SpiralEdge-IoV: secure and adaptive task offloading in internet of vehicles edge computing using logarithmic spiral defense.

In recent years, the internet of vehicles (IoV) has become an important enabler of intelligent transportation systems, providing vehicle-edge computing for latency-sensitive and computation-intensive vehicular applications. In such environments, the efficient offloading of tasks is pivotal; yet existing approaches primarily focus on optimising performance, often under the assumption of benign operating conditions or by employing static, trust-based mechanisms. However, these methods fall short in practical IoV implementations due to high mobility, short-lived connectivity, and the adversarial nature of IoV, where misbehaviour and resource exhaustion can substantially compromise the system's reliability and security. In response to these problems, we design SpiralEdge-IoV, a secure and adaptive task offloading framework that tightly integrates defence and optimisation. This framework embeds a logarithmic spiral defence (LSD) mechanism that models trust as a deepening, adaptive path over time, enabling online risk evaluation and incremental offensive action against suspicious parties. We integrate these risk scores into an in-built bio-inspired Addax-optimisation-based decision model (LSD-AddaxNet) to obtain security-aware multi-objective offloading decisions that not only minimise latency, energy consumption, and execution cost, but also maximise robustness. A combination of realistic vehicular edge-offloading traces and the VeReMi misbehaviour dataset is employed to conduct an extensive simulation-based evaluation, confirming the effectiveness of the proposed framework. Relative to representative optimisation- and learning-based baselines, SpiralEdge-IoV delivers up to 18% lower average task latency, reduces energy consumption by about 15%, and increases task success rates in adversarial settings by over 20%. In addition, the analyses on convergence and scalability demonstrate that the framework enables stable optimisation with acceptable runtime overhead in dense vehicular scenarios. SpiralEdge-IoV can be helpful for attack-resilient, low-latency IoV edge computing and is thus suitable for safety-critical vehicular applications and future intelligent transportation systems, as shown in the results.