the market trends and behavioral patterns of smart city residents [32]. Therefore, the effective progression of smart cities is linked to individual behavior within online social networking (OSN) platforms [33][34].

1.1.7. Smart Factory and Smart Warehouse

The smart factory is defined as an intelligent or digital factory [35]. A fully connected manufacturing system is introduced in the case of the smart factory, which can operate without any human force by data generation, transmission, reception, and processing for production purposes [36]. Smart factories introduce novel technologies in the case of production, such as a cyber physical system (CPS), AI-based decision making, intelligent data exchange models, human–machine interactions, cloud-enabled manufacturing and services, and comprehensive connections for the IoT for the effective production of goods and the minimization of human forces [35][36].
The smart warehouse provides high-quality services for the residents of smart cities [37]. Moreover, the smart warehouse enhances the productivity of supply chain management and also provides advantages to the stack holders of the communities within smart cities [38].

1.1.8. Smart Environment

The smart environment is a vital key enabler for the advancement of the smart city [39]. The smart environment changes the city and also reshapes the environment of the city for the settlement of the residents [40]. Moreover, various environmental challenges such as waste management and the pollution of the smart city can be effectively handled by the smart environment [41][42]. Furthermore, AI-based intelligent resource management in the smart environment is also a vital factor for smart cities.
All of the functionalities of the abovementioned key enablers of the smart city, as well as smart power supply and management, are required to be uninterrupted. As mentioned earlier, the smart grid is a viable solution for fulfilling the power supply requirements of the smart city key enablers.

2. Applications of the Smart Grid Served by 6G

The smart grid holds immense significance for the advancement of the sustainable smart city. The key features of the smart grid communication network are its reliance on high speed, reliability, low latency, low jitter, flexibility, massive connectivity, higher area capacities, computational offloading, and secure data communication networks to effectively and intelligently manage complex power systems. Unlike traditional power grids, smart grids employ bidirectional communication, which greatly enhances their capabilities [43]. However, the specific communication demands and appropriate techniques can vary based on the particular environment and circumstance.
Figure 1 illustrates the flowchart regarding the operation logic and framework for implementing a smart grid by 6G. The goal and objectives of the smart grid communication network for the smart city applications are defined, such as massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations. The 6G-enabled smart grid infrastructure is designed based on these major requirements. The requirements are considered based on the goal and objectives of the smart grid for smart city applications. Afterward, all the requirements based on the objectives are verified. Subsequently, 6G-enabled smart grid infrastructure is deployed and integrated into the control and monitoring system. Afterward, the smart grid functionalities are tested and validated. The monitoring and management of a smart grid by 6G-enabled wireless communication is also performed accordingly, as is depicted in Figure 1.
Algorithms 16 00474 g004
Figure 1. Flowchart of a 6G-Enabled Smart Grid.
At present, low carbonization is one of the prime concerns for the power grid systems of smart cities [44]. The integration of 6G and a smart grid is a suitable solution for enhancing the coordinated operation of the smart grid by a damping technique for low carbonization. A 6G-enabled smart grid can provide real-time monitoring and a control of energy flows. Moreover, the integration of 6G and a smart grid can also provide enhanced energy efficiency, reduction in carbon emissions, and the support of sustainable energy practices. In addition, this synergy provides real-time grid condition monitoring, demand responses, wide-area monitoring and control, advanced control, and efficient load management by the massive, high-speed, and low-latency 6G wireless communication. Grid resilience, cybersecurity, and supportive policies play critical roles in ensuring a sustainable and reliable smart grid ecosystem by utilizing sustainable, secured, and reliable 6G wireless communication. The technology of 6G could have a significant impact on the various applications of smart grid systems.

2.1. Massive Connectivity and Monitoring

The technology of 6G wireless communication is a feasible solution for massive connectivity and the time-critical monitoring of smart grid systems. Furthermore, 6G can provide seamless communication among a massive number of devices, thereby facilitating real-time data exchange, comprehensive time-critical monitoring, and effective energy management. For time-critical monitoring, along with massive connectivity, the important KPIs are connection density, area traffic capacity, data rate, reliability, latency, energy efficiency, and spectral efficiency. PD-NOMA offers notable performance and advantages that facilitate enhanced data rates, spectral efficiency, massive connectivity, as well as low latencies to support massive connectivity along with the time-critical monitoring of smart grid systems [45]. In PD-NOMA, distinct transmission powers are assigned to multiple users according to their respective channel conditions. The superimposed signal, containing data for all users, is then transmitted to them [46]. Based on the reception and channel condition, the users employ SIC or direct decoding to decode their respective signal. The SIC decodes the signal of individual users sequentially from the superimposed signal based on their assigned power [47]. Therefore, PD-NOMA, along with OAM-MIMO (NOMA-OAM-MIMO), can provide massive connectivity, high data rates, and improved spectral efficiency. Moreover, IRS, THz, and BF can be incorporated with the D2D/cooperative relay-based NOMA-OAM-MIMO communication for smart grid UDNs to provide improved reliability, low latency, less power consumption, enhanced area capacity, and an enhanced coverage area of the UDNs of a smart grid [48][49][50][51][52][53][54][55][56][57]. Furthermore, NOMA-enabled Q-learning UDNs can also provide high area capacities and massive connectivity for the UDNs of a smart grid system [58][59]. In addition, energizing the massive number of connected devices is another challenging issue. Thus, NOMA-incorporated wireless power transfers, such as NOMA-SWIPT, are required. They are needed to energize the IoTs, UAVs, robots, and edge/cloud devices, as well as provide energy efficiency [60].
The grid monitoring algorithms (e.g., PLeC/AB-PLeC/FIB/BW-PLeC) can be processed by AI-based edge/cloud computing devices 8. The energy devices (e.g., IoTs, UAVs, and robots) can communicate with the edge/cloud devices via the abovementioned novel techniques of 6G wireless communication system for time-critical smart grid monitoring [61][62]. The fault identification/location, as well as the deployment of the phasor measurement unit (PMU), are vital factors for grid monitoring [61]. For medium voltage grids, along with low latency monitoring, three different algorithms (path length constraint (PLeC), the application-level betweenness, along with PLeC (AB-PLeC), as well as the flow interference and bandwidth constraint (FIB)) were proposed in [61]. These algorithms are mainly focused on the strategic deployment of high-capacity links, thus aiming to strike a trade-off between deployment expenses and the achieved latency. The PLeC along with bandwidth and path length constraint (BW-PLeC) algorithms are introduced to achieve a low-latency communication framework [62]. These algorithms aim to enhance the performance of the conventional power-line communication technique by incorporating alternative enhanced speed communication links at crucial locations of the smart grid. The primary goal is to meet the specific delay requirements while minimizing deployment costs. All the requirements of the monitoring algorithms can be fulfilled by the abovementioned novel communication techniques of 6G.

2.2. Secured Communication for Operations & Resource Management

The interconnection, reconfigurability, the wide variety of energy device types, and information networks introduce crucial implications for data security and reliability in smart grid systems [63][64][65]. Therefore, a massive amount of data are generated in case of bidirectional smart grid communication, and proper security and privacy are required for the operational and other confidential instances of data [17][66][67]. The operational integrity of the smart grid could be compromised by covert cyberattacks or the manipulation of data [68].
Thus, advanced encryption, authentication, and privacy mechanisms are required to protect the critical data transmitted between smart grid devices and control centers. In traditional artificial intelligence of things (AIoT) frameworks, vast amounts of identical energy-oriented data from the IoT devices of each user are transferred to cloud/edge devices for storage or decentralized processing [69]. However, this approach poses significant risks in terms of privacy infringement and potential data misuse. Federated learning (FL) is an attractive AI paradigm that ensures privacy while preserving data [69][70]. The energy data owners (EDOs) can collectively train the shared AI model, except in cases of divulging the energy-oriented data by an edge-cloud-assisted FL framework [70][71]. It facilitates efficient and fortified energy data communication for the respective smart grid users by integrating FL and the blockchain [71]. Furthermore, to address the challenge of limited knowledge regarding confidential multidimensional user data in real-world cases, a bi-layer deep re-enforcement-learning-inspired incentive algorithm is also introduced in [71]. This algorithm incentivizes active participation from energy data owners (EDOs), as well as encourages high-quality contributions to the shared model.
The software-defined IoT (SDIoT) in 6G makes the CPS more vulnerable to cyberattacks [72]. A novel graphics-processing-unit (GPU)-based adaptive robust state estimator named LSTMKF was introduced in [72]. This state estimator integrates a deep learning algorithm, and is specifically based on long short-term memory (LSTM) with an extended nonlinear Kalman filter. By combining these techniques, the LSTMKF state estimator enhances security measures and effectively manages the load within the system [72]. Edge computing is required for the computational offloading of LSTMKF due to computational complexity. However, the lightweight false state injection (FSI) can poison the state information of the edge devices in the case of deep-learning-based resource allocation [58]. Thus, to enhance security and privacy, an FL with the blockchain can be integrated for edge computing-based distributed computational offloading, as well as in also enhancing energy efficiency [69][70][71]. Moreover, the edge/cloud devices can be energized by the NOMA-SWIPT-based technique, which is discussed in an earlier subsection [60].

2.3. Time-Critical Operations

The real-time operations of smart grid systems, such as fault detection, outage management, island detection and management, energy management, and load balancing, can be supported by 6G. The network slicing based on non-real-time, close-to-real-time, and real-time operations is an effective solution for smart grid systems [73]. The time-critical applications of smart grid systems require area traffic capacity, spectral efficiency, latency, user data rate, and reliability. The novel 6G-enabled D2D/cooperative NOMA-OAM-MIMO, along with IRS, BF, THz, AI-based edge computing, and the real-time smart grid operational algorithms (e.g., EDGE/PMU-based voltage stability/phase-angle based island detection/learning-to-infer), can fulfill the KPI requirements and can also perform the time-critical operations of smart grids [74][75][76][77]. Furthermore, wireless power transfer (SWIPT/NOMA-SWIPT) can energize edge/cloud devices for time-critical operational purposes. Moreover, the emBB and mMTC are suitable for non-real-time applications, and mMTC is appropriate for semi-real-time applications and ultra-reliable-low-latency-communication (URLLC). Both are appropriate for time-critical (real-time) smart grid operations [73]. The time-sensitive communication service-based AI-enabled 6G network slicing approach is a possible solution for accomplishing the time-critical smart grid operations [73].
For the time-critical smart grid operations, there are several novel algorithms that have been proposed for time-critical energy management, island detection, and outage detection and recovery. The energy management of the smart grid system regarding the demand response is a time-critical issue that can be overcome by a hybrid EDGE algorithm [74]. Edge is evolved by combining a genetic algorithm (GA) along with an enhanced differential evolution (EDE) algorithm [74]. The phase angle-based island detection method is much more resilient, more secure, computationally simple, and takes less time to detect the island of a smart grid system [78]. The PMU-based voltage instability indexed method was introduced in [75]. The proposed index can also detect the outage of lines/gens in an unsupervised manner [75]. The AI-enabled network slicing and intelligent cloud/edge computing can also overcome the computational issue within the provided time constraint for the proposed technique. The real-time detection of line outages is an immensely challenging task, particularly in scenarios where unknown line outages rapidly accumulate, thus leading to large-scale blackouts [76]. Hence, a pioneering learning-to-infer approach has been devised to detect the time-critical multi-line outages in a smart grid [77]. Additionally, the integration of AI-edge computing and 6G wireless communication techniques can effectively tackle the computational complexity involved in the process. Moreover, 6G-based novel communication techniques, which are mentioned above along with AI-enabled network slicing and intelligent edge/cloud computing, can process the algorithms for computational offloading, as well as effectively perform the time-critical operations of smart grids.
Based on the above discussion, Table 1 summarizes the major applications and respective suitable techniques for 6G-based smart grid communication networks.
Table 1. The application and respective suitable techniques for 6G-based smart grid communication networks.
Major Applications Techniques
Massive connectivity D2D/cooperative PD-NOMA with IRS, beamforming, THz, AI-based edge computing, Q-learning
Monitoring PMU/PLeC/AB-PLeC/FIB/BW-PLeC
Energized massive connected devices NOMA-SWIPT
Security and privacy FL and blockchain
Reducing vulnerabilities Edge-cloud-assisted FL, the blockchain, and LSTMKF
Energized edge/cloud devices NOMA-SWIPT
Time-critical communication D2D/cooperative PD-NOMA with IRS, beamforming, THz, AI-enabled network slicing, AI-based edge computing
Real-time energy load balancing EDGE algorithm
Unsupervised voltage stability PMU-based index
Real-time island detection Phase-angle based detection
Real-time line outage detection and recovery Learning-to-infer approach
Energized edge/cloud devices NOMA-SWIPT

References

  1. Cui, J.; Ding, Z.; Fan, P. Beamforming Design for MISO Non-Orthogonal Multiple Access Systems. IET Commun. 2016, 11, 720–725.
  2. Shehab, M.; Khattab, T.; Kucukvar, M.; Trinchero, D. The Role of 5G/6G Networks in Building Sustainable and Energy-Efficient Smart Cities. In Proceedings of the 2022 IEEE 7th International Energy Conference (ENERGYCON), Riga, Latvia, 9–12 May 2022; pp. 1–7.
  3. Ma, R.; Chen, H.-H.; Huang, Y.-R.; Meng, W. Smart Grid Communication: Its Challenges and Opportunities. IEEE Trans. Smart Grid 2013, 4, 36–46.
  4. Panda, D.K.; Das, S. Smart Grid Architecture Model for Control, Optimization and Data Analytics of Future Power Networks with More Renewable Energy. J. Clean. Prod. 2021, 301, 126877.
  5. Fadel, E.; Gungor, V.C.; Nassef, L.; Akkari, N.; Malik, M.A.; Almasri, S.; Akyildiz, I.F. A Survey on Wireless Sensor Networks for Smart Grid. Comput. Commun. 2015, 71, 22–33.
  6. van der Kam, M.; Bekkers, R. Mobility in the Smart Grid: Roaming Protocols for EV Charging. IEEE Trans. Smart Grid 2023, 14, 810–822.
  7. NIST Framework 2.0. Roadmap for Smart Grid Interoperability Standards ; National Institute of Standards and Technology. 2010. Available online: https://www.nist.gov/system/files/documents/smartgrid/NIST_Framework_Release_2-0_corr.pdf (accessed on 3 October 2023).
  8. Alwis, C.D.; Kalla, A.; Pham, Q.V.; Kumar, P.; Dev, K.; Hwang, W.J.; Liyanage, M. Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research. IEEE Open J. Commun. Soc. 2021, 2, 836–886.
  9. Wang, C.-X.; You, X.; Gao, X.; Zhu, X.; Li, Z.; Zhang, C.; Hanzo, L. On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds. IEEE Commun. Surv. Tutor. 2023, 25, 905–974.
  10. Saad, M.M.; Khan, M.T.R.; Shah, S.H.A.; Kim, D. Advancements in Vehicular Communication Technologies: C-V2X and NR-V2X Comparison. IEEE Commun. Mag. 2021, 59, 107–113.
  11. Kaiser, M.S.; Ray, K.; Bandyopadhyay, A.; Jacob, K.; Long, K.S. (Eds.) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering: TCCE 2021, Parit Raja, Malaysia, 21–22 October 2022; Springer Nature: Berlin/Heidelberg, Germany, 2022.
  12. Neirotti, P.; De Marco, A.; Cagliano, A.C.; Mangano, G.; Scorrano, F. Current Trends in Smart City Initiatives: Some Stylised Facts. Cities 2014, 38, 25–36.
  13. Kunzmann, K.R. Smart Cities after COVID-19: Ten Narratives. disP-Plan. Rev. 2020, 56, 20–31.
  14. Zygiaris, S. Smart City Reference Model: Assisting Planners to Conceptualize the Building of Smart City Innovation Ecosystems. J. Knowl. Econ. 2013, 4, 217–231.
  15. Esenogho, E.; Djouani, K.; Kurien, A.M. Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect. IEEE Access 2022, 10, 4794–4831.
  16. Neffati, O.S.; Sengan, S.; Thangavelu, K.D.; Kumar, S.D.; Setiawan, R.; Elangovan, M.; Mani, D.; Velayutham, P. Migrating from Traditional Grid to Smart Grid in Smart Cities Promoted in Developing Country. Sustain. Energy Technol. Assess. 2021, 45, 101125.
  17. Dileep, G.J. A Survey on Smart Grid Technologies and Applications. Renew. Energy 2020, 146, 2589–2625.
  18. Adnan, M.; Ghadi, Y.; Ahmed, I.; Ali, M. Transmission Network Planning in Super Smart Grids: A Survey. IEEE Access 2023, 11, 77163–77227.
  19. Embarak, O. An Adaptive Paradigm for Smart Education Systems in Smart Cities using the Internet of Behaviour (IoB) and Explainable Artificial Intelligence (XAI). In Proceedings of the 2022 8th International Conference on Information Technology Trends (ITT), Dubai, United Arab Emirates, 25–26 May 2022; pp. 74–79.
  20. Cheung, S.K.S.; Kwok, L.F.; Phusavat, K.; Yang, H.H. Shaping the Future Learning Environments with Smart Elements: Challenges and Opportunities. Int. J. Educ. Technol. High. Educ. 2021, 18, 16.
  21. Masera, M.; Bompard, E.F.; Profumo, F.; Hadjsaid, N. Smart (Electricity) Grids for Smart Cities: Assessing Roles and Societal Impacts. Proc. IEEE 2018, 106, 613–625.
  22. Burlacu, M.; Boboc, R.G.; Butilă, E.V. Smart Cities and Transportation: Reviewing the Scientific Character of the Theories. Sustainability 2022, 14, 8109.
  23. Iyer, L.S. AI Enabled Applications Towards Intelligent Transportation. Transp. Eng. 2021, 5, 100083.
  24. Rocha, N.P.; Dias, A.; Santinha, G.; Rodrigues, M.; Queirós, A.; Rodrigues, C. Smart Cities and Public Health: A Systematic Review. Procedia Comput. Sci. 2019, 164, 516–523.
  25. Wielicka-Gańczarczyk, K.; Jonek-Kowalska, I. Involvement of Local Authorities in the Protection of Residents’ Health in the Light of the Smart City Concept on the Example of Polish Cities. Smart Cities 2023, 6, 744–763.
  26. Hobert, L.; Festag, A.; Llatser, I.; Altomare, L.; Visintainer, F.; Kovacs, A. Enhancements of V2X Communication in Support of Cooperative Autonomous Driving. IEEE Commun. Mag. 2015, 53, 64–70.
  27. Ruiz-Vanoye, J.A.; Barrera-Cámara, R.A.; Díaz-Parra, O.; Ramos-Fernández, J.C.; Fuentes-Penna, A.; Ochoa-Zezzatti, A.; Hernández-Aguilar, J.A.; Campero-Jurado, I. Surveying Smart Farming for Smart Cities. In AI, Edge and IoT-Based Smart Agriculture; Academic Press: Cambridge, MA, USA, 2022; pp. 241–262.
  28. Vermesan, O.; Friess, P. (Eds.) Digitising the Industry Internet of Things Connecting the Physical, Digital and Virtual Worlds; CRC Press: Boca Raton, FL, USA, 2022.
  29. Wolfert, S.; Ge, L.; Verdouw, C.; Bogaardt, M.J. Big Data in Smart Farming—A Review. Agric. Syst. 2017, 153, 69–80.
  30. Bacco, M.; Barsocchi, P.; Ferro, E.; Gotta, A.; Ruggeri, M. The Digitisation of Agriculture: A Survey of Research Activities on Smart Farming. Array 2019, 3, 100009.
  31. Moustaka, V.; Theodosiou, Z.; Vakali, A.; Kounoudes, A.; Anthopoulos, L.G. Enhancing Social Networking in Smart Cities: Privacy and Security Borderlines. Technol. Forecast. Soc. Chang. 2019, 142, 285–300.
  32. Bianchini, D.; Avila, I. Smart Cities and Their Smart Decisions: Ethical Considerations. IEEE Technol. Soc. Mag. 2014, 33, 33–40.
  33. Efstathiades, H.; Antoniades, D.; Pallis, G.; Dikaiakos, M.-D. Identification of Key Locations Based on Online Social Network Activity. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France, 25–28 August 2015.
  34. Doran, D.; Severin, K.; Gokhale, S.; Dagnino, A. Social Media Enabled Human Sensing for Smart Cities. AI Commun. 2016, 29, 57–75. Available online: http://knoesis.wright.edu/sites/default/files/aic14.pdf (accessed on 28 December 2017).
  35. Osterrieder, P.; Budde, L.; Friedli, T. The Smart Factory as a Key Construct of Industry 4.0: A Systematic Literature Review. Int. J. Prod. Econ. 2020, 221, 107476.
  36. Lasi, H.; Fettke, P.; Kemper, H.G.; Feld, T.; Hoffmann, M. Industry 4.0. Bus. Inf. Syst. Eng. 2014, 6, 239–242.
  37. Silva, B.N.; Khan, M.; Han, K. Towards Sustainable Smart Cities: A Review of Trends, Architectures, Components, and Open Challenges in Smart Cities. Sustain. Cities Soc. 2018, 38, 697–713.
  38. Jabbar, H. The Visible Hand: Markets, Politics, and Regulation in Post-Katrina New Orleans. Harv. Educ. Rev. 2016, 86, 1–26.
  39. Salleh, M.S.M.; Fahmy-Abdullah, M.; Sufahani, S.F.; Bin Ali, M.K. Smart Cities with Smart Environment. In Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering, Parit Raja, Malaysia, 21–22 October 2022; Lecture Notes in Networks and Systems; Springer: Singapore, 2022; Volume 348.
  40. Aletà, N.B.; Alonso, C.M.; Ruiz, R.M.A. Smart Mobility and Smart Environment in the Spanish Cities. Transp. Res. Procedia 2017, 24, 163–170.
  41. Stone, M.; Knapper, J.; Evans, G.; Aravopoulou, E. Information Management in the Smart City. Bottom Line 2018, 31, 234–249.
  42. Owa, F.D. Water Pollution: Sources, Effects, Control and Management. Mediterr. J. Soc. Sci. 2013, 4, 65–68.
  43. Abrahamsen, F.E.; Ai, Y.; Cheffena, M. Communication technologies for smart grid: A comprehensive survey. Sensors 2021, 21, 8087.
  44. Huang, Y.; Sun, Q.; Li, Y.; Sun, C.; Chen, Z. Damping technique empowered robust energy flow calculation for integrated energy systems. Appl. Energy 2023, 343, 121168.
  45. Jayachandran, M.; Kalaiarasy, C. Power-domain NOMA for massive connectivity in smart grid communication networks. In Proceedings of the International Conference on Power Electronics and Renewable Energy Systems: ICPERES 2021, Chennai, India, 25–25 March 2021; Springer: Singapore, 2022; pp. 205–212.
  46. Ding, Z.; Fan, P.; Poor, H.V. Impact of user pairing on 5G nonorthogonal multiple-access downlink transmissions. IEEE Trans. Veh. Technol. 2016, 65, 6010–6023.
  47. Bhatia, V.; Swami, P.; Sharma, S.; Mitra, R. Non-orthogonal multiple access as an enabler for massive connectivity for 5G and beyond networks. arXiv 2019, arXiv:1912.07077.
  48. Vlachos, E.; Thompson, J. Energy-Efficiency Maximization of Hybrid Massive MIMO Precoding with Random-Resolution DACs via RF Selection. IEEE Trans. Wirel. Commun. 2021, 20, 1093–1104.
  49. Amin, A.A.; Shin, S.Y. Channel Capacity Analysis of Non-Orthogonal Multiple Access with OAM-MIMO System. IEEE Wirel. Commun. Lett. 2020, 9, 1481–1485.
  50. Al Amin, A.; Shin, S.Y. Capacity Enhancement of NOMA-MIMO with OAM-IM. IEEE Wirel. Commun. Lett. 2021, 10, 924–928.
  51. Uddin, M.B.; Kader, M.F.; Shin, S.Y. Exploiting NOMA in D2D assisted full-duplex cooperative relaying. Phys. Commun. 2020, 38, 100914.
  52. Aswathi, V.; Babu, A.V. Full/half duplex cooperative relaying NOMA network under power splitting based SWIPT: Performance analysis and optimization. Phys. Commun. 2021, 46, 101335.
  53. Hawasli, M.; Çolak, S.A. Toward green 5G heterogeneous small-cell networks: Power optimization using load balancing technique. AEU-Int. J. Electron. Commun. 2017, 82, 474–485.
  54. Björnson, E.; Özdogan, Ö.; Larsson, E.G. Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions. IEEE Commun. Mag. 2020, 58, 90–96.
  55. Chen, J.C. Capacity improvement for intelligent reflecting surface-assisted wireless systems with a small portion of active elements. IEEE Access 2022, 10, 100438–100445.
  56. Lin, T.; Zhu, Y. Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning. IEEE Wirel. Commun. Lett. 2020, 9, 103–107.
  57. Kar, U.N.; Sanyal, D.K. An overview of device-to-device communication in cellular networks. ICT Express 2018, 4, 203–208.
  58. Zhang, H.; Feng, M.; Long, K.; Karagiannidis, G.K.; Nallanathan, A. Artificial Intelligence-Based Resource Allocation in Ultradense Networks: Applying Event-Triggered Q-Learning Algorithms. IEEE Veh. Technol. Mag. 2019, 14, 56–63.
  59. Qin, Z.; Yue, X.; Liu, Y.; Ding, Z.; Nallanathan, A. User Association and Resource Allocation in Unified NOMA Enabled Heterogeneous Ultra Dense Networks. IEEE Commun. Mag. 2018, 56, 86–92.
  60. Kizilkaya, B.; Zhao, G.; Sambo, Y.A.; Li, L.; Imran, M.A. 5G-Enabled Education 4.0: Enabling Technologies, Challenges and Solutions. IEEE Access 2021, 9, 166962–166969.
  61. Yang, B.; Katsaros, K.V.; Chai, W.K.; Pavlou, G. Cost-efficient low latency communication infrastructure for synchrophasor applications in smart grids. IEEE Syst. J. 2016, 12, 948–958.
  62. Katsaros, K.V.; Yang, B.; Chai, W.K.; Pavlou, G. Low latency communication infrastructure for synchrophasor applications in distribution networks. In Proceedings of the 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), Venice, Italy, 3–6 November 2014; pp. 392–397.
  63. Xenofontos, C.; Zografopoulos, I.; Konstantinou, C.; Jolfaei, A.; Khan, M.K.; Choo, K.K.R. Consumer commercial and industrial iot (in) security: Attack taxonomy and case studies. IEEE Internet Things J. 2021, 9, 199–221.
  64. Hossain, M.T.; Islam, S.; Badsha, S.; Shen, H. Desmp: Differential privacy-exploited stealthy model poisoning attacks in federated learning. arXiv 2021, arXiv:2109.11141.
  65. Hossain, M.T.; Badsha, S.; Shen, H. Privacy security and utility analysis of differentially private cpes data. arXiv 2021, arXiv:2106.11225.
  66. Zografopoulos, I.; Konstantinou, C. Detection of malicious attacks in autonomous cyber-physical inverter-based microgrids. IEEE Trans. Ind. Inform. 2021, 18, 5815–5826.
  67. Liu, T.; Tan, Z.; Xu, C.; Chen, H.; Li, Z. Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy Build. 2020, 208, 109675.
  68. Islam, S.; Zografopoulos, I.; Hossain, M.T.; Badsha, S.; Konstantinou, C. A Resource Allocation Scheme for Energy Demand Management in 6G-enabled Smart Grid. In Proceedings of the 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 16–19 January 2023; pp. 1–5.
  69. Bouzinis, P.S.; Diamantoulakis, P.D.; Karagiannidis, G.K. Wireless Federated Learning (WFL) for 6G Networks Part I: Research Challenges and Future Trends. IEEE Commun. Lett. 2022, 26, 3–7.
  70. Su, Z.; Tan, C.L.; Kong, Y.; Nallanathan, A. Secure and Efficient Federated Learning for Smart Grid with Edge-Cloud Collaboration. IEEE Trans. Ind. Inform. 2022, 18, 1333–1344.
  71. Islam, A.; Al Amin, A.; Shin, S.Y. FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things. IEEE Wirel. Commun. Lett. 2022, 11, 972–976.
  72. Tariq, M.; Ali, M.; Naeem, F.; Poor, H.V. Vulnerability Assessment of 6G-Enabled Smart Grid Cyber–Physical Systems. IEEE Internet Things J. 2021, 8, 5468–5475.
  73. Borenius, S.; Hämmäinen, H.; Lehtonen, M.; Ahokangas, P. Smart grid evolution and mobile communications—Scenarios on the Finnish power grid. Electr. Power Syst. Res. 2021, 199, 107367.
  74. Albogamy, F.R.; Khan, S.A.; Hafeez, G.; Murawwat, S.; Khan, S.; Haider, S.I.; Basit, A.; Thoben, K.-D. Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid. Sustainability 2022, 14, 1792.
  75. Guddanti, K.P.; Matavalam, A.R.R.; Weng, Y. PMU-Based Distributed Non-Iterative Algorithm for Real-Time Voltage Stability Monitoring. IEEE Trans. Smart Grid 2020, 11, 5203–5215.
  76. US-Canada Power System Outage Task Force. Final Report on the August 14 2003 Blackout in the United States and Canada. 2004. Available online: https://cybercemetery.unt.edu/archive/energyreport/20090701154100/https://reports.energy.gov/ (accessed on 3 October 2023).
  77. Zhao, Y.; Chen, J.; Poor, H.V. A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification. IEEE Trans. Smart Grid 2020, 11, 555–564.
  78. Shukla, A.; Dutta, S.; Sadhu, P.K. An island detection approach by μ-PMU with reduced chances of cyber attack. Int. J. Electr. Power Energy Syst. 2021, 126, 106599.
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: Engineering, Electrical & Electronic
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Ahmed Al Amin
,
Junho Hong
,
Van-Hai Bui
,
Wencong Su
View Times: 467
Revisions: 2 times (View History)
Update Date: 27 Oct 2023
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback