Nowadays, real-time water quality monitoring has become an important tool for environmental protection and water resource management. Traditional water quality monitoring systems usually rely on cloud computing for data storage and analysis. However, it faces the problems of delay and data loss when facing remote areas or unstable network environments.
In order to solve the problems of insufficient real-time and accuracy as well as high energy consumption, water quality prediction and early warning methods based on edge computing have emerged. It combines IoT and edge computing technologies to formulate an online water quality monitoring and warning model. The system improves the detection accuracy based on the pre-processing of collected source data. Moreover, data streams and computational resources can be saved by introducing edge computing technology for preliminary analysis and processing of data collected from monitoring stations.
1.What causes water pollution?
Water is the most important resource for human beings. However, with the increase of sewage discharge, industrial discharge, agricultural watering and urban discharge, contaminated water threatens the safety of drinking water. Major contaminants of water include viruses, bacteria, fertilizers, parasites, pharmaceuticals, pesticides, nitrates, fecal waste, phosphates, radioactive substances and plastics. These substances do not change the color of water, but they can be difficult pollutants to eliminate. Therefore, the safety of water resources is a matter of urgency.
2. Edge computing based water quality monitoring system architecture
Data Acquisition Layer: using sensors to collect physical, chemical and biological indicators in the water body. Such as pH, dissolved oxygen, turbidity, temperature, etc. The sensors transmit this data to the edge computing devices.
Edge computing layer: the edge device performs data processing and preliminary analysis near the data collection point, including data cleaning, outlier detection, and real-time warning. This localized processing allows for quick identification of water quality anomalies and triggers alarms when necessary.
Cloud Analysis Layer: Data pre-processed by edge computing is transferred to the cloud for more complex analysis and historical data comparison. For example, pollution source analysis and water quality trend prediction. This model ensures efficient and intelligent data processing.
3. Benefits of using edge computing for water quality testing
Traditional models have many limitations when facing complex data and nonlinear problems. In recent years, there has been a gradual increase in research on water quality monitoring using advanced technologies such as wireless sensor networks and the Internet of Things (IoT). Although these systems have improved the monitoring efficiency, they still face new challenges in data transmission and processing.
The water quality monitoring system makes full use of the computing power of the edge layer to process the water quality detection data collected in the perception layer. The edge computing data processing timely and quickly divides different water resources, so as to clarify the relationship between water resources and ecological environment.
Edge computing can effectively solve the problems of high latency, unstable network and low bandwidth in cloud computing. It has been applied to intelligent transportation, smart city, power grid detection and water conservancy fields, etc.