In this paper, we analyze the potential of combining wireless sensor networks with artificial neural networks (ANNs) to build a "smart forest-fire early detection sensory system" (SFFEDSS). We outline our new SFFEDS system in which temperature, light and smoke data from low-cost sensor nodes spread out on the forest bed is aggregated into information. This information is spatially and temporally labeled into knowledge which will be encoded as input to ANN models that convert it into intelligence. At the top tier of our system, the trained neural models make intelligent decisions and report fire in its early stages based on gathered field knowledge. In our experimentation, we extended the sensing capability of the MicaZ sensor motes by attaching external smoke detectors of our own design. The results are very promising as the SFFEDSS unit is able to not only detect fire but also accurately report the direction of fire progress which is deduced from the wind direction. *
Monitoring movement across national borders is a challenging problem due to several economic and technical issues. Due to the vast size, remoteness and other geographical confines of border regions, technical solutions are necessary to complement the limitations of manpower. In this paper we discuss our research in developing a system for detecting border intrusion activity by combining wireless sensor networks with artificial neural networks (ANNs). The key idea is to use ANN models to discover distinct patterns that describe an intrusion activity and use these patterns to train the ANN model which will then recognize intrusions and other abnormalities. We present a border intrusion detection system in which light and sound data from low-cost sensor motes spread out on the field is used to help ANNs make automated decisions and report intrusion activity. Our experimental results show that our border intrusion detection system can be used to monitor the borders without constant human supervision 1 .
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