qIoV: A Quantum-Driven Internet-of-Vehicles-Based Approach for Environmental Monitoring and Rapid Response Systems

TL;DR

This paper introduces qIoV, a quantum-enhanced IoV framework for environmental monitoring and rapid hazard response, utilizing quantum sensors, dynamic quantum networks, and advanced quantum classifiers to improve detection accuracy and speed.

Contribution

It presents a novel quantum IoV architecture integrating quantum sensors, a dynamic quantum mesh network, and variational quantum classifiers for environmental hazard detection.

Findings

Achieved over 90% in precision, recall, and F1-Score metrics for toxic gas detection.

83% increase in detection speed compared to conventional methods.

Validated quantum protocols and entanglement fidelity through theoretical analysis.

Abstract

This research addresses the critical necessity for advanced rapid response operations in managing a spectrum of environmental hazards. We propose a novel framework, qIoV that integrates quantum computing with the Internet-of-Vehicles (IoV) to leverage the computational efficiency, parallelism, and entanglement properties of quantum mechanics. Our approach involves the use of environmental sensors mounted on vehicles for precise air quality assessment. These sensors are designed to be highly sensitive and accurate, leveraging the principles of quantum mechanics to detect and measure environmental parameters. A salient feature of our proposal is the Quantum Mesh Network Fabric (QMF), a system designed to dynamically adjust the quantum network topology in accordance with vehicular movements. This capability is critical to maintaining the integrity of quantum states against environmental and vehicular disturbances, thereby ensuring reliable data transmission and processing. Moreover, our methodology is further augmented by the incorporation of a variational quantum classifier (VQC) with advanced quantum entanglement techniques. This integration offers a significant reduction in latency for hazard alert transmission, thus enabling expedited communication of crucial data to emergency response teams and the public. Our study on the IBM OpenQSAM 3 platform, utilizing a 127 Qubit system, revealed significant advancements in pair plot analysis, achieving over 90% in precision, recall, and F1-Score metrics and an 83% increase in the speed of toxic gas detection compared to conventional methods.Additionally, theoretical analyses validate the efficiency of quantum rotation, teleportation protocols, and the fidelity of quantum entanglement, further underscoring the potential of quantum computing in enhancing analytical performance.