Research on Intelligent Sensor Network for Laboratory Data Acquisition and Processing

This paper proposes a laboratory data acquisition and processing system based on intelligent sensor networks, aiming to improve the efficiency and accuracy of experimental data acquisition. The system collects experimental data in real time by deploying distributed sensor nodes, and uses Huffman coding algorithm for data compression and transmission. Huffman coding effectively reduces the amount of data transmission by optimizing the coding length, thereby improving the transmission efficiency of the system and saving network bandwidth. To ensure the efficient operation of the system, this paper designs a multi-level data processing algorithm based on intelligent sensor networks, which can quickly and accurately analyze and process data after data acquisition, thereby realizing efficient experimental data monitoring and control. Simulation results show that the system can significantly improve the speed of data acquisition and processing under different experimental conditions, and effectively reduce the delay of data transmission. When collecting 1000 data points, the data transmission rate after Huffman coding is increased by about 25%, and the transmission delay is reduced from the original 150ms to 120ms. At the same time, the data compression ratio reaches 50%. Compared with traditional transmission methods, the system can significantly reduce the occupancy of network bandwidth. Huffman coding shows high efficiency in compression ratio and decoding speed. In response to the processing needs of large-scale experimental data, Huffman coding effectively improves data processing capabilities and ensures the integrity of data transmission.