R-CAV: On-Demand Edge Computing Platform for Connected Autonomous Vehicles

Author(s):  
Mohammad Aminul Hoque ◽  
Raiful Hasan ◽  
Ragib Hasan
2020 ◽  
Vol 4 (4) ◽  
pp. 34-41
Author(s):  
Fabrizio Granelli ◽  
Cristina Costa ◽  
Jiajing Zhang ◽  
Riccardo Bassoli ◽  
Frank H.P. Fitzek

Author(s):  
Ashish Joglekar ◽  
Gurunath Gurrala ◽  
Puneet Kumar ◽  
Francis C Joseph ◽  
Kiran T S ◽  
...  

Author(s):  
Jo Yoshimoto ◽  
Ittetsu Taniguchi ◽  
Hiroyuki Tomiyama ◽  
Takao Onoye

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1220
Author(s):  
Chee Wei Lee ◽  
Stuart Madnick

Urban mobility is in the midst of a revolution, driven by the convergence of technologies such as artificial intelligence, on-demand ride services, and Internet-connected and self-driving vehicles. Technological advancements often lead to new hazards. Coupled with the increased levels of automation and connectivity in the new generation of autonomous vehicles, cybersecurity is emerging as a key threat affecting these vehicles. Traditional hazard analysis methods treat safety and security in isolation and are limited in their ability to account for interactions among organizational, sociotechnical, human, and technical components. In response to these challenges, the cybersafety method, based on System Theoretic Process Analysis (STPA and STPA-Sec), was developed to meet the growing need to holistically analyze complex sociotechnical systems. We applied cybersafety to coanalyze safety and security hazards, as well as identify mitigation requirements. The results were compared with another promising method known as Combined Harm Analysis of Safety and Security for Information Systems (CHASSIS). Both methods were applied to the Mobility-as-a-Service (MaaS) and Internet of Vehicles (IoV) use cases, focusing on over-the-air software updates feature. Overall, cybersafety identified additional hazards and more effective requirements compared to CHASSIS. In particular, cybersafety demonstrated the ability to identify hazards due to unsafe/unsecure interactions among sociotechnical components. This research also suggested using CHASSIS methods for information lifecycle analysis to complement and generate additional considerations for cybersafety. Finally, results from both methods were backtested against a past cyber hack on a vehicular system, and we found that recommendations from cybersafety were likely to mitigate the risks of the incident.


2022 ◽  
Vol 54 (9) ◽  
pp. 1-37
Author(s):  
Pasika Ranaweera ◽  
Anca Jurcut ◽  
Madhusanka Liyanage

The future of mobile and internet technologies are manifesting advancements beyond the existing scope of science. The concepts of automated driving, augmented-reality, and machine-type-communication are quite sophisticated and require an elevation of the current mobile infrastructure for launching. The fifth-generation (5G) mobile technology serves as the solution, though it lacks a proximate networking infrastructure to satisfy the service guarantees. Multi-access Edge Computing (MEC) envisages such an edge computing platform. In this survey, we are revealing security vulnerabilities of key 5G-based use cases deployed in the MEC context. Probable security flows of each case are specified, while countermeasures are proposed for mitigating them.


Author(s):  
Liuyan Liu ◽  
Haoqiang Huang ◽  
Haisheng Tan ◽  
Wanli Cao ◽  
Panlong Yang ◽  
...  

Author(s):  
Luiz Angelo Steffenel ◽  
Manuele Kirsch Pinheiro ◽  
Lucas Vaz Peres ◽  
Damaris Kirsch Pinheiro

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.


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