Being SMART about failures: assessing repairs in SMART homes

Inexpensive wireless sensing products are dramatically reducing the cost of in-home sensing. However, these sensors have been found to fail often and prohibitive maintenance costs may negate the cost benefits of inexpensive hardware and do-it-yourself installation. In this paper, we describe a new technique called SMART that uses application-level semantics to detect, assess, and adapt to sensor failures. SMART detects sensor failures at run-time by analyzing the relative behavior of multiple classifier instances trained to recognize the same set of activities based on different subsets of sensors. Once a failure is detected, SMART assesses its importance and adapts the classifier ensemble in attempt to avoid maintenance dispatch. Evaluation on three homes from two public datasets shows that SMART decreases the number of maintenance dispatches by 55% on average, identifies non-fail-stop failures at run-time with more than 85% accuracy, and improves the activity recognition accuracy under sensor failures by 15% on average.

[1]  Antonio Alfredo Ferreira Loureiro,et al.  MANNA: a management architecture for wireless sensor networks , 2003, IEEE Commun. Mag..

[2]  Ricardo Chavarriaga,et al.  Detecting and Rectifying Anomalies in Body Sensor Networks , 2011, 2011 International Conference on Body Sensor Networks.

[3]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.

[4]  Hari Balakrishnan,et al.  Memento: A Health Monitoring System for Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[5]  Gregory J. Pottie,et al.  Sensor network data fault types , 2007, TOSN.

[6]  Fabio Roli,et al.  Dynamic Classifier Selection , 2000, Multiple Classifier Systems.

[7]  Deborah Estrin,et al.  Suelo: human-assisted sensing for exploratory soil monitoring studies , 2009, SenSys '09.

[8]  William B. Yates,et al.  Engineering Multiversion Neural-Net Systems , 1996, Neural Computation.

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

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

[11]  Henry A. Kautz,et al.  Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.

[12]  Diane J Cook,et al.  Assessing the Quality of Activities in a Smart Environment , 2009, Methods of Information in Medicine.

[13]  Matt Welsh,et al.  LiveNet: Using Passive Monitoring to Reconstruct Sensor Network Dynamics , 2008, DCOSS.

[14]  Fabio Roli,et al.  Methods for Designing Multiple Classifier Systems , 2001, Multiple Classifier Systems.

[15]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[16]  Kamin Whitehouse,et al.  The hitchhiker's guide to successful residential sensing deployments , 2011, SenSys.

[17]  Deborah Estrin,et al.  Sympathy for the sensor network debugger , 2005, SenSys '05.

[18]  Mani B. Srivastava,et al.  Reputation-based framework for high integrity sensor networks , 2004, SASN '04.