Efficient Resource Management of Micro-Services in VANETs

Mohammad Bany Taha, Saed Alrabaee, Kim Kwang Raymond Choo

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

While vehicular ad hoc Networks (VANETs) are relatively well-studied, a number of challenges remain, particularly as autonomous vehicles become more commonplace. For example, devices on a vehicle such as, On-Board Units (OBUs) may have resource constraints which render them incapable of supporting computationally expensive cryptography operations required to achieve various security features. One potential solution is to offload computationally expensive tasks to other nodes in the VANET; however, the dynamic nature of the setup (such as the, mobility of the requesting vehicles and other nodes) compounds the challenge of resource management. In this context, we propose a scheme that uses Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to achieve data confidentiality in VANETs despite such challenges. Specifically, we build a cluster of vehicles to perform CP-ABE operations without relying on other nodes in the VANET. We use Kubernetes, an open-source container orchestration system, to build vehicle cluster(s) to handle distributed micro-tasks. In this scheme, we use a set of factors that impact the computation operations in cluster vehicle components (i.e., the OBU). Each factor, including the distance between the data owner vehicle and the target vehicle, the duration of each target vehicle in the cluster, and the resource of each vehicle in the cluster, has a weight based on its influence in computational operations. The Euclidean method is used to calculate the weight value for each factor. Based on the final total weight for each vehicle, our approach distributes the tasks between vehicles. We evaluate our results by comparing our approach with the mechanism of Kubernetes for task distribution, which only considers the resources in each vehicle. We also consider several scenarios with varying factors to evaluate their impact on the execution time of CP-ABE on OBUs in addition to using simulations to evaluate the performance of our approach in terms of transmission and propagation overheads for vehicles in the cluster.

Original languageEnglish
Pages (from-to)6820-6835
Number of pages16
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number7
DOIs
Publication statusPublished - Jul 1 2023

Keywords

  • CP-ABE
  • Euclidean algorithm
  • MLP
  • Resource management
  • SLNN
  • VANET

ASJC Scopus subject areas

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications

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