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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dmucnarmarh2fj6syg5jyqs7ny" style="color: black;">2010 IEEE/RSJ International Conference on Intelligent Robots and Systems</a>
Our research focuses on anomaly detection problems in unknown environments using Wireless Sensor Networks (WSN). We are interested in detecting two types of abnormal events: sensory level anomalies (e.g., noise in an office without lights on) and time-related anomalies (e.g., freezing temperature in a mid-summer day). We present a novel, distributed, machine learning based anomaly detector that is able to detect timerelated changes. It consists of three components. First, a Fuzzy Adaptive<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iros.2010.5649660">doi:10.1109/iros.2010.5649660</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iros/LiTP10.html">dblp:conf/iros/LiTP10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uwfxqgpoabdq7m4irjnovf6ml4">fatcat:uwfxqgpoabdq7m4irjnovf6ml4</a> </span>
more »... nce Theory (ART) neural network classifier is used to label multi-dimensional sensor data into discrete classes and detect sensory level anomalies. Over time, the labeled classes form a sequence of classes. Next, a symbol compressor is used to extract the semantic meaning of the temporal sequence. Finally, a Variable Memory Markov (VMM) model in the form of a Probabilistic Suffix Tree (PST) is used to model and detect time-related anomalies in the environment. To our knowledge, this is the first work that analyzes/models the temporal sensor data using a symbol compressor and PST in a WSN. Our proposed detection algorithm is distributed, "light-weight", and practical for resource constrained sensor nodes. We verify the proposed approach using a volcano monitoring dataset. Our results show that this approach yields the same performance as the traditional Markov models with much less cost.
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