Energy-Efficient Data Gathering and Broadcasting
in Sensor Networks using Channel Codes
Mina Sartipi, Nazanin Rahnavard, Faramarz Fekri
Abstract
Goal:
Energy-efficient and reliable communication in wireless sensor networks
Communication involves:
Encode X2 as follows:
 X2 is fed into a rate R systematic LDPC encoder.
 PX2 , the corresponding parity bits, is sent through the wireless channel.
 Data Gathering (Sensors to sink)
 Multicasting / Broadcasting (Sink to sensors)
 Scaling to more than two correlated sources
 DSC at arbitrary rate on Slepian-Wolf rate region
 DSC with unknwon correlation parameter
RX2=1/R-1 bit per input bit.
Data Gathering:

Extensions of Distributed Source coding of
correlated sources
Distributed Source Coding on Corner Points:
Future activity: Energy-efficient broadcasting
Correlated Data

Distributed Source Coding
Modeling Distributed Source Coding with Parallel Channels:
Multicasting / Broadcasting
Non-uniform Channels
c1
Redundant Transmission
 Correlated Data


Rateless Code
X2
k
Distributed Source coding of correlated sources
using LDPC Codes
X2
Rn
Systematic
(X2 ,PX2 )
Channel
n
Encoder
Rate R
 Motivation
Correlation X1
Channel
Decoder
c2
PX2
(1-R)n
Wireless
Channel
An easy, energy-efficient, and scalable broadcasting scheme
 Providing reliability with little penalty
 Low complexity
 Require no optimization and no topology information

X2
P'X2
 Proposed Approach

Use an efficient erasure coding (rateless coding) to recover for losses
Encoding:
Motivation:
 Channel parameters are different and unknown
 A source can generate potentially infinite supply of encoding packets from
the original data
 Any receiver collects as many packets as it needs to complete the decoding
 Receivers are at one hop distance from the sender
 Extra cares needed for multi-hop wireless networks!
Code Design:
Distributed Source Coding:
Possible Design Methodologies:
1)Design an LDPC code for the equivalent channel
Goal: Compressing X2
 Without communicating with X1
BEC (1)
X1
Use ensemble g (,  ) of bipartite graphs, where,   { ( x),  ( x)}
i (x) is the variable node degree distribution of each set and  (x )
is the check node degree distribution.
1
Decoder
Encoder
Xˆ 2  X 2
RX2
Slepian-Wolf rate region for two sources:
2
Rateless
coding
Simulation:
Corner Point:
RX1 = H(X1)
C
+
RX1 +RX2  H(X1,X2)
H(X2|X1)
B
RX2 = H(X2 | X1)
RX1
H(X1|X2) H(X1)
P=0.11
RX2=H(p)=0.5
LDPC rate=2/3, n=1000
( x)  {x ,0.7585x  0.1422 x  0.0993x }
2
3
4
8
 ( x)  {0.5 x ,0.5 x }
Correlation Model:
10
X1
BSC
p
X2
0
Rec i
Distributed source coding
 Implement the algorithm on testebed to evaluate the real energy saving
benefits (considering the power usage for encoding/decoding)
 Study the extensions of DSC

11

X1, X2 : I.I.D binary sequence; Prob [ Xi =0] = Prob [ Xi=1]=1/2.
Prob [ X1 X2 | X1 ]=p
1
 Multicasting / Broadcasting:
ber
A
BEC (2)
BEC (i)

H(X2)
Rec 2
0
Future work
RX1  H(X1|X2)
RX2  H(X2|X1)
Rec 1
1
0
2)Design a non-uniform LDPC code
 With the knowledge that X1 is present at the decoder
X2
Rateless (Fountain) Codes
Bits are transmitting over 2 different independent channels.
 Rn bits
Correlation channel
 (1-R)n bits
Wireless channel
Many sensors have highly correlated data that is
slowly varying.
How do we exploit correlation structure with lowpower algorithms?

method 2 outperforms method 1
0.
H(X2|X1)
Propose an energy-efficient method for broadcasting / multicasting
Apply distributed source coding to eliminate redundancy
Need route optimization while having load balancing