12,689 research outputs found
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring
Non-intrusive presence detection of individuals in commercial buildings is
much easier to implement than intrusive methods such as passive infrared,
acoustic sensors, and camera. Individual power consumption, while providing
useful feedback and motivation for energy saving, can be used as a valuable
source for presence detection. We conduct pilot experiments in an office
setting to collect individual presence data by ultrasonic sensors, acceleration
sensors, and WiFi access points, in addition to the individual power monitoring
data. PresenceSense (PS), a semi-supervised learning algorithm based on power
measurement that trains itself with only unlabeled data, is proposed, analyzed
and evaluated in the study. Without any labeling efforts, which are usually
tedious and time consuming, PresenceSense outperforms popular models whose
parameters are optimized over a large training set. The results are interpreted
and potential applications of PresenceSense on other data sources are
discussed. The significance of this study attaches to space security, occupancy
behavior modeling, and energy saving of plug loads.Comment: BuildSys 201
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