Acknowledgements

In our paper Sensing, Compression and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework, we we have used several real-world signals to study the applicability of compressing sensing in the field of wireless sensor networking. In details, we have exploited data gathered from the following WSN deployments.

Dept. of Information Engineering @ UNIPD

The Wireless Sensor Network testbed @ the Department of Engineering of the Univrsity of Padova has been initially setup thanks to WISE-WAI WSN Project, see the following paper for an initial description: Wireless Sensor Networks for City-Wide Ambient Intelligence (WISE-WAI). The testbed has been further upgraded through the addition of new sensors and more capable wireless nodes thanks to the projet "MOSAICS: MOnitoring Sensor and Actuator networks through Integrated Compressive Sensing and data gatering" (grant. no. CDPA 094077, 2009-2011).

LUCE (Lausanne Urban Canopy Experiment)

(WSN testbed at the École Polytechnique Fédérale de Lausanne)

See SensorScope and in particular the papers: SensorScope: Application-Specific Sensor Network for Environmental Monitoring and Estimation of urban sensible heat flux using a dense wireless network of observations.

St-Bernard WSN testbed @ EPFL

See SensorScope and in particular the papers: SensorScope: Application-Specific Sensor Network for Environmental Monitoring and Estimation of urban sensible heat flux using a dense wireless network of observations.

CitySense WSN testbed

(Developed by Harvard University and BBN Technologies)

See: CitySense and the paper CitySense: An Urban-Scale Wireless Sensor Network and Testbed.

Sense & Sensitivity WSN testbed

See the paper: Sense and sensitivity: a large-scale experimental study of reactive gradient routing.


For our study, we have processed the data from the above WSN deployments and formatted it according to matrices generated in MatLab. In order to make our work easily reproducible, we make all these matrices publicly accessible, see the accompanying archive: WSN Datasets.

We would like to kindly acknowledge EPFL, Harvard University, BBN Technologies and OrangeLabs for granting the permissions to make their processed data publicly available on this site.