A methodology for extracting temporal properties from sensor network data streams

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. Categories and Subject Descriptors I.5.3 [Computing Methodologies]: Pattern RecognitionClustering[similarity measures] General Terms Algorithms, Experimentation, Human Factors Keywords sensor networks, data stream, te...

• Type: Conference
• Year: 2009
• Where: MOBISYS
• Authors: Dimitrios Lymberopoulos, Athanasios Bamis, Andreas Savvides
• Keywords: MOBISYS 2009, Sensed Events, Sensor Data Streams, Sensor Network Deployments, Wireless Networks