Thesis Topic Details

Topic ID:
3394
Title:
A Database for Sensor Data from Smart Environments
Supervisor:
Salil Kanhere
Research Area:
Sensor Networks, Databases
Associated Staff
Assessor:
Sanjay Jha
Topic Details
Status:
Active
Type:
R & D
Programs:
CS CE SE
Group Suitable:
Yes
Industrial:
No
Pre-requisites:
COMP3331, Good programming skills, familiarity with databases
Description:
Wireless Sensor Networks (WSNs) are widely known for their use in monitoring the physical environment. Due to the users' increasing awareness for rising energy prices, however, numerous power measuring sensors are also being installed in households and office spaces. The generated large volume of power readings serves as an enabler for many novel services and functions, e.g., to infer user behaviour [1] or identify appliances attached to wall outlets [2]. Despite the fact that commercial Cloud providers exist to handle data streams collected by sensors (e.g., Cosm [3]), a structured local storage with convenient and fast access for research purposes remains an open issue.

The goal of this thesis is to set up a database for power consumption data and provide simple means to (1) insert new data from different sensors and (2) access the stored data for further research purposes. As environmental parameters might also be collected along with the power readings, the database needs to be able to annotate each power reading with this contextual data.

The database needs to be capable of inserting readings collected using distributed power monitoring dongles in real-time, as well as an import module for data from the Tracebase [4] repository. The system needs to be able to provide data over a Web-based interface as well as via a Java API to directly use the power readings in other implementations. A data visualisation component for the collected big volume of data should be added in order to enable researchers to quickly inspect collected traces and select data ranges to retrieve.
Comments:
Literature:

[1] Ulrich Greveler, Benjamin Justus, Dennis Loehr: "Multimedia Content Identication Through Smart Meter Power Usage Profiles", Proc. CPDP, 2012.
[2] Andreas Reinhardt, Paul Baumann, Daniel Burgstahler, Matthias Hollick, Hristo Chonov, Marc Werner, Ralf Steinmetz: "On the Accuracy of Appliance Identification Based on Distributed Load Metering Data". Proc. SustainIT, 2012.
[3] Cosm (https://cosm.com)
[4] Tracebase: A repository of high-resolution appliance power traces (http://www.tracebase.org/)
[5] Weka toolkit: Data Mining Software in Java (http://www.cs.waikato.ac.nz/ml/weka/)
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