Abstract
We propose novel algorithms for the timing correlation of streaming sensor data. The sensor data are assumed to have interval timestamps so that they can represent temporal uncertainties. The proposed algorithms can support efficient timing correlation for various timing predicates such as deadline, delay, and within. In addition to the classical techniques, lazy evaluation and result cache are utilized to improve the algorithm performance. The proposed algorithms are implemented and compared under various workloads.
Highlights
Wireless sensor networks are composed of sensors, embedded computers, and communication devices
We propose novel algorithms for the timing correlation of streaming sensor data
We further extend the algorithm by adopting the approaches of the lazy evaluation and the result look-up in this study
Summary
Wireless sensor networks are composed of sensors, embedded computers, and communication devices. They can harvest various interesting information such as light, motion, proximity, temperature, and chemical conditions. There are many emerging applications utilizing the information from sensors. The applications range from simple monitoring systems to sophisticated systems making critical decisions based on the automated analysis of the sensor data. We propose novel algorithms for the timing correlation of streaming sensor data. The sensor data are assumed to have interval timestamps so that they can represent temporal uncertainties
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