A quantum-inspired sensor consolidation measurement approach for cyber-physical systems.

Mekala, M.S.; Srivastava, Gautam; Gandomi, Amir H.; Park, Ju H.; Jung, Ho-Youl

Abstract

Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantum-inspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate the feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design the Angular-based node position analysis method to localize the node through rotation and t-gate to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results convince us that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks.

Citation

MEKALA, M.S., SRIVASTAVA, G., GANDOMI, A.H., PARK, J.H. and JUNG, H.-Y. 2024. A quantum-inspired sensor consolidation measurement approach for cyber-physical systems. IEEE transactions on network science and engineering [online], 11(1), pages 511-524. Available from: https://doi.org/10.1109/TNSE.2023.3301402

• Journal Article Type: Article
• Acceptance Date: Jul 29, 2023
• Online Publication Date: Aug 2, 2023
• Publication Date: Feb 29, 2024
• Deposit Date: Aug 4, 2023
• Publicly Available Date: Aug 21, 2023
• Journal: IEEE Transactions on Network Science and Engineering
• Print ISSN: 2327-4697
• Electronic ISSN: 2327-4697
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 1
• Pages: 511-524
• DOI: https://doi.org/10.1109/TNSE.2023.3301402
• Keywords: Quantum computing; Node contiguity rate; Node knack measurement index; Machine learning; Hadamard gate
• Public URL: https://rgu-repository.worktribe.com/output/2029507