; Distributed Intelligence Laboratory, Department of Electrical Engineering and Computer Science, The University of Tennessee, 203 Claxton Complex, Knoxville, 37996-3450, USA
Lynne E. Parker
In this paper, we present an anomaly detection system that is able to detect time-related anomalies by using a wireless sensor network and a mobile robot. The sensor network uses an unsupervised fuzzy adaptive resonance theory (ART) neural network to learn and detect intruders in a previously unknown environment. Upon the detection of an intruder, a mobile robot travels to the position where the intruder is detected to investigate by using its camera. The wireless sensor network uses a hierarchical communication/learning structure, where the mobile robot is the root node of the tree. Our fuzzy ART network is based on Kulakov and Davcevpsilas implementation (Kulakov and Davcev, 2005). However, we enhance their work by extending the fuzzy ART neural network with a Markov model to learn a time series and detect time-related anomalies. Finally, a mobile robot is employed to verify whether the detected anomalies were caused by intruders. The proposed architecture is tested on physical hardware. Our results show that our enhanced detection system with mobile robot verification has a higher accuracy and lower false alarm rate than the original fuzzy ART system.