A novel quantum-inspired solution for high-performance energy-efficient data acquisition from IoT networks

Abstract

Accuracy in IoT data acquisition has been an indispensable need to meet the increasing demand for time-sensitive data analysis, and real-time decision-making. Conspicuously, this study proposes a Quantum Computing inspired technique of temporal space optimization for real-time big IoT applications. For this purpose, quantification of IoT sensors is performed in terms of Sensors of Interest (SoI) and Degree of Aptness (DoA) measure to minimize IoT sensor-space in real-time. The proposed methodology incorporates quantum computing-based formalization of IoT sensor parameters to present a Quantum-Temporal Minimization Algorithm. Moreover, 2 key performance indicators in terms of Data Similarity Analysis and Energy Efficiency are estimated for optimized efficacy. To evaluate the presented technique, numerous simulations are performed in real-time scenario of vehicular traffic determination over 1km of Regional National Highway using 70 WiSense nodes comprising of noise sensors, vibration sensors, and Raspberry Pi device. Acquired data comprising of 28,586 segments are stored in the Amazon EC2 cloud database for evaluation. The performance enhancement is estimated based on comparative analysis with several state-of-the-art optimization techniques. Results registered depict that significant improvements are registered for the presented technique in terms of temporal effectiveness, and performance parameters like Accuracy, Correlation Analysis, and Reliability.