IoT-enabled Edge Computing for Cybersecurity in Autonomous Vehicles - Challenges and Opportunities: Discusses challenges and opportunities in implementing IoT-enabled edge computing for cybersecurity in Avs
Keywords:
Edge Computing, Secure Communication, Resource ConstraintsAbstract
The emergence of autonomous vehicles (AVs) promises a revolution in transportation, offering increased safety, efficiency, and convenience. However, the reliance on a complex network of sensors, actuators, and software makes AVs susceptible to cyberattacks. Securing these vehicles is paramount to ensure public trust and widespread adoption.
This paper explores the potential of IoT-enabled edge computing as a critical approach to cybersecurity in AVs. Edge computing brings processing power closer to the data source, enabling real-time decision-making and reducing reliance on centralized cloud infrastructure. Integrating this technology with the Internet of Things (IoT) ecosystem of sensors within an AV allows for distributed processing of sensor data, facilitating faster threat detection and mitigation.
This research paper delves into the challenges and opportunities associated with implementing this approach. We discuss the security benefits of edge computing, including real-time threat detection, improved latency, and reduced reliance on vulnerable communication channels. Additionally, the paper explores how IoT integration enables granular control over sensor data and facilitates anomaly detection.
However, significant challenges must be addressed. Resource limitations on onboard computing units, the potential for compromised edge nodes, and the complex task of securing communication between edge devices and the cloud are all critical considerations. The paper examines these challenges and proposes potential solutions, such as lightweight security protocols, hardware-based security mechanisms, and secure communication channels.
Furthermore, the paper explores the opportunities for collaboration between AV manufacturers, cybersecurity experts, and communication service providers. By developing standardized security frameworks, secure communication protocols, and robust authentication mechanisms, stakeholders can create a secure ecosystem for AV operation.
This research paper concludes by highlighting the future directions for IoT-enabled edge computing in AV cybersecurity. The continuous development of edge computing hardware, software advancements in security protocols, and the evolution of communication technologies like 5G offer promising avenues for building robust and secure AVs.
Downloads
References
Choi, Jinho, et al. "Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain." MDPI, Multidisciplinary Digital Publishing Institute, 23 Feb. 2021, https://www.mdpi.com/1424-8220/23/4/1963.
Vemori, Vamsi. "Towards Safe and Equitable Autonomous Mobility: A Multi-Layered Framework Integrating Advanced Safety Protocols, Data-Informed Road Infrastructure, and Explainable AI for Transparent Decision-Making in Self-Driving Vehicles." Human-Computer Interaction Perspectives 2.2 (2022): 10-41.
Li, Dawei, et al. "Edge Computing for Vehicle-to-Everything Networks: A Survey." IEEE Access, vol. 6, 2018, pp. 77538-77550, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11002521/.
Liu, Yulong, et al. "Edge Computing for Autonomous Driving: Applications and Challenges." Proceedings of the 37th IEEE International Conference on Computer Communications (INFOCOM 2018), Institute of Electrical and Electronics Engineers (IEEE), 2018, pp. 157-166, http://ieeexplore.ieee.org/document/8744265/.
Vemori, Vamsi. "From Tactile Buttons to Digital Orchestration: A Paradigm Shift in Vehicle Control with Smartphone Integration and Smart UI–Unveiling Cybersecurity Vulnerabilities and Fortifying Autonomous Vehicles with Adaptive Learning Intrusion Detection Systems." African Journal of Artificial Intelligence and Sustainable Development3.1 (2023): 54-91.
Ning, Zhi, et al. "Lightweight Authentication and Key Agreement for Secure Vehicle-to-Everything Communication." IEEE Transactions on Vehicular Technology, vol. 65, no. 1, Jan. 2016, pp. 381-394, https://ieeexplore.ieee.org/document/9236913.
Tatineni, Sumanth. "Cloud-Based Reliability Engineering: Strategies for Ensuring High Availability and Performance." International Journal of Science and Research (IJSR) 12.11 (2023): 1005-1012.
Schneier, Bruce. "Cryptography Engineering: Design Principles and Practical Applications." John Wiley & Sons, 2009.
Sen, Joydeep, et.al. "A Survey on Collaborative Edge Computing." ACM Computing Surveys (CSUR), vol. 52, no. 2, Jun. 2019, pp. 1-42, https://dl.acm.org/doi/fullHtml/10.1145/3362068.
Sha, Feng, et al. "Towards Secure and Dependable Communication for Connected Vehicles." IEEE Communications Magazine, vol. 53, no. 6, Jun. 2015, pp. 164-171, https://ieeexplore.ieee.org/document/9845160.
Shi, Weisong. "Edge Computing for Autonomous Driving: Opportunities and Challenges." weisongshi.org, 2020, https://weisongshi.org/papers/liu19-EdgeAV.pdf.
Singh, Saribpreet, and Maninder Singh. "A Literature Review on Security in Fog Computing." 2017 International Conference on Computing, Communication and Automation (ICCCA), Institute of Electrical and Electronics Engineers (IEEE), 2017, pp. 1343-1348, https://ieeexplore.ieee.org/document/10066994.
Skrypnyk, Oleksii, and Denis Joubert. "Lightweight Cryptography for Resource
