While building the smart environment using IoT. it enables environmental monitoring to be able to conserve energy. water and other natural resources, which are being contaminated every second. In conventional environmental monitoring methods, samples are collected and analyzed and anałytical instrumentation is carried out on them.
There are two ways of doing this. One is manual, where the sample is collected and analyzed in a lab. The second is instrumental, where the quantity of pollutants in the sample is analyzed, automatically. Instrumental methods have direct analytics, where readings and results are automatically received. Manual methods, on the other hand, need pre-treating the sample before carrying out sedimentation, isolation, and other processes on it.
When we think about environmental monitoring, it is a very complex system and, hence, we cannot just start using sensors for regulatory purposes. If we have data for water and air, then we can use AI and ML tools, among others.
There are environmental sensors for measuring water quality, radiation, and hazardous chemicals. Similarly, in the industrial IoT (IIoT), we need methods for ensuring the safety of workers, because some industries generate obnoxious gases like sulfur, methane, and sulfur compounds, which are bad for human health. By getting data out of sensors, we can maintain a good safety record. Places that are inaccessible can also effectively utilize sensors.
Sensors are divided into two categories: electrochemical-based and metal-oxide-based. Companies use these sensors based on their requirements. Both types of sensors have advantages and disadvantages. But research is being done mostly on metal-oxide sensors to get more sound results for environmental monitoring. Because of the environment is heterogeneous, the system needs to be utilized well because we cannot develop one protocol-based system and expect it to work in all situations.
Therefore we need a multi-protocol system. Also, it is important to understand the interference of pollutants, because pollutants such as ozone, NO2, and particles have interference capability. Therefore the science behind this interference, how data is coming, and what could be the reason for any deviation in data must be studied and understood. Only then can a sensor be well-characterized and developed.
The top sensors used in the smart environment are:
- Temperature sensors
- Proximity sensors
- Water quality sensors, measure pH, BOD, COD, and other microbial contaminants; these also measure ion parameters like arsenic, iron, or other compounds
- Gas sensors, which detect air quality conditions
- Smoke sensors, which are required for industrial environmental conditions
IoT Applications for Smart Environments
- Weather Monitoring
- Air Pollution Monitoring
- Noise Pollution Monitoring
- Forest Fire Detection
Environment Weather Monitoring
It collects data from a number of a sensor attached such as a temperature, humidity, pressure, etc, and sends the data to cloud-based applications and stores back-ends. The data collected in the cloud can then be analyzed and visualized by cloud-based applications. Weather alerts can be sent to subscribed users from such applications. AirPi is a weather and air quality monitoring kit capable of recording and uploading information about temperature, humidity, air pressure, light levels, UV levels, carbon monoxide, nitrogen dioxide, and smoke levels to the Internet.
Environment Air Pollution Monitoring
IoT-based air pollution monitoring systems can monitor the emission of harmful gases by factories and automobiles using gaseous and meteorological sensors. The collected data can be analyzed to make informed decisions on pollution control approaches.
Environment Noise Pollution Monitoring
Noise pollution monitoring can help in generating noise maps for cities. It can help policymakers in making policies to control noise levels near residential areas, schools, and parks. It uses a number of noise monitoring stations that are deployed at different places in a city. The data on noise levels from the stations is collected on servers or in the cloud and then the collected data is aggregated to generate noise maps.
Environment Forest Fire Detection
IoT-based forest fire detection system uses a number of monitoring nodes deployed at different locations in a forest. Each monitoring node collects measurements of ambient conditions including temperature, humidity, light levels, etc. Early detection of forest fires can help in minimizing the damage.
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