Edge Computing Architecture for Real-Time Denoising in Remote Electrochemical Environmental Sensor Networks

The article describes a new type of edge-computing architecture which is able to do real-time signal denoising at sensor nodes in remote electrochemical environmental sensor networks. Electrochemical sensors are chemical sensors that identify the chemical species by electrode reactions and provide analog signals that need denoising to enable proper environmental analysis. The three-tier system, which is proposed offers (i) sensor nodes with local processing, (ii) edge gateways to do the aggregation, and (iii) the cloud infrastructure to store and do analytics and integrate adaptive filtering, variational modal decomposition (VMD), and neural network-based denoising at sensor nodes. The architecture performance is assessed by a discrete-event simulation system that examines the performance of the architecture with 100 distributed sensor nodes under different noise levels (0–20 dB signal-to-noise-ratio). Comparative analysis against conventional cloud-only processing demonstrates substantial improvements: signal-to-noise ratio increases by 15 dB, per-node energy consumption reduces by 40%, and data transmission volume decreases by 70%. The edge-optimized strategy allows the real-time reduction of noise and extends the network life and minimizes the communication expenses. The robustness in different environmental conditions and sensor densities is proved by statistical analysis with the help of Analysis of Variance (ANOVA) and Monte Carlo simulations. The results of the simulation show that edge-computing can be used to provide more accurate, energy-efficient, and scalable electrochemical sensor networks to remotely monitor the environment.