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Computational Sensing and Smart Machines (CSSM) laboratory
Research
1. Development of a multi-model sensor node with data recovery ability to monitor environmental factors which could be used for forest fires detection
Researcher(s)
Supervisor(s)
Abstract
Forests are the valuable resources in our world and forests support us to improve our life making better environment conditions. But forest fires can occur at any time due to few reasons such as lightning, human carelessness, and exposure of fuel to extreme heat etc. These forest fires are mostly uncontrolled and destroy natural and human resources spreading urban areas.
A one way to survive from these disasters is by using an early detection method such as real-time monitoring the factors that mostly affect to forest fires. The major factors which mostly affected to the forest fires are temperature and humidity and presence of smoke in forest areas. Therefore, a sensor node with few sensors such as temperature sensors, humidity monitors and smoke detectors can be used to early detection of forest fires. The design procedure of a sensor node depends on few parameters such as accuracy, power consumption, number of sensor nodes to successful early detection, data processing, data transmission, cost etc. Also, some countries use some methods (for an example fire weather index in Canada) to identify forest fires. Following these methods, a successful sensor node can be designed fulfilling above design parameters. Read more...