Lightweight TinyML-Enhanced Task Offloading in VANETs for Next-Generation Intelligent Transportation Systems

Document Type

Article

Source of Publication

IEEE Open Journal of the Communications Society

Publication Date

1-1-2026

Abstract

Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification Component that applies TinyML anomaly detection to validate execution with minimal cloud dependence; and a Scoring Component that updates credibility using an exponentially weighted delay-penalty model. A hybrid approach then integrates Random Forest (RF) for credibility-aware node selection with TinyLSTM for link stability prediction, while vehicles pre-screen candidates locally by mobility and resource availability before querying RSUs, reducing signalling overhead. Simulations in OMNeT++, Veins, and SUMO using a Luxembourg City mobility trace show a 24% higher task success rate, 9% lower completion time, 21% lower packet drop ratio, and 65% fewer disconnection-induced failures over heuristic, MLP, and deep-RL baselines, at only 9-18% RSU-side CPU overhead. The RF (380KB) and TinyLSTM (48KB post-quantization) models fit automotive-grade microcontroller budgets, confirming practical deployability for next-generation Intelligent Transportation Systems (ITS).

ISSN

2644-125X

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

7

First Page

8444

Last Page

8459

Disciplines

Computer Sciences

Keywords

autonomous vehicular networks, credibility assessment, link stability prediction, mobility-aware offloading, network dynamics, task offloading, tiny machine learning (TinyML), TinyLSTM, Vehicular ad hoc networks (VANETs)

Scopus ID

105045092655

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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