An Abnormal Breakpoint Data Positioning Method of Wireless Sensor Network Based on Signal Reconstruction
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNson the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory;the common part of the associated data set is mined by exchanging common information among the clusterhead nodes, and the independent parts are updated within each cluster head node. To solve the non-convergenceproblem in the distributed computing, the approximate term is introduced into the optimization objectivefunction to make the sub-optimization problem strictly convex. And the decompressed sensing signalreconstruction problem is addressed by the alternating direction multiplier method to realize the distributedsignal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is locatedaccording to the characteristic information of the cross-power spectrum. The proposed method can accuratelyacquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance thecommunication quality of the experimental object.
Abstract
The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNson the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory;the common part of the associated data set is mined by exchanging common information among the clusterhead nodes, and the independent parts are updated within each cluster head node. To solve the non-convergenceproblem in the distributed computing, the approximate term is introduced into the optimization objectivefunction to make the sub-optimization problem strictly convex. And the decompressed sensing signalreconstruction problem is addressed by the alternating direction multiplier method to realize the distributedsignal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is locatedaccording to the characteristic information of the cross-power spectrum. The proposed method can accuratelyacquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance thecommunication quality of the experimental object.
참고문헌 (10)
- Y. Liao, "Research on optimisation processing of spatiotemporal correlation temperature and humidity data based on wireless sensor networks in cigarette factory", The Journal of Engineering, 2019(23), pp.9230~9235 (2019)
- Naveed Jan, "An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)", Sensors, 19(9), pp.2050~ (2019), MDPI AG[DOI]
- Reza Fotohi, "Securing Wireless Sensor Networks Against Denial‐of‐Sleep Attacks Using RSA Cryptography Algorithm and Interlock Protocol", International Journal of Communication Systems, 33(4) (2019), Wiley[DOI]
- Y. Zhao, "Structural compressed network coding for data collection in cluster-based wireless sensor networks", IEICE Transactions on Communications, 102(11), pp.2126~2138 (2019)
- J. Liu, "UAV-aided data collection for information freshness in wireless sensor networks", IEEE Transactions on Wireless Communications, 20(4), pp.2368~2382 (2021)
- Lourdes Martínez-Villaseñor, "A concise review on sensor signal acquisition and transformation applied to human activity recognition and human–robot interaction", International Journal of Distributed Sensor Networks, 15(6), pp.155014771985398~ (2019), SAGE Publications[DOI]
- Zhou-zhou Liu, "WSNs Compressed Sensing Signal Reconstruction Based on Improved Kernel Fuzzy Clustering and Discrete Differential Evolution Algorithm", Journal of Sensors, 2019, pp.1~9 (2019), Hindawi Limited[DOI]
- H. Wang, "Efficient reconstruction architecture of compressed sensing and integrated source-channel decoder based on Reed Solomon code", IEEE Communications Letters, 24(2), pp.239~243 (2020)
- S. Wang, "A reconstruction method for missing data in power system measurement using an improved generative adversarial network", Proceedings of the Chinese Society of Electrical Engineering, 39(1), pp.56~64 (2019)
- A. Mehto, "Virtual grid-based rendezvous point and sojourn location selection for energy and delay efficient data acquisition in wireless sensor networks with mobile sink", Wireless Networks, 26, pp.3763~3779 (2020)