2016. Paper abstract bibtex
Wireless sensor networks (WSNs) are widely deployed to help detect disaster such as landslides, etc. in countries like Japan due to their efficiency and low cost. But there are various problems in these systems, such as the different kind of noise (such as random visit from animals, which leads to unnecessary false alarms). Orthogonally, context aware activity recognition draws increasing attention recently, where different environmental or martial sensors are adopted for activity recognition in various application scenarios. This paper studies a disaster detection system utilizing the Wi-Sun acceleration sensors together with the Wi-Sun signal fluctuation and considers the detection of human beings in the scene of interest. Hence, the false alarms introduced by the animals’ walk in, etc. could be detected using similar method. During the detection process, not only the wireless signal but also the acceleration value collected by the sensors are adopted for a better detection result. Then feature values are calculated for event detection based on the data samples. K nearest neighbors (KNN) is used to classify the events in the scene of interest: empty and non-empty. The detection results are promising as shown in the result section and the proposed method is applicable to a real landslide to avoid animal-induced false alarms.