Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Reliable computer systems (3rd ed.): design and evaluation
Reliable computer systems (3rd ed.): design and evaluation
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
A Methodology for Detection and Estimation of Software Aging
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Critical event prediction for proactive management in large-scale computer clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A wireless sensor network For structural monitoring
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
A large-scale study of failures in high-performance computing systems
DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
RETRACTED: Impacts of sensor node distributions on coverage in sensor networks
Journal of Parallel and Distributed Computing
International Journal of Sensor Networks
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The Critter temperature sensor device has been developed to understand the full nature of pervasive sensor networks. Off the shelf integrated sensor devices incorporate some amount of adaptation to make the devices more reliable. The Critter provides raw instantaneous readings including outlier data that may be considered anomalies or perturbations. We have deployed Critter data sensors pervasively through one academic building for almost 18 months. This paper explores the causes of temperature data perturbations, defined as two temperature data readings taken within seconds of each other that differ by several degrees. Temperature sensor data perturbations are actually the effects of user activity within buildings. By capturing the raw data without automatic processing, we are able to show a correlation between time of the work day and the frequency of data perturbation.