Abstract
Connected Vehicles Rely on Sophisticated Software Systems for Diverse Features, Including Navigation, Entertainment, Communication, and Safety Functions. as Technology Continues to Advance, the Reliance on Software in Connected Vehicles Becomes Increasingly Integral to their overall Performance and the Delivery of Innovative Features. Therefore, in the Domain of Software-Enabled Automobiles, the Implementation of over-The-Air (OTA) Software Updates is Deemed Essential for the Dissemination of Software and Fixes in Connected Vehicles. the Conventional Method of Addressing This Matter Entailed Manufacturers Undertaking the Task of Recalling Outdated Vehicles; However, the Central Issue Lies in the Considerable Challenge of Effectively Notifying Owners through Recall Notices. This Process Gets Further Complicated by the Presence of Organizational and Procedural Obstacles, Ultimately Culminating in a Significant Number of Vehicles Operating on Insecure and Unstable Software. in This Paper, We Present a Multi-Tier Software Update Dissemination Framework that Systematically Identifies Appropriate Fog Nodes based on Traffic Density to Optimize the Convergence of Updates. Furthermore, at the Fog Node, We Advocate for a Multi-Agent Labeling Approach to Identify Pivotal Vehicles Capable of Efficiently Streaming Received Software Updates to Other Vehicles. the Updates Are Transmitted from the Cloud to the Fog Node, Which Subsequently Relays Them through the Labeled Vehicles to Reach Other Vehicles. Harnessing a Fog Distribution Framework with Multi-Pivot Schemes, the Proposed Approach Has Demonstrated a Reduction in Network Convergence Time Compared to Both Single-Vehicle and Fog-Based Approaches.
Recommended Citation
A. Asayesh et al., "Disseminating over-the-Air Updates Via Intelligent Labeling in Multi-tier Networks," 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2024, pp. 714 - 719, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/PerComWorkshops59983.2024.10503576
Department(s)
Computer Science
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024