Scale Wireless Sensor Network with Compressed Sensing
scale wireless sensor networks (WSNs) consist of a huge number of wireless transceivers, each
with sensing, processing, communication and power supply units to monitor the real
information: i.e., perform measurements such as pressur
e, temperature, position, flow, vibration, force,
humidity, pollutants and biomedical signals like heart
rate and blood pressure.
The ideal WSNs are
designed to consume very limited power and are capable of fast data acquisition and multiple
Compressive sensing (CS) is an emerging signal acquisition technique that recovers a sparse signal from
few linear measurements. Due to its popularity, CS is currently applied in many areas such as coding,
signal processing and wireless sensor networks [
2]. In this paper, we present a new CS framework for
wireless sensor networks. We consider a sensor network consisting of S sensors connected to a
centralized fusion center. Each sensor measures a desired sparse signal and then compresses the sensed
using a sensing matrix. The compressed measurements from different sensors are sent to the fusion
center for joint recovery of the sparse signals. Thus, we have to consider a wireless sensor network having
number of sensors deployed at random locations.
Let s denote a
sparse signal of length
Therefore, we have to compress the sensed measurements from N to almost K values only.
thesis on this topic and describe how you will:
Select your research resources and state
the results that you need to re
simulate (via MATLAB)
simulate existing results and what tools you like to use (Matlab, existing L1
minimization software, etc…)
What do you suggest for improvements on the existing models
How can you extend this wo
Describe in details a major contribution you can do/discuss/evaluate/simulate to minimize the
power drained in real wireless sensor field and extend the network life time.
Suggest further application we can cover
Search for German/international partne
rs that we can contact to enhance our research and suggest
how we can contact them.
hesis has to be written in LATEX!!!
The maximum group size is 5; please organize your team to have:
One project manager (and research coordinator)
(elect the ones who have higher research capabilities)
Two developers (MATLAB, application level guys, … )
Dror Baron, Marco F. Duarte, Michael B. Wakin, Shriram Sarvotham, and Richard G. Baraniuk,
Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama,
Sensing capacity of
sensor networks: Fundamental
tradeoffs of SNR, sparsity, and sensing diversity
. (Information Theory and Applications Workshop, January 2007)
iorgio Quer, Riccardo Masiero, Gianluigi Pillonetto, Michele Rossi, Michele Zorzi,
Sensing, Compression and
Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework
. (IEEE Transactions on Wireless
Communications, Vol. 11, No. 10, October 2012, pp. 3447
enjamin Miller, Joel Goodman, Keith Forsythe, John Sun, Vivek Goyal,
sensor compressed sensing
: Performance bounds and simulated results
Third Asilomar Conference on Signals and Systems, pp.
1575, Nov. 2009)