Research on Data Transmission in Wireless Sensor Networks Based on QP-CSDR and CS-TSSC
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초록
In wireless sensor networks (WSNs), reducing communication energy consumption while maintaining communication quality is crucial for extending network lifespan. Compressed sensing (CS) theory enables high-precision data reconstruction even at high compression ratios, effectively reducing communication data volume, lowering energy consumption, and prolonging network life. This chapter delves into the application of CS theory in WSN data transmission, improving the quantum particle swarm optimization-based CS data reconstruction algorithm by considering the characteristics of monitored signals. To address issues of high algorithm complexity and insufficient reconstruction accuracy under high sparsity conditions, a data adaptive length segmentation method based on CS is proposed, further enhancing algorithm performance and offering new perspectives for optimizing WSN data transmission.
Abstract
In wireless sensor networks (WSNs), reducing communication energy consumption while maintaining communication quality is crucial for extending network lifespan. Compressed sensing (CS) theory enables high-precision data reconstruction even at high compression ratios, effectively reducing communication data volume, lowering energy consumption, and prolonging network life. This chapter delves into the application of CS theory in WSN data transmission, improving the quantum particle swarm optimization-based CS data reconstruction algorithm by considering the characteristics of monitored signals. To address issues of high algorithm complexity and insufficient reconstruction accuracy under high sparsity conditions, a data adaptive length segmentation method based on CS is proposed, further enhancing algorithm performance and offering new perspectives for optimizing WSN data transmission.