A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Methods for Designing Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Sympathy for the sensor network debugger
Proceedings of the 3rd international conference on Embedded networked sensor systems
Engineering multiversion neural-net systems
Neural Computation
Reputation-based framework for high integrity sensor networks
ACM Transactions on Sensor Networks (TOSN)
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
LiveNet: Using Passive Monitoring to Reconstruct Sensor Network Dynamics
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Sensor network data fault types
ACM Transactions on Sensor Networks (TOSN)
Suelo: human-assisted sensing for exploratory soil monitoring studies
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Detecting and Rectifying Anomalies in Body Sensor Networks
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
The hitchhiker's guide to successful residential sensing deployments
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
MANNA: a management architecture for wireless sensor networks
IEEE Communications Magazine
Towards Automatic Spatial Verification of Sensor Placement in Buildings
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Hi-index | 0.00 |
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.