compressive oversampling for robust data transmission in sensor networks
Post on 23-Feb-2016
50 Views
Preview:
DESCRIPTION
TRANSCRIPT
Compressive Oversampling for Robust Data Transmission in Sensor Networks
Infocom 2010
Outline
• Introduction– Problem Formulation
• Compressive Sensing for Erasure Coding– Channel Coding Overview– Compressive Sensing Fundamentals– Handling Data Losses Compressively
• Evaluation Results• Conclusions
Introduction
• Data loss in wireless sensing applications is inevitable– Transmission medium impediments– Faulty sensors
• To cope with channel disturbances,– Retransmission
• Inefficient in many scenarios, e.g., acoustic links – Forward error correction schemes (Reed-Solomon , LT ,
convolutional codes)• Computational complexity or bandwidth overhead
Introduction
• Why compressive sensing?– Reconstruction algorithms for compressively sampled
data exploit randomness • The stochastic nature of wireless link losses do not hamper the
performance of reconstruction algorithms at the decoder
• Proposed Compressive Sensing Erasure Coding (CSEC)– CSEC is achieved by nominal oversampling in an
incoherent measurement basis. • Cheaper than conventional erasure coding that is applied over
the entire data set from scratch.
Introduction
• CSEC is not intended as a replacement for traditional physical layer channel codes
• It is neither as general-purpose– It cannot be used for arbitrary non-sparse data
• Nor is the decoding as computationally efficient
Conventional and Proposed Approaches
Compressive Sensing for Erasure Coding
• The problem is to address is acquiring a length n signal vector f at a sensor node and communicating a length k measurement vector z such that f can be recovered accurately at a base station one or more wireless hops away
Channel Coding Overview
• Consider a simple sense-and-send scenario where we send the sensed signal to a base station over an unreliable communication channel, and
• Channel coding increases the average transmission rate by adding redundancy
nf R
z f k n
k n
, k nz f R
Channel Coding Overview
• Define the received measurement vector of length , where is the number of erasures
• Recovering the original signal from the received data is then a decoding operation of the form :
zk k e e
1
ˆ
T T
f C z
z Cz
X X X X
Compressive Sensing Fundamentals
• Compressive Sensing– is original signal– is K-sparse under – ,
measurement matrix – Reconstruct signal by linear program
Nx R
, x S S N N
M Ny x S My R
1ˆ arg min s.t.
NS RS S y S
Handling Data Losses Compressively
• We argue that compressive sensing not only concentrates but also spreads information across the m measurements acquired
• Based on this observation, we propose an efficient strategy for improving the robustness of data transmissions– the sensing matrix Φ with e additional rows
generated in the same way as the first m rows
Handling Data Losses Compressively
• These extra rows constitute extra measurements, which, under channel erasures will ensure that sufficient information is available at the receiver
• If incoherent measurements are acquired through “compressive oversampling” and e erasures occur in the channel
• The CS recovery performance will equal that of the original sensing matrix with a pristine channel with high probability
k m e
Evaluation Results
• Realistic wireless channels exhibit bursty behavior• To estimate the effect of CSEC performance with
bursty channels, we use the popular Gilbert-Elliott (GE) model
• We do this by performing a Monte-Carlo simulation over 104 random instances of a length 256 sparse signal and computing how often CS erasure coding results in exact recovery
Evaluation Results
Evaluation Results
Evaluation Results
Evaluation Results
Evaluation Results
• using a wireless network trace from the CRAWDAD database
• We selected used sensor nodes with an IEEE 802.15.4 radio transceiver placed about 12m apart between two different floors of auniversitybuilding
CSEC Implementation Costs
Conclusion
• We have explored the application of Compressive Sensing to handling data loss from erasure channels by viewing it as a low encoding-cost, proactive, erasure correction scheme
• We showed that oversampling is much less expensive than competing erasure coding methods and performs just as well
• This makes it an attractive choice for low-power embedded sensing where forward erasure correction is needed
top related