Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges

Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions.

[1]  Cheng Huang,et al.  TripSense: A Trust-Based Vehicular Platoon Crowdsensing Scheme with Privacy Preservation in VANETs , 2016, Sensors.

[2]  Kim-Kwang Raymond Choo,et al.  Forensic-by-Design Framework for Cyber-Physical Cloud Systems , 2016, IEEE Cloud Computing.

[3]  Ali A. Ghorbani,et al.  A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT , 2017, IEEE Access.

[4]  Cheng Huang,et al.  An Efficient Privacy-Preserving Location-Based Services Query Scheme in Outsourced Cloud , 2016, IEEE Transactions on Vehicular Technology.

[5]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[6]  Cristian Borcea,et al.  DIVERT: A Distributed Vehicular Traffic Re-Routing System for Congestion Avoidance , 2017, IEEE Transactions on Mobile Computing.

[7]  Fei-Yue Wang,et al.  A Security and Privacy Review of VANETs , 2015, IEEE Transactions on Intelligent Transportation Systems.

[8]  Xiaodong Lin,et al.  A Lightweight Conditional Privacy-Preservation Protocol for Vehicular Traffic-Monitoring Systems , 2013, IEEE Intelligent Systems.

[9]  Xuemin Shen,et al.  GLARM: Group-based lightweight authentication scheme for resource-constrained machine to machine communications , 2016, Comput. Networks.

[10]  Saurabh Amin,et al.  Vulnerability of Transportation Networks to Traffic-Signal Tampering , 2016, 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS).

[11]  Azzedine Boukerche,et al.  Intelligent Traffic Light Controlling Algorithms Using Vehicular Networks , 2016, IEEE Transactions on Vehicular Technology.