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iEnergy 2022, 1(3): 285-305 https://doi.org/10.23919/IEN.2022.0040
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Shaping future low-carbon energy and transportation systems: Digital technologies and applications

Show Author's Information Jie Song1,2,  Guannan He1,2,  Jianxiao Wang2,  Pingwen Zhang3
Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China
National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing 100871, China
School of Mathematical Sciences, Peking University, Beijing 100871, China
Keywords:
energy system, transportation system, Digitalization, decarbonization, energy and transportation integration
Cite this article:
Song J, He G, Wang J, et al. Shaping future low-carbon energy and transportation systems: Digital technologies and applications. iEnergy, 2022, 1(3): 285-305. https://doi.org/10.23919/IEN.2022.0040
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Abstract Full text About this article

Digitalization and decarbonization are projected to be two major trends in the coming decades. As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern. This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles. This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems.


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About this article

Shaping future low-carbon energy and transportation systems: Digital technologies and applications

Show Author's information Jie Song1,2, Guannan He1,2, Jianxiao Wang2, Pingwen Zhang3
Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China
National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing 100871, China
School of Mathematical Sciences, Peking University, Beijing 100871, China

Abstract

Digitalization and decarbonization are projected to be two major trends in the coming decades. As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern. This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles. This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems.

Keywords: energy system, transportation system, Digitalization, decarbonization, energy and transportation integration

References(286)

[1]

Davis, S. J., Lewis, N. S., Shaner, M., Aggarwal, S., Arent, D., Azevedo, I. L., Benson, S. M., Bradley, T., Brouwer, J., Chiang, Y. M., et al. (2018). Net-zero emissions energy systems. Science, 360: eaas9793.
DOI Google Scholar
[2]

Zhuo, Z. Y., Du, E. S., Zhang, N., Nielsen, C. P., Lu, X., Xiao, J. Y., Wu, J. W., Kang, C. Q. (2022). Cost increase in the electricity supply to achieve carbon neutrality in China. Nature Communications, 13: 3172.
DOI Google Scholar
[3]

Grubler, A., Wilson, C., Bento, N., Boza-Kiss, B., Krey, V., McCollum, D. L., Rao, N. D., Riahi, K., Rogelj, J., de Stercke, S., et al. (2018). A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies. Nature Energy, 3: 515–527.
DOI Google Scholar
[4]

Barrett, J., Pye, S., Betts-Davies, S., Broad, O., Price, J., Eyre, N., Anable, J., Brand, C., Bennett, G., Carr-Whitworth, R., et al. (2022). Energy demand reduction options for meeting national zero-emission targets in the United Kingdom. Nature Energy, 7: 726–735.
DOI Google Scholar
[5]

Hittinger, E., Jaramillo, P. (2019). Internet of Things: Energy boon or bane? Science, 364: 326–328.
DOI Google Scholar
[6]

Avila, A. M., Mezić, I. (2020). Data-driven analysis and forecasting of highway traffic dynamics. Nature Communications, 11: 2090.
DOI Google Scholar
[7]

Asensio, O. I., Alvarez, K., Dror, A., Wenzel, E., Hollauer, C., Ha, S. (2020). Real-time data from mobile platforms to evaluate sustainable transportation infrastructure. Nature Sustainability, 3: 463–471.
DOI Google Scholar
[8]

Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22: 52–67.
DOI Google Scholar
[9]

Sung, W. T., Tsai, M. H. (2012). Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms. Computers & Mathematics with Applications, 64: 1450–1461.
DOI Google Scholar
[10]
Kodali, R. K., Jain, V., Bose, S., Boppana, L. (2016). IoT based smart security and home automation system. In: Proceedings of the 2016 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.
DOI
[11]

Zhao, Y. B., Ye, Z. H. (2008). A low cost GSM/GPRS based wireless home security system. IEEE Transactions on Consumer Electronics, 54: 567–572.
DOI Google Scholar
[12]

Yang, F., Du, L., Chen, W. G., Li, J., Wang, Y. Y., Wang, D. S. (2017). Hybrid energy harvesting for condition monitoring sensors in power grids. Energy, 118: 435–445.
DOI Google Scholar
[13]

Iwaszenko, S., Kalisz, P., Słota, M., Rudzki, A. (2021). Detection of natural gas leakages using a laser-based methane sensor and UAV. Remote Sensing, 13: 510.
DOI Google Scholar
[14]
Paulet, M. V., Salceanu, A., Neacsu, O. M. (2016). Ultrasonic radar. In: Proceedings of the 2016 International Conference and Exposition on Electrical and Power Engineering (EPE), Iasi, Romania.
DOI
[15]
Barbagli, B., Manes, G., Facchini, R., Manes, A. (2012). Acoustic sensor network for vehicle traffic monitoring. In: Proceedings of the 1st International Conference on Advances in Vehicular Systems, Technologies and Applications, Venice, Italy.
[16]
US NHTSA (2016). Department of transportation, national highway traffic safety administration.
[17]
Rajab, S. A., Mayeli, A., Refai, H. H. (2014). Vehicle classification and accurate speed calculation using multi-element piezoelectric sensor. In: Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA.
DOI
[18]

Quang, V. V., Thang, V. T. (2021). A novel system for measuring vehicle speed via analog signals of pyroelectric infrared sensors. International Journal of Modern Physics B, 35: 2140028.
DOI Google Scholar
[19]

Rajaraman, V. (2017). Radio frequency identification. Resonance, 22: 549–575.
DOI Google Scholar
[20]

Zhang, J. Y., Yao, H. Y., Rizzoni, G. (2017). Fault diagnosis for electric drive systems of electrified vehicles based on structural analysis. IEEE Transactions on Vehicular Technology, 66: 1027–1039.
DOI Google Scholar
[21]

Liu, Z. T., He, H. W. (2017). Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter. Applied Energy, 185: 2033–2044.
DOI Google Scholar
[22]

Hassan, N., Yau, K. L. A., Wu, C. (2019). Edge computing in 5G: A review. IEEE Access, 7: 127276–127289.
DOI Google Scholar
[23]

Wu, Q. Q., Li, G. Y., Chen, W., Ng, D. W. K., Schober, R. (2017). An overview of sustainable green 5G networks. IEEE Wireless Communications, 24: 72–80.
DOI Google Scholar
[24]
Liu, J. H., Wang, S. Q., Yang, Q. C., Li, H. J., Deng, F. Z., Zhao, W. J. (2021). Feasibility study of power demand response for 5G base station. In: Proceedings of the 2021 IEEE International Conference on Power Electronics, Computer Applications, Shenyang, China.
DOI
[25]

Hui, H. X., Ding, Y., Shi, Q. X., Li, F. X., Song, Y. H., Yan, J. Y. (2020). 5G network-based Internet of Things for demand response in smart grid: A survey on application potential. Applied Energy, 257: 113972.
DOI Google Scholar
[26]

Zhou, W. Q., Chen, L. Y., Tang, S. P., Lai, L. J., Xia, J. J., Zhou, F. S., Fan, L. S. (2022). S Offloading strategy with PSO for mobile edge computing based on cache mechanism. Cluster Computing, 25: 2389–2401.
DOI Google Scholar
[27]

Tang, S. P., Zhou, W. Q., Chen, L. Y., Lai, L. J., Xia, J. J., Fan, L. S. (2021). Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks. Physical Communication, 47: 101381.
DOI Google Scholar
[28]

Yang, P., Xiao, Y., Xiao, M., Li, S. Q. (2019). 6G wireless communications: Vision and potential techniques. IEEE Network, 33: 70–75.
DOI Google Scholar
[29]

Roy, C., Misra, S. (2021). Safe-passé: Dynamic handoff scheme for provisioning safety-as-a-service in 5G-enabled intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 22: 5415–5425.
DOI Google Scholar
[30]

do Vale Saraiva, T., Campos, C. A. V., Fontes, R. D. R., Rothenberg, C. E., Sorour, S., Valaee, S. (2021). An application-driven framework for intelligent transportation systems using 5G network slicing. IEEE Transactions on Intelligent Transportation Systems, 22: 5247–5260.
DOI Google Scholar
[31]

Din, S., Paul, A., Rehman, A. (2019). 5G-enabled Hierarchical architecture for software-defined intelligent transportation system. Computer Networks, 150: 81–89.
DOI Google Scholar
[32]

Tan, L., Yu, K. P., Lin, L., Cheng, X. F., Srivastava, G., Lin, J. C. W., Wei, W. (2022). Speech emotion recognition enhanced traffic efficiency solution for autonomous vehicles in a 5G-enabled space-air-ground integrated intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 23: 2830–2842.
DOI Google Scholar
[33]

Ibrahim, M. S., Jamlos, M. A., Mustafa, W. A., Idrus, S. Z. S. (2021). 4 × 1 array antenna with staging transmission line for vehicle 5G application. Journal of Physics: Conference Series, 1874: 012034.
DOI Google Scholar
[34]

Shah, S. A. A., Ahmed, E., Imran, M., Zeadally, S. (2018). 5G for vehicular communications. IEEE Communications Magazine, 56: 111–117.
DOI Google Scholar
[35]

Pattinson, J. A., Chen, H. B. (2020). A barrier to innovation: Europe’s ad-hoc cross-border framework for testing prototype autonomous vehicles. International Review of Law, Computers & Technology, 34: 108–122.
DOI Google Scholar
[36]

Qiao, L., Li, Y. J., Chen, D. L., Serikawa, S., Guizani, M., Lv, Z. H. (2021). A survey on 5G/6G, AI, and robotics. Computers and Electrical Engineering, 95: 107372.
DOI Google Scholar
[37]

Tanwar, S., Kakkar, R., Gupta, R., Raboaca, M. S., Sharma, R., Alqahtani, F., Tolba, A. (2022). Blockchain-based electric vehicle charging reservation scheme for optimum pricing. International Journal of Energy Research, 46: 14994–15007.
DOI Google Scholar
[38]
Germanà, R., de Santis, E., Liberati, F., di Giorgio, A. (2021). On the participation of charging point operators to the frequency regulation service using plug-in electric vehicles and 5G communications. In: Proceedings of the 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe, Bari, Italy.
DOI
[39]

Ghodki, M. K. (2013). Microcontroller and solar power based electrical energy management system for renewable energy applications. International Journal of Electrical Power & Energy Systems, 44: 852–860.
DOI Google Scholar
[40]

Koutroulis, E., Kalaitzakis, K. (2006). Design of a maximum power tracking system for wind-energy-conversion applications. IEEE Transactions on Industrial Electronics, 53: 486–494.
DOI Google Scholar
[41]
Steinhorst, S., Lukasiewycz, M., Narayanaswamy, S., Kauer, M., Chakraborty, S. (2014). Smart cells for embedded battery management. In: Proceedings of the 2014 IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, Hong Kong, China.
DOI
[42]

Kabalci, Y. (2016). A survey on smart metering and smart grid communication. Renewable and Sustainable Energy Reviews, 57: 302–318.
DOI Google Scholar
[43]

Viegas, J. L., Vieira, S. M., Melício, R., Mendes, V. M. F., Sousa, J. M. C. (2016). Classification of new electricity customers based on surveys and smart metering data. Energy, 107: 804–817.
DOI Google Scholar
[44]

Rajabi, A., Eskandari, M., Jabbari Ghadi, M., Ghavidel, S., Li, L., Zhang, J. F., Siano, P. (2019). A pattern recognition methodology for analyzing residential customers load data and targeting demand response applications. Energy and Buildings, 203: 109455.
DOI Google Scholar
[45]
Hou, J. M., Gao, Y. (2010). Greenhouse wireless sensor network monitoring system design based on solar energy. In: Proceedings of the 2010 International Conference on Challenges in Environmental Science and Computer Engineering, Wuhan, China.
DOI
[46]

Zhang, C. W. (2018). Greenhouse intelligent control system based on microcontroller. AIP Conference Proceedings, 1955: 040033.
DOI Google Scholar
[47]

Uysal, A., Soylu, E. (2017). Embedded system design and implementation of an intelligent electronic differential system for electric vehicles. International Journal of Advanced Computer Science and Applications, 8: 129–134.
DOI Google Scholar
[48]

Lu, H. M., Liu, Q., Tian, D. X., Li, Y. J., Kim, H., Serikawa, S. (2019). The cognitive Internet of vehicles for autonomous driving. IEEE Network, 33: 65–73.
DOI Google Scholar
[49]
Gerla, M., Lee, E. K., Pau, G., Lee, U. (2014). Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. In: Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea.
DOI
[50]
Bhatt, G., Manoharan, K., Chauhan, P. S., Bhattacharya, S. (2019). MEMS sensors for automotive applications: A review. In: Bhattacharya, S., Agarwal, A., Prakash, O., et al. Eds. Sensors for Automotive and Aerospace Applications. Springer: Singapore.
DOI
[51]

Luan, T. H., Lu, R. X., Shen, X. M., Bai, F. (2015). Social on the road: Enabling secure and efficient social networking on highways. IEEE Wireless Communications, 22: 44–51.
DOI Google Scholar
[52]

Maglaras, L., Al-Bayatti, A., He, Y., Wagner, I., Janicke, H. (2016). Social Internet of vehicles for smart cities. Journal of Sensor and Actuator Networks, 5: 3.
DOI Google Scholar
[53]
Nitti, M., Girau, R., Floris, A., Atzori, L. (2014). On adding the social dimension to the Internet of Vehicles: Friendship and middleware. In: Proceedings of the 2014 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Odessa, Ukraine.
DOI
[54]

Luo, L., Yu, H. F., Foerster, K. T., Noormohammadpour, M., Schmid, S. (2020). Inter-datacenter bulk transfers: Trends and challenges. IEEE Network, 34: 240–246.
DOI Google Scholar
[55]

Masanet, E., Shehabi, A., Lei, N. A., Smith, S., Koomey, J. (20120). Recalibrating global data center energy-use estimates. Science, 367: 984–986.
DOI Google Scholar
[56]
Magaki, I., Khazraee, M., Gutierrez, L. V., Taylor, M. B. (2016). ASIC clouds: specializing the datacenter. In: Proceedings of the 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture, Seoul, Korea (South).
DOI
[57]

Tripathi, R., Vignesh, S., Tamarapalli, V. (2017). Optimizing green energy, cost, and availability in distributed data centers. IEEE Communications Letters, 21: 500–503.
DOI Google Scholar
[58]

Huang, P., Copertaro, B., Zhang, X. X., Shen, J. C., Löfgren, I., Rönnelid, M., Fahlen, J., Andersson, D., Svanfeldt, M. (2020). A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating. Applied Energy, 258: 114109.
DOI Google Scholar
[59]

Kwon, S. (2020). Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations. Applied Energy, 276: 115424.
DOI Google Scholar
[60]

Habibi Khalaj, A., Abdulla, K., Halgamuge, S. K. (2018). Towards the stand-alone operation of data centers with free cooling and optimally sized hybrid renewable power generation and energy storage. Renewable and Sustainable Energy Reviews, 93: 451–472.
DOI Google Scholar
[61]

Jin, C. Q., Bai, X. L., Yang, C. (2019). Effects of airflow on the thermal environment and energy efficiency in raised-floor data centers: A review. The Science of the Total Environment, 695: 133801.
DOI Google Scholar
[62]

Kandasamy, R., Ho, J. Y., Liu, P. F., Wong, T. N., Toh, K. C., Chua, S. Jr. (2022). Two-phase spray cooling for high ambient temperature data centers: Evaluation of system performance. Applied Energy, 305: 117816.
DOI Google Scholar
[63]

Zhang, H. N., Shao, S. Q., Tian, C. Q., Zhang, K. Z. (2018). A review on thermosyphon and its integrated system with vapor compression for free cooling of data centers. Renewable and Sustainable Energy Reviews, 81: 789–798.
DOI Google Scholar
[64]

Cho, J., Kim, Y. (2016). Improving energy efficiency of dedicated cooling system and its contribution towards meeting an energy-optimized data center. Applied Energy, 165: 967–982.
DOI Google Scholar
[65]

Khosravi, A., Laukkanen, T., Vuorinen, V., Syri, S. (2021). Waste heat recovery from a data centre and 5G smart poles for low-temperature district heating network. Energy, 218: 119468.
DOI Google Scholar
[66]
NREL (2010). Best practices guide for energy-efficient data center design. Technical report, National Renewable Laboratory, USA.
[67]

Zhou, L. Y., Wang, C. K., Zhang, Q. (2022). The construction of folk sports featured towns based on intelligent building technology based on the Internet of Things. Applied Bionics and Biomechanics, 2022: 4541533.
DOI Google Scholar
[68]

Zouinkhi, A., Ayadi, H., Val, T., Boussaid, B., Abdelkrim, M. N. (2020). Auto-management of energy in IoT networks. International Journal of Communication Systems, 33: e4168.
DOI Google Scholar
[69]

Tushar, W., Wijerathne, N., Li, W. T., Yuen, C., Poor, H. V., Saha, T. K., Wood, K. L. (2018). Internet of Things for green building management: Disruptive innovations through low-cost sensor technology and artificial intelligence. IEEE Signal Processing Magazine, 35: 100–110.
DOI Google Scholar
[70]

Baniata, M., Reda, H. T., Chilamkurti, N., Abuadbba, A. (2021). Energy-efficient hybrid routing protocol for IoT communication systems in 5G and beyond. Sensors (Basel, Switzerland), 21: 537.
DOI Google Scholar
[71]

Elhebeary, M. R., Ibrahim, M. A. A., Aboudina, M. M., Mohieldin, A. N. (2018). Dual-source self-start high-efficiency microscale smart energy harvesting system for IoT. IEEE Transactions on Industrial Electronics, 65: 342–351.
DOI Google Scholar
[72]

Wei, M., Hong, S. H., Alam, M. (2016). An IoT-based energy-management platform for industrial facilities. Applied Energy, 164: 607–619.
DOI Google Scholar
[73]
Wan, L. J., Sun, D. W., Deng, J. H. (2010). Application of IOT in building energy consumption supervision. In: Proceedings of the 2010 International Conference on Anti-Counterfeiting, Security and Identification, Chengdu, China.
DOI
[74]

Rafsanjani, H. N., Ghahramani, A., Nabizadeh, A. H. (2020). iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings. Applied Energy, 266: 114892.
DOI Google Scholar
[75]

Yang, J. C., Han, Y. R., Wang, Y. F., Jiang, B., Lv, Z. H., Song, H. B. (2020). Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city. Future Generation Computer Systems, 108: 976–986.
DOI Google Scholar
[76]
Al-Dweik, A., Muresan, R., Mayhew, M., Lieberman, M. (2017). IoT-based multifunctional scalable real-time enhanced road side unit for intelligent transportation systems. In: Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada.
DOI
[77]
Saarika, P. S., Sandhya, K., Sudha, T. (2017). Smart transportation system using IoT. In: Proceedings of the 2017 International Conference on Smart Technologies for Smart Nation (SmartTechCon), Bengaluru, India.
DOI
[78]
Aydin, I., Karakose, M., Karakose, E. (2017). A navigation and reservation based smart parking platform using genetic optimization for smart cities. In: Proceedings of the 2017 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG), Stanbul, Turkey.
DOI
[79]

Ghosh, A., Chatterjee, T., Samanta, S., Aich, J., Roy, S. (2017). Distracted driving: A novel approach towards accident prevention. Advances in Computational Sciences and Technology, 10: 2693–2705.
Google Scholar
[80]

Gopalakrishnan, K. (2018). Deep learning in data-driven pavement image analysis and automated distress detection: A review. Data, 3: 28.
DOI Google Scholar
[81]

Celesti, A., Galletta, A., Carnevale, L., Fazio, M., Ĺay-Ekuakille, A., Villari, M. (2018). An IoT cloud system for traffic monitoring and vehicular accidents prevention based on mobile sensor data processing. IEEE Sensors Journal, 18: 4795–4802.
DOI Google Scholar
[82]

Bansal, K., Mittal, K., Ahuja, G., Singh, A., Gill, S. S. (2020). DeepBus: Machine learning based real time pothole detection system for smart transportation using IoT. Internet Technology Letters, 3: e156.
DOI Google Scholar
[83]
Soliman, I. A., Numair, M., Akl, M. M., Mansour, D. E. A., Elkholy, A. M., Hussien, M. G. (2021). Hosting capacity enhancement through IoT-based active power curtailment of PV generation. In: Proceedings of the 2021 22nd International Middle East Power Systems Conference (MEPCON), Assiut, Egypt.
DOI
[84]
Jiang, X. M., Li, Z. L., Zhang, Y., Zhou, Z. G., Tang, X., Zan, R. S. (2019). Research on leakage current filtering method of low voltage distribution network based on IoT. In: Proceedings of the 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration, Changsha, China.
DOI
[85]

Chamola, V., Sancheti, A., Chakravarty, S., Kumar, N., Guizani, M. (2020). An IoT and edge computing based framework for charge scheduling and EV selection in V2G systems. IEEE Transactions on Vehicular Technology, 69: 10569–10580.
DOI Google Scholar
[86]

Liao, S. Y., Li, J. H., Wu, J., Yang, W., Guan, Z. T. (2019). Fog-enabled vehicle as a service for computing geographical migration in smart cities. IEEE Access, 7: 8726–8736.
DOI Google Scholar
[87]

Khan, M. A., Ghosh, S., Busari, S. A., Huq, K. M. S., Dagiuklas, T., Mumtaz, S., Iqbal, M., Rodriguez, J. (2021). Robust, resilient and reliable architecture for V2X communications. IEEE Transactions on Intelligent Transportation Systems, 22: 4414–4430.
DOI Google Scholar
[88]

Yan, H. S., Ashikhmin, A., Yang, H. (2021). A scalable and energy-efficient IoT system supported by cell-free massive MIMO. IEEE Internet of Things Journal, 8: 14705–14718.
DOI Google Scholar
[89]
Fusco, G., Colombaroni, C., Comelli, L., Isaenko, N. (2015). Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models. In: Proceedings of the 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, Hungary.
DOI
[90]
Liu, W., Kim, S. W., Marczuk, K., Ang, M. H. (2014). Vehicle motion intention reasoning using cooperative perception on urban road. In: Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China.
DOI
[91]
Wu, Q., Huang, C., Wang, S. Y., Chiu, W. C., Chen, T. (2007). Robust parking space detection considering inter-space correlation. In: Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, Beijing, China.
DOI
[92]

Hou, Y., Edara, P., Sun, C. (2015). Traffic flow forecasting for urban work zones. IEEE Transactions on Intelligent Transportation Systems, 16: 1761–1770.
DOI Google Scholar
[93]
Kanoh, H., Furukawa, T., Tsukahara, S., Hara, K., Nishi, H., Kurokawa, H. (2005). Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network. In: Proceedings of the 2005 IEEE Intelligent Transportation Systems, 2005, Vienna, Austria.
[94]
Mamun, M. A. A., Puspo, J. A., Das, A. K. (2017). An intelligent smartphone based approach using IoT for ensuring safe driving. In: Proceedings of the 2017 International Conference on Electrical Engineering and Computer Science (ICECOS), Palembang, Indonesia.
DOI
[95]

Markovic, D. S., Zivkovic, D., Branovic, I., Popovic, R., Cvetkovic, D. (2013). Smart power grid and cloud computing. Renewable and Sustainable Energy Reviews, 24: 566–577.
DOI Google Scholar
[96]

Shuja, J., Gani, A., Shamshirband, S., Ahmad, R. W., Bilal, K. (2016). Sustainable cloud data centers: A survey of enabling techniques and technologies. Renewable and Sustainable Energy Reviews, 62: 195–214.
DOI Google Scholar
[97]

Giordano, A., Mastroianni, C., Menniti, D., Pinnarelli, A., Sorrentino, N. (2019). An energy community implementation: The unical energy cloud. Electronics, 8: 1517.
DOI Google Scholar
[98]

Schaefer, J. L., Siluk, J. C. M., de Carvalho, P. S., Renes Pinheiro, J., Schneider, P. S. (2020). Management challenges and opportunities for energy cloud development and diffusion. Energies, 13: 4048.
DOI Google Scholar
[99]
Muhammad KaleemUllah Khan, Nadeem Javaid, Shakeeb Murtaza, Maheen Zahid, Wajahat Ali Gilani, and Muhammad Junaid Ali. Efficient energy management using fog computing. In: Barolli, L., Kryvinska, N., Enokido, T., et al. Eds. Advances in Network-Based Information Systems. Springer, Cham.
[100]

Petri, I., Rana, O., Rezgui, Y., Fadli, F. (2021). Edge HVAC analytics. Energies, 14: 5464.
DOI Google Scholar
[101]

Agostinelli, S., Cumo, F., Guidi, G., Tomazzoli, C. (2021). Cyber-physical systems improving building energy management: Digital twin and artificial intelligence. Energies, 14: 2338.
DOI Google Scholar
[102]

Liyanage, S., Dia, H., Abduljabbar, R., Bagloee, S. (2019). Flexible mobility on-demand: An environmental scan. Sustainability, 11: 1262.
DOI Google Scholar
[103]

Guerrero-ibanez, J. A., Zeadally, S., Contreras-Castillo, J. (2015). Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and Internet of Things technologies. IEEE Wireless Communications, 22: 122–128.
DOI Google Scholar
[104]
Pop, M., Avram, C., Domuţa, C., Radu, D., Aştilean, A. (2019). Route planning strategy for smart tourism services development. In: Proceedings of the 2019 6th International Symposium on Electrical and Electronics Engineering (ISEEE), Galati, Romania.
[105]

Arthurs, P., Gillam, L., Krause, P., Wang, N., Halder, K., Mouzakitis, A. (2022). A taxonomy and survey of edge cloud computing for intelligent transportation systems and connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 23: 6206–6221.
DOI Google Scholar
[106]

Dai, Y. Y., Xu, D., Maharjan, S., Qiao, G. H., Zhang, Y. (2019). Artificial intelligence empowered edge computing and caching for Internet of vehicles. IEEE Wireless Communications, 26: 12–18.
Google Scholar
[107]

Mershad, K., Artail, H. (2013). Finding a STAR in a vehicular cloud. IEEE Intelligent Transportation Systems Magazine, 5: 55–68.
DOI Google Scholar
[108]

Datta, S. K., Haerri, J., Bonnet, C., Ferreira da Costa, R. (2017). Vehicles as connected resources: Opportunities and challenges for the future. IEEE Vehicular Technology Magazine, 12: 26–35.
DOI Google Scholar
[109]

Jiang, D. D., Huo, L. W., Zhang, P., Lv, Z. H. (2021). Energy-efficient heterogeneous networking for electric vehicles networks in smart future cities. IEEE Transactions on Intelligent Transportation Systems, 22: 1868–1880.
DOI Google Scholar
[110]

Báguena, M., Calafate, C. T., Cano, J. C., Manzoni, P. (2015). An adaptive anycasting solution for crowd sensing in vehicular environments. IEEE Transactions on Industrial Electronics, 62: 7911–7919.
DOI Google Scholar
[111]

Zheng, K., Meng, H. L., Chatzimisios, P., Lei, L., Shen, X. M. (2015). An SMDP-based resource allocation in vehicular cloud computing systems. IEEE Transactions on Industrial Electronics, 62: 7920–7928.
DOI Google Scholar
[112]

Liu, H., Zhang, Y., Yang, T. (2018). Blockchain-enabled security in electric vehicles cloud and edge computing. IEEE Network, 32: 78–83.
Google Scholar
[113]

Liu, X. H., Shan, M. Y., Zhang, L. H. (2016). Low-carbon supply chain resources allocation based on quantum chaos neural network algorithm and learning effect. Natural Hazards, 83: 389–409.
DOI Google Scholar
[114]

Xu, J., Chen, L. X., Ren, S. L. (2017). Online learning for offloading and autoscaling in energy harvesting mobile edge computing. IEEE Transactions on Cognitive Communications and Networking, 3: 361–373.
DOI Google Scholar
[115]

Zeng, D. Z., Gu, L., Yao, H. (2020). Towards energy efficient service composition in green energy powered Cyber-Physical Fog Systems. Future Generation Computer Systems, 105: 757–765.
DOI Google Scholar
[116]

Abbasi, M., Yaghoobikia, M., Rafiee, M., Jolfaei, A., Khosravi, M. R. (2020). Energy-efficient workload allocation in fog-cloud based services of intelligent transportation systems using a learning classifier system. IET Intelligent Transport Systems, 14: 1484–1490.
DOI Google Scholar
[117]
Chitchyan, R., Murkin, J. (2018). Review of blockchain technology and its expectations: Case of the energy sector. arXiv preprint: 1803.03567.
[118]

Zhang, N., Wang, Y., Kang, C. Q., Cheng, J. N., He, D., W. (2016). Blockchain technique in the energy Internet: Preliminary research framework and typical applications. Proceedings of the Chinese Society of Electrical Engineering, 36(15): 4011–4022.
Google Scholar
[119]
Swan, M. (2015). Blockchain: Blueprint for a new economy. Sebastopol, CA, USA: O’Reilly Media, Inc.
[120]

Andoni, M., Robu, V., Flynn, D., Abram, S., Geach, D., Jenkins, D., McCallum, P., Peacock, A. (2019). Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renewable and Sustainable Energy Reviews, 100: 143–174.
DOI Google Scholar
[121]
Burger, A., Kuhlmann, C., Richard, P., Weinmann. J. (2016). Blockchain in the energy transition. A survey among decision-makers in the german energy industry. Available at https://esmt.berlin/knowledge/blockchain-energy-transition-survey-among-decision-makers-german-energy-industry.
[122]

Mengelkamp, E., Gärttner, J., Rock, K., Kessler, S., Orsini, L., Weinhardt, C. (2018). Designing microgrid energy markets: A case study: The Brooklyn Microgrid. Applied Energy, 210: 870–880.
DOI Google Scholar
[123]

Aitzhan, N. Z., Svetinovic, D. (2018). Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streams. IEEE Transactions on Dependable and Secure Computing, 15: 840–852.
DOI Google Scholar
[124]
Mannaro, K., Pinna, A., Marchesi, M. (2017). Crypto-trading: Blockchain-oriented energy market. In: Proceedings of the 2017 AEIT International Annual Conference, Cagliari, Italy.
[125]
Sharma, P. K., Park, J. H. (2021). Blockchain-based secure mist computing network architecture for intelligent transportation sys-tems. IEEE Transactions on Intelligent Transportation Systems, 22: 5168–5177.
[126]

Xu, Z. S., Liang, W., Li, K. C., Xu, J. B., Jin, H. (2021). A blockchain-based roadside unit-assisted authentication and key agreement protocol for internet of vehicles. Journal of Parallel and Distributed Computing, 149: 29–39.
DOI Google Scholar
[127]

Buzachis, A., Celesti, A., Galletta, A., Fazio, M., Fortino, G., Villari, M. (2020). A multi-agent autonomous intersection management (MA-AIM) system for smart cities leveraging edge-of-things and blockchain. Information Sciences, 522: 148–163.
DOI Google Scholar
[128]

Zia, M. (2021). B-DRIVE: A blockchain based distributed IoT network for smart urban transportation. Blockchain: Research and Applications, 2: 100033.
DOI Google Scholar
[129]

Yu, R., Zhong, W. F., Xie, S. L., Yuen, C., Gjessing, S., Zhang, Y. (2016). Balancing power demand through EV mobility in vehicle-to-grid mobile energy networks. IEEE Transactions on Industrial Informatics, 12: 79–90.
DOI Google Scholar
[130]

Pop, C., Cioara, T., Antal, M., Anghel, I., Salomie, I., Bertoncini, M. (2018). Blockchain based decentralized management of demand response programs in smart energy grids. Sensors (Basel, Switzerland), 18: 162.
DOI Google Scholar
[131]

Nikoobakht, A., Aghaei, J., Mardaneh, M. (2016). Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory. Energy, 114: 846–861.
DOI Google Scholar
[132]

Hassija, V., Chamola, V., Garg, S., Krishna, D. N. G., Kaddoum, G., Jayakody, D. N. K. (2020). A blockchain-based framework for lightweight data sharing and energy trading in V2G network. IEEE Transactions on Vehicular Technology, 69: 5799–5812.
DOI Google Scholar
[133]

di Silvestre, M. L., Gallo, P., Ippolito, M. G., Sanseverino, E. R., Zizzo, G. (2018). A technical approach to the energy blockchain in microgrids. IEEE Transactions on Industrial Informatics, 14: 4792–4803.
DOI Google Scholar
[134]

Sikorski, J. J., Haughton, J., Kraft, M. (2017). Blockchain technology in the chemical industry: Machine-to-machine electricity market. Applied Energy, 195: 234–246.
DOI Google Scholar
[135]

Mengelkamp, E., Notheisen, B., Beer, C., Dauer, D., Weinhardt, C. (2018). A blockchain-based smart grid: Towards sustainable local energy markets. Computer Science - Research and Development, 33: 207–214.
DOI Google Scholar
[136]
Thakur, S., Hayes, B. P., Breslin, J. G. (2018). Distributed double auction for peer to peer energy trade using blockchains. In: Proceedings of the 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA), Rome, Italy.
[137]

Kang, J. W., Yu, R., Huang, X. M., Maharjan, S., Zhang, Y., Hossain, E. (2017). Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Transactions on Industrial Informatics, 13: 3154–3164.
DOI Google Scholar
[138]
Chaudhry, N., Yousaf, M. M. (2018). Consensus algorithms in blockchain: Comparative analysis, challenges and opportunities. In: Proceedings of the 2018 12th International Conference on Open Source Systems and Technologies (ICOSST), Lahore, Pakistan.
[139]
Porter, E. M., Heppelmann, E. J. (2015). How smart, connected products are transforming companies. Harvard Business Review, Available at https://hbr.org/2015/10/how-smart-connected-products-are-transforming-companies.
[140]
Muschalle, A., Stahl, F., Löser, A., Vossen, G. (2013). Pricing Approaches for Data Markets. In: Castellanos, M., Dayal, U., Rundensteiner, E. A. Eds. Enabling Real-Time Business Intelligence. BIRTE 2012. Lecture Notes in Business Information Processing. Springer, Berlin, Heidelberg.
[141]

Liang, F., Yu, W., An, D., Yang, Q. Y., Fu, X. W., Zhao, W. (2018). A survey on big data market: Pricing, trading and protection. IEEE Access, 6: 15132–15154.
DOI Google Scholar
[142]
Fernandez, R. C., Subramaniam, P., Franklin M. J. (2020). Data market platforms: Trading data assets to solve data problems. arXiv preprint: 2002.01047.
[143]

Pei, J. (2022). A survey on data pricing: From economics to data science. IEEE Transactions on Knowledge and Data Engineering, 34: 4586–4608.
DOI Google Scholar
[144]

Huang, L. H., Dou, Y. F., Liu, Y. Z., Wang, J. Z., Chen, G., Zhang, X. Y., Wang, R. Y. (2021). Toward a research framework to conceptualize data as a factor of production: The data marketplace perspective. Fundamental Research, 1: 586–594.
DOI Google Scholar
[145]

Nuño, E., Koivisto, M., Cutululis, N. A., Sørensen, P. (2018). On the simulation of aggregated solar PV forecast errors. IEEE Transactions on Sustainable Energy, 9: 1889–1898.
DOI Google Scholar
[146]

Wei, W., Liu, F., Mei, S. W. (2015). Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty. IEEE Transactions on Smart Grid, 6: 1364–1374.
DOI Google Scholar
[147]

Dolatabadi, A., Jadidbonab, M., Mohammadi-ivatloo, B. (2019). Short-term scheduling strategy for wind-based energy hub: A hybrid stochastic/IGDT approach. IEEE Transactions on Sustainable Energy, 10: 438–448.
DOI Google Scholar
[148]

Vahid-Ghavidel, M., Mahmoudi, N., Mohammadi-Ivatloo, B. (2019). Self-scheduling of demand response aggregators in short-term markets based on information gap decision theory. IEEE Transactions on Smart Grid, 10: 2115–2126.
DOI Google Scholar
[149]

Chen, K. N., Wu, W. C., Zhang, B. M., Sun, H. B. (2015). Robust restoration decision-making model for distribution networks based on information gap decision theory. IEEE Transactions on Smart Grid, 6: 587–597.
DOI Google Scholar
[150]

Mehdizadeh, A., Taghizadegan, N., Salehi, J. (2018). Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management. Applied Energy, 211: 617–630.
DOI Google Scholar
[151]

Sadiq, A., Javed, M. U., Khalid, R., Almogren, A., Shafiq, M., Javaid, N. (2020). Blockchain based data and energy trading in Internet of electric vehicles. IEEE Access, 9: 7000–7020.
Google Scholar
[152]
Ramachandran, G., Ji, X., Navaney, P., Zheng, L. C., Martinez, M., Krishnamachari, B. (2019). MOTIVE: Micropayments for trusted vehicular services. arXiv preprint: 1904.01630.
[153]

Yang, X., Deng, J., Li, H., Fang, T., Ma, Z. (2016). Design and research on public service and interactive platform in electric vehicle. Power System Protection and Control, 44(10): 137–144.
Google Scholar
[154]

Javaid, N., Hussain, S., Ullah, I., Noor, M., Abdul, W., Almogren, A., Alamri, A. (2017). Demand side management in nearly zero energy buildings using heuristic optimizations. Energies, 10: 1131.
DOI Google Scholar
[155]

Bulut, E., Kisacikoglu, M. C., Akkaya, K. (2019). Spatio-temporal non-intrusive direct V2V charge sharing coordination. IEEE Transactions on Vehicular Technology, 68: 9385–9398.
DOI Google Scholar
[156]

Guo, Q. L., Wang, B. H., Tian, N. F., Sun, H. B., Wen B. J. (2020). Data transactions in energy internet: Architecture and key technologies. Transactions of China Electrotechnical Society, 35(11): 2285–2295.
Google Scholar
[157]
Wang, B. H., Guo, Q. L., Yang, T. Y. (2019). From uncertainty elimination to profit enhancement: Role of data in demand response. In: Proceedings of the 2019 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia), Chengdu, China.
[158]

Miranda, V., Hang, P. S. (2005). Economic dispatch model with fuzzy wind constraints and attitudes of dispatchers. IEEE Transactions on Power Systems, 20: 2143–2145.
DOI Google Scholar
[159]

Lee, D., Shin, H., Baldick, R. (2018). Bivariate probabilistic wind power and real-time price forecasting and their applications to wind power bidding strategy development. IEEE Transactions on Power Systems, 33: 6087–6097.
DOI Google Scholar
[160]

Dvorkin, Y., Lubin, M., Backhaus, S., Chertkov, M. (2016). Uncertainty sets for wind power generation. IEEE Transactions on Power Systems, 31: 3326–3327.
DOI Google Scholar
[161]

Wang, W., Jiang, L., Wang, Z., Song, J., Tian, N., Jiang, W. (2016). Trade model of smart grid big data based on vector evaluated genetic algorithm. Power System and Clean Energy, 32(10): 1–8.
Google Scholar
[162]

Wang, B. H., Guo, Q. L., Yang, T. Y., Xu, L., Sun, H. B. (2021). Data valuation for decision-making with uncertainty in energy transactions: A case of the two-settlement market system. Applied Energy, 288: 116643.
DOI Google Scholar
[163]

Gao, F., Zhu, L. H., Shen, M., Sharif, K., Wan, Z. G., Ren, K. (2018). A blockchain-based privacy-preserving payment mechanism for vehicle-to-grid networks. IEEE Network, 32: 184–192.
DOI Google Scholar
[164]

Li, Z.C., Wang, L.M., Ge, S.J., Ma, D.H., Qin, B. (2019). Big data plain text watermarking based on orthogonal coding. Computer Science, 46: 148–154.
Google Scholar
[165]

Ferrag, M. A., Maglaras, L. A., Janicke, H., Jiang, J. M., Shu, L. (2018). A systematic review of data protection and privacy preservation schemes for smart grid communications. Sustainable Cities and Society, 38: 806–835.
DOI Google Scholar
[166]

Giraldo, J., Sarkar, E., Cardenas, A. A., Maniatakos, M., Kantarcioglu, M. (2017). Security and privacy in cyber-physical systems: A survey of surveys. IEEE Design & Test, 34: 7–17.
Google Scholar
[167]
Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. Available at https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication#fullTextFileContent
[168]
Boschert, S., Rosen, R. (2016). Digital twin—The simulation aspect. In: Hehenberger, P., Bradley, D. Eds. Mechatronic Futures. Springer, Cham.
[169]

Bhatti, G., Mohan, H., Singh, R. R. (2021). Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews, 141: 110801.
DOI Google Scholar
[170]

El Saddik, A. (2018). Digital twins: The convergence of multimedia technologies. IEEE MultiMedia, 25: 87–92.
Google Scholar
[171]
GE Digital Twin (2016). Analytic engine for the digital power plant. Available at https://www.ge.com/digital/sites/default/files/download_assets/Digital-Twin-for-the-digital-power-plant-.pdf.
[172]
Siemens (2017). For a digital twin of the grid Siemens solution enables a single digital grid model of the finnish power system. Available at https://www.siemens.com/press/pool/de/events/2017/corporate/2017-12innovation/inno2017-digitaltwin-e.pdf.
[173]

Xu, B., Wang, J. E., Wang, X. P., Liang, Z. H., Cui, L. M., Liu, X., Ku, A. Y. (2019). A case study of digital-twin-modelling analysis on power-plant-performance optimizations. Clean Energy, 3: 227–234.
DOI Google Scholar
[174]

Jain, P., Poon, J., Singh, J. P., Spanos, C., Sanders, S. R., Panda, S. K. (2020). A digital twin approach for fault diagnosis in distributed photovoltaic systems. IEEE Transactions on Power Electronics, 35: 940–956.
DOI Google Scholar
[175]
Pileggi, P., Verriet, J., Broekhuijsen, J., van Leeuwen, C., Wijbrandi W., Konsman M. (2019). A digital twin for cyber-physical energy systems. In: Proceedings of the 2019 7th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), Montreal, QC, Canada.
[176]

Wang, W. W., Wang, J., Tian, J. P., Lu, J. H., Xiong, R. (2021). Application of digital twin in smart battery management systems. Chinese Journal of Mechanical Engineering, 34: 57.
DOI Google Scholar
[177]
Merkle, L., Segura, A. S., Torben Grummel, J., Lienkamp, M. (2019). Architecture of a digital twin for enabling digital services for battery systems. In: Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems, Taipei, Taiwan, China.
[178]

Wang, Y. J., Xu, R. L., Zhou, C. J., Kang, X., Chen, Z. H. (2022). Digital twin and cloud-side-end collaboration for intelligent battery management system. Journal of Manufacturing Systems, 62: 124–134.
DOI Google Scholar
[179]

Li, W. H., Rentemeister, M., Badeda, J., Jöst, D., Schulte, D., Sauer, D. U. (2020). Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. Journal of Energy Storage, 30: 101557.
DOI Google Scholar
[180]

O’Dwyer, E., Pan, I., Charlesworth, R., Butler, S., Shah, N. (2020). Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems. Sustainable Cities and Society, 62: 102412.
DOI Google Scholar
[181]

Fathy, Y., Jaber, M., Nadeem, Z. (2021). Digital twin-driven decision making and planning for energy consumption. Journal of Sensor and Actuator Networks, 10: 37.
DOI Google Scholar
[182]

Kaewunruen, S., Peng, S. J., Phil-Ebosie, O. (2020). Digital twin aided sustainability and vulnerability audit for subway stations. Sustainability, 12: 7873.
DOI Google Scholar
[183]
Ahmadi, M., Kaleybar, H. J., Brenna, M., Castelli-Dezza, F., Carmeli, M. S. (2021). Adapting digital twin technology in electric railway power systems. In: Proceedings of the 2021 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), Tabriz, Iran.
[184]
Barosan, I., Basmenj, A.A., Chouhan, S.G.R., Manrique, D. (2020). Development of a virtual simulation environment and a digital twin of an autonomous driving truck for a distribution center. In: Muccini, H., Avgeriou, P., Buhnova, B., et al. Eds. Software Architecture. ECSA 2020. Communications in Computer and Information Science. Springer, Cham.
[185]

Liu, Y. K., Wang, Z. R., Han, K., Shou, Z. Y., Tiwari, P., Hansen, J. H. L. (2022). Vision-cloud data fusion for ADAS: A lane change prediction case study. IEEE Transactions on Intelligent Vehicles, 7: 210–220.
DOI Google Scholar
[186]
Veledar, O., Damjanovic-Behrendt, V., Macher, G. (2019). Digital twins for dependability improvement of autonomous driving. In: Walker, A., O'Connor, R., Messnarz, R. Eds. Systems, Software and Services Process Improvement. Springer, Cham.
[187]

Almeaibed, S., Al-Rubaye, S., Tsourdos, A., Avdelidis, N. P. (2021). Digital twin analysis to promote safety and security in autonomous vehicles. IEEE Communications Standards Magazine, 5: 40–46.
Google Scholar
[188]
Bécue, A., Fourastier, Y., Praça, I., Savarit, A., Baron, C., Gradussofs, B., Pouille, E., Thomas, C. (2018). CyberFactory#1—Securing the industry 4.0 with cyber-ranges and digital twins. In: Proceedings of the 2018 14th IEEE International Workshop on Factory Communication Systems, Imperia, Italy.
[189]
Bitton, R., Gluck, T., Stan, O., Inokuchi, M., Ohta, Y., Yamada, Y., Yagyu, T., Elovici, Y., Shabtai, A. (2018). Deriving a cost-effective digital twin of an ICS to facilitate security evaluation. In: Lopez, J., Zhou, J., Soriano, M. Eds. Computer Security. ESORICS 2018. Lecture Notes in Computer Science. Springer, Cham.
[190]

Damjanovic-Behrendt, V. (2018). A digital twin architecture for security, privacy and safety. ERCIM NEWS, 115: 25–26.
Google Scholar
[191]
Yuan, Y. L., Huo, L. W., Hogrefe, D. (2017). Two layers multi-class detection method for network intrusion detection system. In: Proceedings of the 2017 IEEE Symposium on Computers and Communications, Heraklion, Greece.
[192]

Khammassi, C., Krichen, S. (2017). A GA-LR wrapper approach for feature selection in network intrusion detection. Computers & Security, 70: 255–277.
Google Scholar
[193]

Hasan, M. A. M., Nasser, M., Pal, B., Ahmad, S. (2014). Support vector machine and random forest modeling for intrusion detection system (IDS). Journal of Intelligent Learning Systems and Applications, 6: 45–52.
Google Scholar
[194]

Singh, R., Kumar, H., Singla, R. K. (2015). An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Systems with Applications, 42: 8609–8624.
DOI Google Scholar
[195]

Shone, N., Ngoc, T. N., Phai, V. D., Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2: 41–50.
DOI Google Scholar
[196]
Kim, J., Shin, N., Jo, S. Y., Kim, S. H. (2017). Method of intrusion detection using deep neural network. In: Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju.
[197]

Liu, H. Y., Lang, B., Liu, M., Yan, H. B. (2019). CNN and RNN based payload classification methods for attack detection. Knowledge-Based Systems, 163: 332–341.
DOI Google Scholar
[198]

Zhang, X. Q., Yang, F., Hu, Y., Tian, Z., Liu, W., Li, Y. F., She, W. (2022). RANet: Network intrusion detection with group-gating convolutional neural network. Journal of Network and Computer Applications, 198: 103266.
DOI Google Scholar
[199]
Berndt, H., Emmert, J., Dietmayer, K. (2008). Continuous driver intention recognition with hidden Markov models. In: Proceedings of the 2008 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China.
[200]

Kumar, S. A. P., Madhumathi, R., Chelliah, P. R., Tao, L., Wang, S. G. (2018). A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. Journal of Reliable Intelligent Environments, 4: 199–209.
DOI Google Scholar
[201]
Wang, Z. R., Liao, X. S., Zhao, X. P., Han, K., Tiwari, P., Barth, M. J., Wu, G. Y. (2020). A digital twin paradigm: Vehicle-to-cloud based advanced driver assistance systems. 2020 IEEE 91st Vehicular Technology Conference, Antwerp, Belgium.
[202]
Khosroshahi, A., Ohn-Bar, E., Trivedi, M. M. (2016). Surround vehicles trajectory analysis with recurrent neural networks. In: Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil.
[203]

Venkatesan, S., Manickavasagam, K., Tengenkai, N., Vijayalakshmi, N. (2019). Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin. IET Electric Power Applications, 13: 1328–1335.
DOI Google Scholar
[204]

Meng, F., Chowdhury, B., Hossan, M. S. (2019). Optimal integration of DER and SST in active distribution networks. International Journal of Electrical Power & Energy Systems, 104: 626–634.
Google Scholar
[205]

Wu, Y., Wu, Y. P., Guerrero, J. M., Vasquez, J. C. (2021). Digitalization and decentralization driving transactive energy Internet: Key technologies and infrastructures. International Journal of Electrical Power & Energy Systems, 126: 106593.
Google Scholar
[206]

Zheng, Y., Xia, Z., Luo, Y., Chen, Y., Zhou, L., Peng, J. (2021). Operation and maintenance mode selection of poverty alleviation photovoltaic power station based on fuzzy analytic hierarchy process. China Electric Power, 54(6): 8.
Google Scholar
[207]

Devi, S., Neetha, T. (2017). Machine learning based traffic congestion prediction in a IoT based smart city. International Research Journal of Engineering and Technology, 4(5): 3442–3445.
Google Scholar
[208]

Ye, P., Yang, S., Sun, F., Zhang, M. L., Zhang, N. (2021). Research on optimal design and control method of integrated energy system based on improved cloud adaptive particle swarm. E3S Web of Conferences, 257: 02009.
DOI Google Scholar
[209]

Wei, M. J., Yang, Y., Hu, M. J., Wang, Y. L., Tao, S. Y., Zhou, M. H., Ma, Y., Song, F. H. (2020). Optimal scheduling of building integrated energy system based on demand response. E3S Web of Conferences, 185: 01068.
DOI Google Scholar
[210]

Yu, H. F., Zhang, M. X. (2017). Data pricing strategy based on data quality. Computers & Industrial Engineering, 112: 1–10.
Google Scholar
[211]
Fallah, A., Makhdoumi, A., Malekian, A., Ozdaglar, A. (2022). Optimal and differentially private data acquisition: Central and local mechanisms. arXiv preprint: 2201.03968.
[212]

Parra-Arnau, J. (2018). Optimized, direct sale of privacy in personal data marketplaces. Information Sciences, 424: 354–384.
DOI Google Scholar
[213]

Onile, A. E., Machlev, R., Petlenkov, E., Levron, Y., Belikov, J. (2021). Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review. Energy Reports, 7: 997–1015.
DOI Google Scholar
[214]

van Summeren, L. F. M., Wieczorek, A. J., Verbong, G. P. J. (2021). The merits of becoming smart: How Flemish and Dutch energy communities mobilise digital technology to enhance their agency in the energy transition. Energy Research & Social Science, 79: 102160.
Google Scholar
[215]

You, M. L., Wang, Q., Sun, H. J., Castro, I., Jiang, J. (2022). Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, 305: 117899.
DOI Google Scholar
[216]

Ang, B. W., Choong, W. L., Ng, T. S. (2015). Energy security: Definitions, dimensions and indexes. Renewable and Sustainable Energy Reviews, 42: 1077–1093.
DOI Google Scholar
[217]

Lv, Z. H., Kong, W. J., Zhang, X., Jiang, D. D., Lv, H. B., Lu, X. H. (2020). Intelligent security planning for regional distributed energy Internet. IEEE Transactions on Industrial Informatics, 16: 3540–3547.
DOI Google Scholar
[218]

Zhao, Y., Li, T. T., Zhang, X. J., Zhang, C. B. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109: 85–101.
DOI Google Scholar
[219]

Guerrero-Ibáñez, J., Zeadally, S., Contreras-Castillo, J. (2018). Sensor technologies for intelligent transportation systems. Sensors (Basel, Switzerland), 18: E1212.
DOI Google Scholar
[220]
Litman T. (2017). Autonomous Vehicle Implementation Predictions. Victoria, Canada: Victoria Transport Policy Institute.
[221]

Talebpour, A., Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71: 143–163.
DOI Google Scholar
[222]

Levin, M. W., Kockelman, K. M., Boyles, S. D., Li, T. X. (2017). A general framework for modeling shared autonomous vehicles with dynamic networkloading and dynamic ride-sharing application. Computers, Environment and Urban Systems, 64: 373–383.
DOI Google Scholar
[223]

Boesch, P. M., Ciari, F., Axhausen, K. W. (2016). Autonomous vehicle fleet sizes required to serve different levels of demand. Transportation Research Record: Journal of the Transportation Research Board, 2542: 111–119.
DOI Google Scholar
[224]

Bischoff, J., Maciejewski, M. (2016). Simulation of city-wide replacement of private cars with autonomous taxis in berlin. Procedia Computer Science, 83: 237–244.
DOI Google Scholar
[225]

Papadoulis, A., Quddus, M., Imprialou, M. (2019). Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis & Prevention, 124: 12–22.
Google Scholar
[226]
Spieser, K., Treleaven, K., Zhang, R., Frazzoli, E., Morton, D., Pavone, M. (2014). Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in Singapore. In: Meyer, G., Beiker, S. Eds. Road Vehicle Automation. Springer, Cham.
[227]

Fagnant, D. J., Kockelman, K. M. (2018). Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation, 45: 143–158.
DOI Google Scholar
[228]

Akimoto, K., Sano, F., Oda, J. (2022). Impacts of ride and car-sharing associated with fully autonomous cars on global energy consumptions and carbon dioxide emissions. Technological Forecasting and Social Change, 174: 121311.
DOI Google Scholar
[229]

Greenblatt, J. B., Saxena, S. (2015). Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nature Climate Change, 5: 860–863.
DOI Google Scholar
[230]

Greenblatt, J. B., Shaheen, S. (2015). Automated vehicles, on-demand mobility, and environmental impacts. Current Sustainable/Renewable Energy Reports, 2: 74–81.
DOI Google Scholar
[231]

Igliński, H., Babiak, M. (2017). Analysis of the potential of autonomous vehicles in reducing the emissions of greenhouse gases in road transport. Procedia Engineering, 192: 353–358.
DOI Google Scholar
[232]
Tyagi, A.K., Rekha, G., Sreenath, N. (2020). Beyond the hype: Internet of Things concepts, security and privacy concerns. In: Satapathy, S. C., Raju, K. S., Shyamala, K., et al. Eds. Advances in Decision Sciences, Image Processing, Security and Computer Vision. Springer, Cham.
[233]
Krishna, A. M., Tyagi, A. K. (2020). Intrusion detection in intelligent transportation system and its applications using blockchain technology. In: Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India.
[234]

Atlam, H. F., Alenezi, A., Alassafi, M. O., Wills, G. B. (2018). Blockchain with Internet of Things: Benefits, challenges, and future directions. International Journal of Intelligent Systems and Applications, 10: 40–48.
Google Scholar
[235]

Saleem, Y., Crespi, N., Rehmani, M. H., Copeland, R. (2019). Internet of Things-aided smart grid: Technologies, architectures, applications, prototypes, and future research directions. IEEE Access, 7: 62962–63003.
DOI Google Scholar
[236]

Wei, W., Wu, D. M., Wu, Q. W., Shafie-Khah, M., Catalao, J. P. S. (2019). Interdependence between transportation system and power distribution system: A comprehensive review on models and applications. Journal of Modern Power Systems and Clean Energy, 7: 433–448.
DOI Google Scholar
[237]

Qian, T., Shao, C. C., Li, X. L., Wang, X. L., Shahidehpour, M. (2020). Enhanced coordinated operations of electric power and transportation networks via EV charging services. IEEE Transactions on Smart Grid, 11: 3019–3030.
DOI Google Scholar
[238]

Geng, L. J., Lu, Z. G., He, L. C., Zhang, J. F., Li, X. P., Guo, X. Q. (2019). Smart charging management system for electric vehicles in coupled transportation and power distribution systems. Energy, 189: 116275.
DOI Google Scholar
[239]

Dong, Z., Zhao, J., Wen, F., Xue, Y. (2014). From smart grid to energy internet: basic concept and research framework. Automation of electric power systems, 38(15): 1–11.
Google Scholar
[240]

Bao, Z. Y., Xie, C. (2021). Optimal Station locations for en-route charging of electric vehicles in congested intercity networks: A new problem formulation and exact and approximate partitioning algorithms. Transportation Research Part C: Emerging Technologies, 133: 103447.
DOI Google Scholar
[241]

Kchaou-Boujelben, M. (2021). Charging Station location problem: A comprehensive review on models and solution approaches. Transportation Research Part C: Emerging Technologies, 132: 103376.
DOI Google Scholar
[242]

Kavianipour, M., Fakhrmoosavi, F., Singh, H., Ghamami, M., Zockaie, A., Ouyang, Y. F., Jackson, R. (2021). Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior. Transportation Research Part D: Transport and Environment, 93: 102769.
DOI Google Scholar
[243]

Guo, Z. M., Afifah, F., Qi, J. J., Baghali, S. (2021). A stochastic multiagent optimization framework for interdependent transportation and power system analyses. IEEE Transactions on Transportation Electrification, 7: 1088–1098.
DOI Google Scholar
[244]

Yang, T. Y., Guo, Q. L., Xu, L., Sun, H. B. (2021). Dynamic pricing for integrated energy-traffic systems from a cyber-physical-human perspective. Renewable and Sustainable Energy Reviews, 136: 110419.
DOI Google Scholar
[245]

Sun, Y., Chen, Z., Li, Z., Tian, W., Shahidehpour, M. (2018). EV charging schedule in coupled constrained networks of transportation and power system. IEEE Transactions on Smart Grid, 10: 4706–4716.
Google Scholar
[246]

Zhou, Z., Moura, S. J., Zhang, H. C., Zhang, X., Guo, Q. L., Sun, H. B. (2021). Power-traffic network equilibrium incorporating behavioral theory: A potential game perspective. Applied Energy, 289: 116703.
DOI Google Scholar
[247]

Qian, T., Shao, C. C., Wang, X. L., Shahidehpour, M. (2020). Deep reinforcement learning for EV charging navigation by coordinating smart grid and intelligent transportation system. IEEE Transactions on Smart Grid, 11: 1714–1723.
DOI Google Scholar
[248]

Afshar, S., Macedo, P., Mohamed, F., Disfani, V. (2021). Mobile charging stations for electric vehicles—A review. Renewable and Sustainable Energy Reviews, 152: 111654.
DOI Google Scholar
[249]

He, G. N., Michalek, J., Kar, S., Chen, Q. X., da Zhang, Whitacre, J. F. (2021). Utility-scale portable energy storage systems. Joule, 5: 379–392.
DOI Google Scholar
[250]

Khardenavis, A., Hewage, K., Perera, P., Shotorbani, A. M., Sadiq, R. (2021). Mobile energy hub planning for complex urban networks: A robust optimization approach. Energy, 235: 121424.
DOI Google Scholar
[251]

Zhao, Y., Noori, M., Tatari, O. (2017). Boosting the adoption and the reliability of renewable energy sources: Mitigating the large-scale wind power intermittency through vehicle to grid technology. Energy, 120: 608–618.
DOI Google Scholar
[252]

Mehrjerdi, H., Rakhshani, E. (2019). Vehicle-to-grid technology for cost reduction and uncertainty management integrated with solar power. Journal of Cleaner Production, 229: 463–469.
DOI Google Scholar
[253]

Robledo, C. B., Oldenbroek, V., Abbruzzese, F., van Wijk, A. J. M. (2018). Integrating a hydrogen fuel cell electric vehicle with vehicle-to-grid technology, photovoltaic power and a residential building. Applied Energy, 215: 615–629.
DOI Google Scholar
[254]

Geng, Y., Zhao, H. Y., Liu, Z., Xue, B., Fujita, T., Xi, F. M. (2013). Exploring driving factors of energy-related CO2 emissions in Chinese provinces: A case of Liaoning. Energy Policy, 60: 820–826.
DOI Google Scholar
[255]

Gustavsson, L., Joelsson, A., Sathre, R. (2010). Life cycle primary energy use and carbon emission of an eight-storey wood-framed apartment building. Energy and Buildings, 42: 230–242.
DOI Google Scholar
[256]
Elsner, P., Erlach, B., Fischedick, M., Lunz, B., Sauer, U. (2016). Flexibilitätskonzepte für die Stromversorgung 2050: Technologien, Szenarien, Systemzusammenhänge. München: acatech - Deutsche Akademie der Technikwissenschaften e.V. (in German)
[257]

Marmiroli, B., Messagie, M., Dotelli, G., van Mierlo, J. (2018). Electricity generation in LCA of electric vehicles: A review. Applied Sciences, 8: 1384.
DOI Google Scholar
[258]

Vuarnoz, D., Jusselme, T. (2018). Temporal variations in the primary energy use and greenhouse gas emissions of electricity provided by the Swiss grid. Energy, 161: 573–582.
DOI Google Scholar
[259]

Braeuer, F., Finck, R., McKenna, R. (2020). Comparing empirical and model-based approaches for calculating dynamic grid emission factors: An application to CO2-minimizing storage dispatch in Germany. Journal of Cleaner Production, 266: 121588.
DOI Google Scholar
[260]

Tamayao, M. A., Michalek, J. J., Hendrickson, C., Azevedo, I. M. (2015). Regional variability and uncertainty of electric vehicle life cycle CO2 emissions across the United States. Environmental Science & Technology, 49: 8844–8855.
Google Scholar
[261]

Jansen, K. H., Brown, T. M., Samuelsen, G. S. (2010). Emissions impacts of plug-in hybrid electric vehicle deployment on the US western grid. Journal of Power Sources, 195: 5409–5416.
DOI Google Scholar
[262]
UNFCCC (1997). Kyoto Protocol to the United Nations Framework Convention on Climate Change. United Nations Framework Convention on Climate Change (UNFCCC).
[263]

Sun, Y. P., Xue, J. J., Shi, X. P., Wang, K. Y., Qi, S. Z., Wang, L., Wang, C. (2019). A dynamic and continuous allowances allocation methodology for the prevention of carbon leakage: Emission control coefficients. Applied Energy, 236: 220–230.
DOI Google Scholar
[264]

Rogge, K. S., Schneider, M., Hoffmann, V. H. (2011). The innovation impact of the EU Emission Trading System—Findings of company case studies in the German power sector. Ecological Economics, 70: 513–523.
DOI Google Scholar
[265]

Zhang, Y. F., Li, S., Luo, T. Y., Gao, J. (2020). The effect of emission trading policy on carbon emission reduction: Evidence from an integrated study of pilot regions in China. Journal of Cleaner Production, 265: 121843.
DOI Google Scholar
[266]

Sadawi, A. A., Madani, B., Saboor, S., Ndiaye, M., Abu-Lebdeh, G. (2021). A comprehensive hierarchical blockchain system for carbon emission trading utilizing blockchain of things and smart contract. Technological Forecasting and Social Change, 173: 121124.
DOI Google Scholar
[267]

Back, J. A., Tedesco, L. P., Molz, R. F., Nara, E. O. B. (2016). An embedded system approach for energy monitoring and analysis in industrial processes. Energy, 115: 811–819.
DOI Google Scholar
[268]

Ballard, Z., Brown, C., Madni, A. M., Ozcan, A. (2021). Machine learning and computation-enabled intelligent sensor design. Nature Machine Intelligence, 3: 556–565.
DOI Google Scholar
[269]

Yu, D. X., Kang, J. T., Dong, J. L. (2021). Service attack improvement in wireless sensor network based on machine learning. Microprocessors and Microsystems, 80: 103637.
DOI Google Scholar
[270]

Hou, J. C., Wang, C., Luo, S. (2020). How to improve the competiveness of distributed energy resources in China with blockchain technology. Technological Forecasting and Social Change, 151: 119744.
DOI Google Scholar
[271]

Noor, S., Yang, W. T., Guo, M., Dam, K. H. V., Wang, X. N. (2018). Energy demand side management within micro-grid networks enhanced by blockchain. Applied Energy, 228: 1385–1398.
DOI Google Scholar
[272]
Altarawneh, A., Herschberg, T., Medury, S., Kandah, F., Skjellum, A. (2020). Buterin’s scalability trilemma viewed through a state-change-based classification for common consensus algorithms. In: Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.
[273]

Reyna, A., Martín, C., Chen, J., Soler, E., Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems, 88: 173–190.
DOI Google Scholar
[274]

Fuller, A., Fan, Z., Day, C., Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8: 108952–108971.
DOI Google Scholar
[275]

Rasheed, A., San, O., Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8: 21980–22012.
DOI Google Scholar
[276]
Lou, X., Guo, Y., Gao, Y., Waedt, K., Parekh, M. (2019). An idea of using Digital Twin to perform the functional safety and cybersecurity analysis. In: Proceedings of the INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft (Workshop-Beiträge).
[277]

He, Y., Guo, J. C., Zheng, X. L. (2018). From surveillance to digital twin: Challenges and recent advances of signal processing for industrial Internet of Things. IEEE Signal Processing Magazine, 35: 120–129.
DOI Google Scholar
[278]
Protection Regulation. Regulation (eu) 2016/679 of theeuropean parliament and of the council. Regulation (eu),679:2016, 2016.
[279]

Bagga, P., Das, A. K., Wazid, M., Rodrigues, J. J. P. C., Choo, K. K. R., Park, Y. (2021). On the design of mutual authentication and key agreement protocol in Internet of vehicles-enabled intelligent transportation system. IEEE Transactions on Vehicular Technology, 70: 1736–1751.
DOI Google Scholar
[280]
Ning, Z. L., Sun, S. M., Wang, X. J., Guo, L., Guo, S., Hu, X. P., Hu, B., Kwok, R. (2021). Blockchain-enabled intelligent transportation systems: A distributed crowdsensing framework. IEEE Transactions on Mobile Computing, https://doi.org/10.1109/TMC.2021.3079984.
[281]

Dogger, J. D., Roossien, B., Nieuwenhout, F. D. J. (2011). Characterization of Li-ion batteries for intelligent management of distributed grid-connected storage. IEEE Transactions on Energy Conversion, 26: 256–263.
DOI Google Scholar
[282]
Fasugba, M. A., Krein, P. T. (2011). Cost benefits and vehicle-to-grid regulation services of unidirectional charging of electric vehicles. In: Proceedings of the 2011 IEEE Energy Conversion Congress and Exposition, Phoenix, AZ, USA.
[283]

Światowiec-Szczepańska, J., Stępień, B. (2022). Drivers of digitalization in the energy sector—the managerial perspective from the catching up economy. Energies, 15: 1437.
DOI Google Scholar
[284]

Fruhner, D., Klingebiel, K. (2021). Digitization of the car: Impact on automotive logistics. Proceedings of the Hamburg International Conference of Logistics (HICL), 31: 565–583.
Google Scholar
[285]

Au, M. H., Liu, J. K., Fang, J. B., Jiang, Z. L., Susilo, W., Zhou, J. Y. (2014). A new payment system for enhancing location privacy of electric vehicles. IEEE Transactions on Vehicular Technology, 63: 3–18.
DOI Google Scholar
[286]

Almajed, H. N., Almogren, A. S., Altameem, A. (2019). A resilient smart body sensor network through pyramid interconnection. IEEE Access, 7: 51039–51046.
DOI Google Scholar
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Received: 21 July 2022
Revised: 30 September 2022
Accepted: 06 October 2022
Published: 20 September 2022
Issue date: September 2022

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