Detecting Anomalous Sensor Events in Smart Home Data for Enhancing the Living Experience

The need to have a secure lifestyle at home is in demand more than ever. Today's home is more than just four walls and a roof. Technology at home is on the rise and the place for smart home solutions is growing. One of the major concerns for smart home systems is the capability of adapting to the user. Personalizing the behavior of the home may provide improved comfort, control, and safety. One of the challenges of this goal is tackling anomalous events or actions. This work proposes using machine learning techniques to address this issue of detecting anomalous events or actions in smart environment datasets. The approaches are validated using real-world sensor data captured from a smart home testbed.

[1]  Hagit Attiya,et al.  Wiley Series on Parallel and Distributed Computing , 2004, SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention.

[2]  Vikramaditya R. Jakkula,et al.  Anomaly Detection Using Temporal Data Mining in a Smart Home Environment , 2008, Methods of Information in Medicine.

[3]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Diane J. Cook,et al.  Temporal pattern discovery for anomaly detection in a smart home , 2007 .

[5]  Ahmad Lotfi,et al.  Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour , 2012, J. Ambient Intell. Humaniz. Comput..

[6]  William C. Mann,et al.  The Gator Tech Smart House: a programmable pervasive space , 2005, Computer.

[7]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  D. M. Hutton,et al.  Smart Environments: Technology, Protocols and Applications , 2005 .

[10]  Dmitry B. Goldgof,et al.  Evaluation of smart video for transit event detection , 2009 .

[11]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[12]  Sajal K. Das,et al.  Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) , 2004 .

[13]  Gregory D. Abowd,et al.  Designing for the Human Experience in Smart Environments , 2005 .

[14]  Diane J. Cook,et al.  Predicting air quality in smart environments , 2010, J. Ambient Intell. Smart Environ..

[15]  Chien-Chen Chen,et al.  RFID-based human behavior modeling and anomaly detection for elderly care , 2010 .

[16]  Hans W. Guesgen,et al.  Use Cases for Abnormal Behaviour Detection in Smart Homes , 2010, ICOST.

[17]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[18]  Diane J. Cook,et al.  MavHome: an agent-based smart home , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..