Low-Cost Sensors Array Based on Planar Electromagnetic Sensor Simulation for Environmental Monitoring

Abstract

Agriculture sectors have increased in sizes and numbers due to the expansion of human population. Human population growth has caused the demand in food, cloth, transportation, shelter etc. to increase tremendously. Essentially speaking, those surpluses of demands will adversely affect the production of agriculture sectors. In order to obtain high yield, the farmers might be overusing the fertilizer for their crops. These overused fertilizers will be dissolved in the nearest water resources such as river, pond and well. Hence, the natural water resources will be contaminated by these overused fertilizers. In this situation, there is a need for a sensor which could determine the contamination level in natural water resources. This project proposed low-cost sensor arrays based on planar electromagnetic sensors to monitor the contamination in the natural water resources. The sensors consist of three electromagnetic sensors that have been constructed in three types of configuration array namely, parallel, star, and delta. The modeling and simulation of the sensors were done by using COMSOL Multiphysics 4.2 software. Each sensor’s input was assign to 10 Volt peak to peak voltages and then the impedance of each sensor was obtained. In practical application, the impedance of these sensors will determine the contamination level of the water under test. The contamination condition was simulated by changing the electrical properties of the environment domain of the model that suit contamination condition. The impedance of each sensor was tabulated according to the increasing of frequency of the supply voltages. Hence, the best configuration of the planar electromagnetic sensors array for detection of contamination inside the natural water will be determined based on the sensitivity. This type of sensors will provide the in-situ measurement system which will save the cost and consequently the time required for each sample.