Quantum-Inspired Artificial Bee Colony for Latency-Aware Task Offloading in IoV

TL;DR

This paper presents a quantum-inspired heuristic algorithm to optimize latency-sensitive task offloading in Internet of Vehicles, improving decision-making speed and solution quality in vehicular networks.

Contribution

It introduces a novel Quantum-Inspired Artificial Bee Colony algorithm that leverages quantum principles to enhance task offloading in IoV systems.

Findings

QABC outperforms classical algorithms in latency reduction

Enhanced exploration avoids local optima effectively

Improves real-time decision-making in vehicular networks

Abstract

Efficient task offloading is crucial for reducing latency and ensuring timely decision-making in intelligent transportation systems within the rapidly evolving Internet of Vehicles (IoV) landscape. This paper introduces a novel Quantum-Inspired Artificial Bee Colony (QABC) algorithm specifically designed for latency-sensitive task offloading involving cloud servers, Roadside Units (RSUs), and vehicular nodes. By incorporating principles from quantum computing, such as quantum state evolution and probabilistic encoding, QABC enhances the classical Artificial Bee Colony (ABC) algorithm's ability to avoid local optima and explore high-dimensional solution spaces. This research highlights the potential of quantum-inspired heuristics to optimize real-time offloading strategies in future vehicular networks.