Abstract
Increasingly research interests within the area of pervasive and ubiquitous computing, such as activity recognition, rely upon storage and retrieval of sensor data. Due to the increase in volume, velocity and variation of such sensor data its storage and retrieval has become a big data problem. There are a number of current platforms that are intended to store large amount of sensor data, however, they lack research oriented features. To address these deficiencies this study introduces a research oriented, device agnostic sensor, data platform called SensorCentral. This platform incorporates several research oriented features such as offering annotation interfaces, metric generation, exporting experimental datasets, machine learning services, rule based classification, forwarding live sensor records to other systems and quick sensor configuration. The current main installation of this platform has been in place for over 18 months, has been successfully associated with 6 sensor classes from 13 vendors and currently holds over 500 million records. Future work will involve offering this platform to other researchers and incorporating direct integration with the Open Data Initiative enabling better collaboration with other researchers on an international scale.
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Invest Northern Ireland is acknowledged for supporting this project under the Competence Centre Programs Grant RD0513853 – Connected Health Innovation Centre.
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Rafferty, J., Synnott, J., Ennis, A., Nugent, C., McChesney, I., Cleland, I. (2017). SensorCentral: A Research Oriented, Device Agnostic, Sensor Data Platform. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_11
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DOI: https://doi.org/10.1007/978-3-319-67585-5_11
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