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Extracting Temporal Characteristics from Sensor Network Data Streams

Dimitrios Lymberopoulos, Athanasios Bamis, and Andreas Savvides


The extraction of temporal characteristics from sensor data streams can reveal important properties about the sensed events. Knowledge of temporal characteristics in applications where sensed events tend to periodically repeat, can provide a great deal of information towards identifying patterns, building models and using the timing information to actuate and provide services. In this paper we outline a methodology for extracting the temporal properties, in terms of start time and duration, of sensor data streams that can be used in applications such as human, habitat, environmental and traffic monitoring where sensed events repeat over a time window. Its application is demonstrated on a 30-day dataset collected from one of our assisted living sensor network deployments.


Publication typeInproceedings
Published inThe International Conference on Mobile Systems, Applications, and Services (MobiSys)
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