Design of a Terminal Sensor With Super-Resolution Reconstruction Algorithm Used in IoT-Based Water Quality Monitoring

In IoT-based water quality monitoring, traditional three-dimensional (3D) fluorescence spectroscopy is difficult to deploy directly on terminal sensors due to its large size, high cost, and complex structure, despite its rich information being highly advantageous for water pollution detection. In this work, a novel approach is proposed through designing a compact sensor integrated with a super-resolution reconstruction algorithm to meet the requirement of entire 3D fluorescence spectra in cloud deployment of monitoring network at minimal cost. In the design of the terminal sensor, multiple LEDs with different wavelengths serve as excitation light source and multiple filters are used to switch emission wavelengths, enabling the acquisition of a sparse 3D fluorescence spectrum. In the reconstruction algorithm, a point spread function (PSF) is employed as a constraint module to simulate the degradation process of spectra from high to low resolution by modeling the spectral characteristics of the optical components responsible for degradation, including LEDs and optical filters, which can enhance the authenticity of the reconstructed spectra. Furthermore, a joint training strategy is introduced for better robustness by jointly optimizing the parameters of PSF and deep learning network. Experiment results demonstrate that the proposed method achieves the highest reconstruction quality (PSNR: 47.57 dB, SSIM: 0.9903) and pollutant identification accuracy (98.1%) compared with seven other reconstruction methods. The proposed approach can obtain high-resolution and entire 3D fluorescence spectra for water pollution detection with low manufacturing cost and minimal data, thereby meeting the practical application requirements in the field of IoT-based water quality monitoring.