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Sensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the Received Signal Strength (RSS) of wireless sensors are used instead of a global positioning system because of the lack of availability of a global positioning system for indoor environments. In this paper, the authors used a wireless sensor node to collect data on various environmental parameters - temperature, humidity, sound, and light. They then extracted some features from the collected data and trained the location data classifier to identify the location of the wireless sensor node.
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