Wireless sensor networks are often designed to perform two tasks: sensing a physical field and transmitting the data to end-users. A crucial aspect of the design of a WSN is the minimization of the overall energy consumption. Previous researchers aim at optimizing the energy spent for the communication, while mostly ignoring the energy cost due to sensing. Recently, it has been shown that considering the sensing energy cost can be beneficial for further improving the overall energy efficiency. More precisely, sparse sensing techniques were proposed to reduce the amount of collected samples and recover the missing data by using data statistics. While the majority of these techniques use fixed or random sampling patterns, we propose to adaptively learn the signal model from the measurements and use the model to schedule when and where to sample the physical field. The proposed method requires minimal on-board computation, no inter-node communications and still achieves appealing reconstruction performance. With experiments on real-world datasets, we demonstrate significant improvements over both traditional sensing schemes and the state-of-the-art sparse sensing schemes, particularly when the measured data is characterized by a strong intra-sensor (temporal) or inter-sensors (spatial) correlation.
This repository contains all the code and data to reproduce the results of the paper DASS: Distributed Adaptive Sparse Sensing.
First download both code and data provided with the paper. Then, for each figure
x, simply execute
Note that the data corresponding to each figure is stored in the corresponding
.mat file. In case you want to re-run the algorithms for each figure, simply delete/rename the file and execute the above command.
cvx\: Matlab Software for Disciplined Convex Programming, by Michael Grant and Stephen Boyd (Available here). We use it in the implementation of compressive sensing based methods (CS and CSN in the paper).
halfprecision\: Half precision library, by James Tursa. We use it in the implementation of the lossy compression algorithm (DCT-LPF and LTC in the paper). If you are not running Windows 32-bit or 64-bit, run
mex halfprecision.cinside this folder to compile it.
data\: The datasets for the experiments in the paper.
data\swissmeteo\:corresponds to "Payerne" in the paper. 4 sensor nodes (we use the first one), more than 1000 days, 144 points per day.
alignDataC2.matcontains the temperature,
alignDataC6.matcontains the solar radiation, alignDataC7.mat: temperature (formated in half-day cycle)
data\valais\alignDataC4.mat: corresponds to "Valais" in the paper. 43 sensor nodes (we use the first six nodes), 125 days, 144 points per day.
algorithm\: implementations of the DASS, CS, CSN, OLS-uniform, OLS-random
.mat) alone with the script. If you run the script with existing
.matfile, it will simply load the result and plot it. If you want to rerun the experiment, simply delete the corresponding
Copyright (C) 2013 Laboratory of Audiovisual Communications (LCAV), Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.