Open Access
A Study on Analyzing the Security and Privacy Implications of Existing and Emerging Sensors in Autonomous Electric Vehicles
Abstract
The popularity of anonymous consumers’ modern Electric Vehicles (EVs) has developed tremendously. The increasing demand for more environmentally friendly and resource-conserving transit systems has paved the way for the widespread acceptance of EVs. The EV uses the information communication technology to transmit information which ensures the effective transportation. The paper’s analysis includes security issues regarding the Internet of vehicles, EV charging, sensors, anomaly detection, and vulnerable cyber-attacks. The analysis uses a wide range of datasets with different observations and statistical results and reports the greatest risk of privacy issues occurring in EVs communication. The major issues of security and privacy of autonomous electric vehicles within ecological settings are discussed, as well as a wide range of security risks, approaches, countermeasures, and solutions to handle them.
Keywords
References
[1]
T. Mo, Y. Li, K. T. Lau, C. K. Poon, Y. Wu, and Y. Luo, Trends and emerging technologies for the development of electric vehicles, Energies, vol. 15, no. 17, p. 6271, 2022.
[2]
Z. Teimoori and A. Yassine, A review on intelligent energy management systems for future electric vehicle transportation, Sustainability, vol. 14, no. 21, p. 14100, 2022.
[3]
A. R. Sani, M. U. Hassan, and J. Chen, Privacy preserving machine learning for electric vehicles: A survey, arXiv preprint arXiv: 2205.08462, 2022.
[4]
Z. Garofalaki, D. Kosmanos, S. Moschoyiannis, D. Kallergis, and C. Douligeris, Electric vehicle charging: A survey on the security issues and challenges of the open charge point protocol (OCPP), IEEE Commun. Surveys Tutorials, vol. 24, no. 3, pp. 1504–1533, 2022.
[5]
P. R. Babu, A. G. Reddy, B. Palaniswamy, and S. K. Kommuri, EV-Auth: Lightweight authentication protocol suite for dynamic charging system of electric vehicles with seamless handover, IEEE Trans. Intell. Veh., vol. 7, no. 3, pp. 734–747, 2022.
[6]
A. Alshaeri and M. Younis, Lightweight authentication and authorization protocol for dynamic charging of electric vehicles, in Proc. 2022 IEEE 19th Annu. Consumer Communications & Networking Conf. (CCNC), Las Vegas, NV, USA, 2022, pp. 550–556.
[7]
S. Kahveci, B. Alkan, M. H. Ahmad, B. Ahmad, and R. Harrison, An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles, J. Manuf. Syst., vol. 63, pp. 214–223, 2022.
[8]
K. Hassan, F. Dakalbab, M. A. Talib, C. Ghenai, Q. Nasir, and M. Bettayeb, Blockchain networks for solar PV electric vehicles charging station to support and foster clean energy transition, in Proc. 2022 Int. Conf. Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, 2022, pp. 1–7.
[9]
K. Y. Yap, H. H. Chin, and J. J. Klemeš, Future outlook on 6G technology for renewable energy sources (RES), Renew. Sust. Energy Rev., vol. 167, p. 112722, 2022.
[10]
T. Alam and R. Gupta, Federated learning and its role in the privacy preservation of IoT devices, Future Internet, vol. 14, no. 9, p. 246, 2022.
[11]
G. Huang and Y. Yu, The application of artificial intelligence in organizational innovation management: Take the autonomous driving technology of tesla as an example, in Proc. Int. Conf. Artificial Intelligence and Logistics Engineering, Kiev, Ukraine, 2022, pp. 690–697.
[12]
J. L. Pisarov and G. Mester, Self-driving robotic cars: Cyber security developments, in Research Anthology on Cross-Disciplinary Designs and Applications of Automation, Information Resources Management Association, ed. New York, NY, USA: IGI Global Scientific Publishing, 2022, pp. 969–1001
[13]
K. Sevdari, L. Calearo, P. B. Andersen, and M. Marinelli, Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives, Renew. Sust. Energy Rev., vol. 167, p. 112666, 2022.
[14]
X. Tang, Ecological driving on multiphase trajectories and multiobjective optimization for autonomous electric vehicle platoon, Sci. Rep., vol. 12, no. 1, p. 5209, 2022.
[15]
P. Dixit, P. Bhattacharya, S. Tanwar, and R. Gupta, Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey, Expert Syst., vol. 39, no. 5, p. e12754, 2022.
[16]
K. N. Qureshi, L. Shahzad, A. Abdelmaboud, T. A. E. Eisa, B. Alamri, I. T. Javed, A. Al-Dhaqm, and N. Crespi, A blockchain-based efficient, secure and anonymous conditional privacy-preserving and authentication scheme for the Internet of vehicles, Appl. Sci., vol. 12, no. 1, p. 476, 2022.
[17]
L. Benarous and B. Kadri, Obfuscation-based location privacy-preserving scheme in cloud-enabled Internet of vehicles, Peer-to-Peer Netw. Appl., vol. 15, no. 1, pp. 461–472, 2022.
[18]
M. S. Rathore, M. Poongodi, P. Saurabh, U. K. Lilhore, S. Bourouis, W. Alhakami, J. Osamor, and M. Hamdi, A novel trust-based security and privacy model for Internet of Vehicles using encryption and steganography, Comput. Electr. Eng., vol. 102, p. 108205, 2022.
[19]
D. S. Gupta, A. Karati, W. Saad, and D. B. da Costa, Quantum-defended blockchain-assisted data authentication protocol for Internet of vehicles, IEEE Trans. Veh. Technol., vol. 71, no. 3, pp. 3255–3266, 2022.
[20]
Y. Duan, J. Liu, W. Jin, and X. Peng, Characterizing differentially-private techniques in the era of Internet-of-vehicles, arXiv preprint arXiv: 2202.06477, 2022.
[21]
U. Aashma, D. B. Rawat, and J. Li, Privacy preserving misbehavior detection in IoV using federated machine learning, in Proc. 2021 IEEE 18 th Annu. Consumer Communications & Networking Conf. (CCNC), Las Vegas, NV, USA, 2021, pp. 1–6.
[22]
M. S. Eddine, M. A. Ferrag, O. Friha, and L. Maglaras, EASBF: An efficient authentication scheme over blockchain for fog computing-enabled Internet of vehicles, J. Inf. Secur. Appl., vol. 59, p. 102802, 2021.
[23]
M. Zhang, J. Zhou, G. Zhang, M. Zou, and M. Chen, EC-BAAS: Elliptic curve-based batch anonymous authentication scheme for Internet of vehicles, J. Syst. Archit., vol. 117, p. 102161, 2021.
[24]
K. Fan, W. Jiang, Q. Luo, H. Li, and Y. Yang, Cloud-based RFID mutual authentication scheme for efficient privacy preserving in IoV, J. Franklin Inst., vol. 358, no. 1, pp. 193–209, 2021.
[25]
Z. Li, Q. Miao, S. A. Chaudhry, and C. M. Chen, A provably secure and lightweight mutual authentication protocol in fog-enabled social Internet of vehicles, Int. J. Distrib. Sens. Netw., vol. 18, no. 6, pp. 1–16, 2022.
[26]
H. Y. Lin and M. Y. Hsieh, A dynamic key management and secure data transfer based on m-tree structure with multi-level security framework for Internet of vehicles, Connect. Sci., vol. 34, no. 1, pp. 1089–1118, 2022.
[27]
A. M. Almuhaideb and S. S. Algothami, Efficient privacy-preserving and secure authentication for electric-vehicle-to-electric-vehicle-charging system based on ECQV, J. Sens. Actuator Netw., vol. 11, no. 2, p. 28, 2022.
[28]
H. Yi, A secure blockchain system for Internet of Vehicles based on 6G-enabled Network in Box, Comput. Commun., vol. 186, pp. 45–50, 2022.
[29]
M. Gupta, R. Kumar, S. Shekhar, B. Sharma, R. B. Patel, S. Jain, I. B. Dhaou, and C. Iwendi, Game theory-based authentication framework to secure Internet of vehicles with blockchain, Sensors, vol. 22, no. 14, p. 5119, 2022.
[30]
P. W. Shaikh and H. T. Mouftah, Intelligent charging infrastructure design for connected and autonomous electric vehicles in smart cities, in Proc. 2021 IFIP/IEEE Int. Symp. Integrated Network Management (IM), Bordeaux, France, 2021, pp. 992–997.
[31]
Y. Yuan, Y. Yuan, P. Memarmossrci, T. Baker, and D. Hogrefe, LbSP: Load-balanced secure and private autonomous electric vehicle charging framework with online price optimization, IEEE Internet Things J., vol. 9, no. 17, pp. 15685–15696, 2022.
[32]
K. N. Qureshi, A. Alhudhaif, and G. Jeon, Electric-vehicle energy management and charging scheduling system in sustainable cities and society, Sustain. Cities Soc., vol. 71, p. 102990, 2021.
[33]
J. Xiong, R. Bi, Y. Tian, X. Liu, and D. Wu, Toward lightweight, privacy-preserving cooperative object classification for connected autonomous vehicles, IEEE Internet Things J., vol. 9, no. 4, pp. 2787–2801, 2022.
[34]
H. Chen, S. Chen, and S. Jiang, Optimal game-based energy management with renewable energy for secure electric vehicles charging in Internet of Things, Math. Probl. Eng., vol. 2021, p. 6078149, 2021.
[35]
P. W. Shaikh and H. T. Mouftah, Connected and autonomous electric vehicles charging reservation and trip planning system, in Proc. 2021 Int. Wireless Communications and Mobile Computing (IWCMC), Harbin, China, 2021, pp. 1135–1140.
[36]
K. Dev, Y. Xiao, T. R. Gadekallu, J. M. Corchado, G. Han, and M. Magarini, Guest editorial special issue on green communication and networking for connected and autonomous vehicles, IEEE Trans. Green Commun. Netw., vol. 6, no. 3, pp. 1260–1266, 2022.
[37]
N. Kamble, R. Gala, R. Vijayaraghavan, E. Shukla, and D. Patel, Using blockchain in autonomous vehicles, in Artificial Intelligence and Blockchain for Future Cybersecurity Applications, Y. Maleh, Y. Baddi, M. Alazab, L. Tawalbeh, and I. Romdhani, eds. Cham, Switzerland: Springer, 2021, pp. 285–305.
[38]
M. Dorokhova, J. Vianin, J. M. Alder, C. Ballif, N. Wyrsch, and D. Wannier, A blockchain-supported framework for charging management of electric vehicles, Energies, vol. 14, no. 21, p. 7144, 2021.
[39]
Z. Teimoori, A. Yassine, and M. S. Hossain, A secure cloudlet-based charging station recommendation for electric vehicles empowered by federated learning, IEEE Trans. Ind. Inf., vol. 18, no. 9, pp. 6464–6473, 2022.
[40]
Y. Wang, Q. Liu, E. Mihankhah, C. Lv, and D. Wang, Detection and isolation of sensor attacks for autonomous vehicles: Framework, algorithms, and validation, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 8247–8259, 2022.
[41]
D. Reebadiya, T. Rathod, R. Gupta, S. Tanwar, and N. Kumar, Blockchain-based secure and intelligent sensing scheme for autonomous vehicles activity tracking beyond 5G networks, Peer-to-Peer Netw. Appl., vol. 14, no. 5, pp. 2757–2774, 2021.
[42]
F. A. Butt, J. N. Chattha, J. Ahmad, M. U. Zia, M. Rizwan, and I. H. Naqvi, On the integration of enabling wireless technologies and sensor fusion for next-generation connected and autonomous vehicles, IEEE Access, vol. 10, pp. 14643–14668, 2022.
[43]
A. Barzegar, O. Doukhi, and D. J. Lee, Design and implementation of an autonomous electric vehicle for self-driving control under GNSS-denied environments, Appl. Sci., vol. 11, no. 8, p. 3688, 2021.
[44]
S. Ai, J. Song, G. Cai, and K. Zhao, Active fault-tolerant control for quadrotor UAV against sensor fault diagnosed by the auto sequential random forest, Aerospace, vol. 9, no. 9, p. 518, 2022.
[45]
L. Barbieri, S. Savazzi, M. Brambilla, and M. Nicoli, Decentralized federated learning for extended sensing in 6G connected vehicles, Veh. Commun., vol. 33, p. 100396, 2022.
[46]
J. Jia, J. Chen, G. Chang, and Z. Tan, Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm, Comput. Math. Appl., vol. 57, no. 11–12, pp. 1756–1766, 2009.
[47]
Y. Li, C. Ai, Z. Cai, and R. Beyah, Sensor scheduling for p-percent coverage in wireless sensor networks, Cluster Comput., vol. 14, no. 1, pp. 27–40, 2011.
[48]
X. Liu and D. He, Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks, J. Netw. Comput. Appl., vol. 39, pp. 310–318, 2014.
[49]
H. P. Gupta and S. V. Rao, Demand-based coverage and connectivity-preserving routing in wireless sensor networks, IEEE Syst. J., vol. 10, no. 4, pp. 1380–1389, 2016.
[50]
C. P. Chen, S. C. Mukhopadhyay, C. L. Chuang, M. Y. Liu, and J. A. Jiang, Efficient coverage and connectivity preservation with load balance for wireless sensor networks, IEEE Sens. J., vol. 15, no. 1, pp. 48–62, 2015.
[51]
M. Movassagh and H. S. Aghdasi, Game theory based node scheduling as a distributed solution for coverage control in wireless sensor networks, Eng. Appl. Artif. Intell., vol. 65, pp. 137–146, 2017.
[52]
D. Arivudainambi and S. Balaji, Improved memetic algorithm for energy efficient sensor scheduling with adjustable sensing range, Wireless Pers. Commun., vol. 95, no. 2, pp. 1737–1758, 2017.
[53]
D. Ye and M. Zhang, A self-adaptive sleep/wake-up scheduling approach for wireless sensor networks, IEEE Trans. Cybern., vol. 48, no. 3, pp. 979–992, 2018.
[54]
R. Wan, N. Xiong, and N. T. Loc, An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks, Human-Centric Comput. Inf. Sci., vol. 8, no. 1, p. 18, 2018.
[55]
B. Mostafa, A. Benslimane, M. Saleh, S. Kassem, and M. Molnar, An energy-efficient multiobjective scheduling model for monitoring in Internet of Things, IEEE Internet Things J., vol. 5, no. 3, pp. 1727–1738, 2018.
[56]
H. T. T. Binh, N. T. Hanh, L. Van Quan, and N. Dey, Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks, Neural Comput. Appl., vol. 30, no. 7, pp. 2305–2317, 2018.
[57]
Y. Xu, O. Ding, R. Qu, and K. Li, Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization, Appl. Soft Comput., vol. 68, pp. 268–282, 2018.
[58]
T. S. Panag and J. S. Dhillon, A novel random transition based PSO algorithm to maximize the lifetime of wireless sensor networks, Wireless Pers. Commun., vol. 98, no. 2, pp. 2261–2290, 2018
[59]
G. P. Gupta and S. Jha, Biogeography-based optimization scheme for solving the coverage and connected node placement problem for wireless sensor networks, Wireless Netw., vol. 25, no. 6, pp. 3167–3177, 2019.
[60]
S. Harizan and P. Kuila, Coverage and connectivity aware energy efficient scheduling in target based wireless sensor networks: An improved genetic algorithm based approach, Wireless Netw., vol. 25, no. 4, pp. 1995–2011, 2019.
[61]
A. Sharma and S. Chauhan, A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network, Wireless Netw., vol. 26, no. 6, pp. 4411–4429, 2020.
[62]
Y. Ji and H. Lee, Event-based anomaly detection using a one-class SVM for a hybrid electric vehicle, IEEE Trans. Veh. Technol., vol. 71, no. 6, pp. 6032–6043, 2022.
[63]
M. Bozdal, M. Samie, and I. Jennions, A survey on can bus protocol: Attacks, challenges, and potential solutions, in Proc. 2018 Int. Conf. Computing, Electronics & Communications Engineering (iCCECE), Southend, UK, 2018, pp. 201–205.
[64]
A. Kavousi-Fard, M. Dabbaghjamanesh, T. Jin, W. Su, and M. Roustaei, An evolutionary deep learning-based anomaly detection model for securing vehicles, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4478–4486, 2021.
[65]
S. El Motaki and B. Hirchoua, An improved anomaly detection process in smart vehicle using a supervised ensemble-learning framework, in Artificial Intelligence of Things in Smart Environments, M. Ouaissa, Z. Boulouard, M. Ouaissa, and Y. Maleh, eds. Boston, MA, USA: De Gruyter, 2022, pp. 83–100.
[66]
X. Han, Y. Zhou, K. Chen, H. Qiu, M. Qiu, Y. Liu, and T. Zhang, ADS-Lead: Lifelong anomaly detection in autonomous driving systems, IEEE Trans. Intell. Transp. Syst., vol. 24, no. 1, pp. 1039–1051, 2023.
[67]
A. Khan, N. Moustafa, D. Pi, W. Haider, B. Li, and A. Jolfaei, An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 12, pp. 25469–25478, 2022.
[68]
B. Yang and J. Ye, Data-driven detection of physical faults and cyber attacks in dual-motor EV powertrains, in Proc. 2022 IEEE Transportation Electrification Conf. & Expo (ITEC), Anaheim, CA, USA, 2022, pp. 991–996.
[69]
A. Algarni and V. Thayananthan, Autonomous vehicles: The cybersecurity vulnerabilities and countermeasures for big data communication, Symmetry, vol. 14, no. 12, p. 2494, 2022.
[70]
Wu, S. Weng, and Z. Sun, Resilient event-triggered distributed electric vehicles charging scheduling against cyber attacks, in Proc. 2022 41st Chinese Control Conf. (CCC), Hefei, China, 2022, pp. 4543–4550.
[71]
Mihály, B. Németh, and P. Gáspár, Safe eco-cruise control for connected vehicles against trained cyber attacks, IFAC-PapersOnLine, vol. 55, no. 14, pp. 1–6, 2022.
[72]
Zhao, J. Li, and Y. Yang, Joint mobile energy replenishment and data gathering in wireless rechargeable sensor networks, in Proc. 2011 23rd Int. Teletraffic Congress (ITC), San Francisco, CA, USA, pp. 238–245, 2011.
[73]
L. He, L. Kong, Y. Gu, J. Pan, and T. Zhu, Evaluating the on-demand mobile charging in wireless sensor networks, IEEE Trans. Mobile Comput., vol. 14, no. 9, pp. 1861–1875, 2015.
[74]
C. Wang, J. Li, F. Ye, and Y. Yang, A mobile data gathering framework for wireless rechargeable sensor networks with vehicle movement costs and capacity constraints, IEEE Trans. Comput., vol. 65, no. 8, pp. 2411–2427, 2016.
[75]
X. Ye and W. Liang, Charging utility maximization in wireless rechargeable sensor networks, Wireless Netw., vol. 23, no. 7, pp. 2069–2081, 2017.
[76]
G. Jiang, S. K. Lam, Y. Sun, L. Tu, and J. Wu, Joint charging tour planning and depot positioning for wireless sensor networks using mobile chargers, IEEE/ACM Trans. Netw., vol. 25, no. 4, pp. 2250–2266, 2017.
[77]
A. Kaswan, A. Tomar, and P. K. Jana, An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks, J. Netw. Comput. Appl., vol. 114, pp. 123–134, 2018.
[78]
G. Han, X. Yang, L. Liu, S. Chan, and W. Zhang, A coverage-aware hierarchical charging algorithm in wireless rechargeable sensor networks, IEEE Netw., vol. 33, no. 4, pp. 201–207, 2019.
[79]
H. Dai, Q. Ma, X. Wu, G. Chen, D. K. Y. Yau, S. Tang, X. Y. Li, and C. Tian, CHASE: Charging and scheduling scheme for stochastic event capture in wireless rechargeable sensor networks, IEEE Trans. Mobile Comput., vol. 19, no. 1, pp. 44–59, 2020.
[80]
Z. Lyu, Z. Wei, J. Pan, H. Chen, C. Xia, J. Han, and L. Shi, Periodic charging planning for a mobile WCE in wireless rechargeable sensor networks based on hybrid PSO and GA algorithm, Appl. Soft Comput., vol. 75, pp. 388–403, 2019.
[81]
Liu, J. Peng, L. He, J. Pan, S. Li, M. Ling, and Z. Huang, An active mobile charging and data collection scheme for clustered sensor networks, IEEE Trans. Veh. Technol., vol. 68, no. 5, pp. 5100–5113, 2019.
[82]
Z. Wei, M. Li, Q. Zhao, Z. Lyu, S. Zhu, and Z. Wei, Multi-MC charging schedule algorithm with time windows in wireless rechargeable sensor networks, IEEE Access, vol. 7, pp. 156217–156227, 2019.
[83]
Rahman, X. Gao, J. Xie, I. Alvarez-Fernandez, H. Haggi, and W. Sun, Challenges and opportunities in cyber-physical security of highly DER-penetrated power systems, in Proc. 2022 IEEE Power & Energy Society General Meeting (PESGM), Denver, CO, USA, 2022, pp. 1–5.
[84]
A. O. Khadidos, A. M. Alshareef, H. Manoharan, A. O. Khadidos, and S. Selvarajan, Application of improved support vector machine for pulmonary syndrome exposure with computer vision measures, Curr. Bioinform., vol. 19, no. 3, pp. 281–293, 2024.
[85]
K. Al-Ani, S. U. A. Laghari, H. Manoharan, S. Selvarajan, and M. Uddin, Improved transportation model with Internet of Things using artificial intelligence algorithm, Comput., Mater. Continua, vol. 76, no. 2, pp. 2261–2279, 2023.
[86]
S. S, H. Manoharan, A. M. Alshareef, A. Yafoz, H. Alkhiri, and O. M. Mirza, Hyper spectral image classifications for monitoring harvests in agriculture using fly optimization algorithm, Comput. Electr. Eng., vol. 103, p. 108400, 2022.
[87]
S. Selvarajan, H. Manoharan, C. Iwendi, R. A. Alsowail, and S. Pandiaraj, A comparative recognition research on excretory organism in medical applications using artificial neural networks, Front. Bioeng. Biotechnol., vol. 11, p. 1211143, 2023.
[88]
A. O. Khadidos, S. Shitharth, H. Manoharan, A. Yafoz, A. O. Khadidos, and K. H. Alyoubi, An intelligent security framework based on collaborative mutual authentication model for smart city networks, IEEE Access, vol. 10, pp. 85289–85304, 2022.
[89]
S. Shitharth, S. Yonbawi, H. Manoharan, S. Alahmari, A. Yafoz, and H. Mujlid, Physical stint virtual representation of biomedical signals with wireless sensors using swarm intelligence optimization algorithm, IEEE Sens. J., vol. 23, no. 4, pp. 3870–3877, 2023.
[90]
S. Shitharth, H. Manoharan, R. A. Alsowail, A. Shankar, S. Pandiaraj, C. Maple, and G. Jeon, Development of edge computing and classification using the Internet of Things with incremental learning for object detection, Internet Things, vol. 23, p. 100852, 2023.
[91]
Chandrakasan, M. Subramanian, H. Manoharan, S. Selvarajan, and R. Aluvalu, Original research article Future transportation computing model with trifold algorithm for real-time multipath networks, J. Auton. Intell., vol. 6, no. 2, pp. 1–18, 2023.
[92]
H. Manoharan, S. Shitharth, K. Sangeetha, B. P. Kumar, and M. Hedabou, Detection of superfluous in channels using data fusion with wireless sensors and fuzzy interface algorithm, Meas. Sens., vol. 23, p. 100405, 2022.
[93]
S. Shitharth, S. Yonbawi, H. Manoharan, A. Shankar, C. Maple, and S. Alahmari, Secured data transmissions in corporeal unmanned device to device using machine learning algorithm, Phys. Commun., vol. 59, p. 102116, 2023.
Pages 1401-1420
Cite this article:
Shankar A, Maple C, Epiphaniou G. A Study on Analyzing the Security and Privacy Implications of Existing and Emerging Sensors in Autonomous Electric Vehicles. Tsinghua Science and Technology, 2025, 30(4): 1401-1420. https://doi.org/10.26599/TST.2023.9010132
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