Faulty sensor detection using data correlation of multivariant sensor reading in smart agriculture with IOT

The Internet of Things (IoT), the idea of getting real-world objects connected with each other, will change the ways we organize, obtain and consume information radically. Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers and crops regardless of their geographical differences. On the other hand, Sensor fault is critical in smart grids, where controllers rely on healthy measurements from different sensors to determine all kinds of operations. However, when sensor fault happens, missing data and/or bad data can flow into control and management systems, which may lead to potential malfunction or even system failures. This brings the need for Sensor Fault Detection and eliminate this potential fault. This thesis proposes to design a Faulty Sensor Detection Mechanism using the data correlation method of multivariate sensors. This method will be applied to the smart agriculture which uses multi-variate sensors such as moisture sensor, temperature sensor and water sensor in IoT. The data are collected and received by a microcontroller which also can be linked to the internet. According to the algorithm, which applied on the smart agriculture, in case, the system gives No FAULT when the correlation value between (temperature, moisture) and (temperature, water) are negative and positive for (Water, moisture). In other cases. The system has a fault in a sensor when the correlation values between sensors are changed. Also, when the sensor gives a constant reading for a long time the system has got a fault in this sensor. The system got No FAULT when was different in sensors reading and the correlation value between (temperature, moisture) is (-0.33), between (temperature, water) is (-0.16) and (moisture, water) is (0.36). In addition, this system will be connected to the internet through the ESP8266 module. In order to surveillance the system at anytime and anywhere, this system is connected with the cloud (Things board) by using an ESP8266 WiFi network connection. This would allow the system to be more efficient and more reliable in detecting and monitoring the system’s parameters such as the state of sensors. The accuracy of the algorithm for data correlation may be changing depending on the application that wants to detect the faulty sensor in the system and according to how many data that income to the microcontroller per minute and how many data should take to calculate the correlation coefficient. Therefore, for the smart agriculture which it's used in this project, the period is adjusted to give a good diagnose for the sensor as soon as possible