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The Journal of Supercomputing

, Volume 66, Issue 2, pp 649–669 | Cite as

An energy-efficient clustered distributed coding for large-scale wireless sensor networks

  • Yuexing Peng
  • Yonghui Li
  • Lei ShuEmail author
  • Wenbo Wang
Article

Abstract

A wireless sensor network (WSN) usually consists of a large number of battery-powered low-cost sensors with limited data collection and processing capacity. In order to prolong the lifetime of the WSN with a target error performance, a novel clustered distributed coding framework, referred to as distributed multiple-sensor cooperative turbo coding (DMSCTC), is developed for a large-scale WSN with sensor grouped in cooperative cluster. In the proposed DMSCTC scheme, a simple forward error correction is employed at each sensor and a simple multi-sensor joint coding is adopted at the cluster head, while complicated joint iterative decoding is implemented only at the data collector. The proposed DMSCTC scheme achieves extra distributed coding gain and cooperative spatial diversity without introducing extra complexity burden on the sensors by transferring the complicated joint decoding process to the data collector. With the proposed scheme, the WSN can achieve the target error performance with less power consumption, thus prolonging its lifetime. The error performance and energy efficiency of the proposed DMSCTC scheme are analyzed, and followed by Monte Carlo simulations. Both analytical and simulation results show that the DMSCTC can substantially improve the energy efficiency of the clustered WSN.

Keywords

Wireless sensor networks Distributed turbo coding Joint coding Energy efficiency 

Abbreviations

AWGN

Additive white Gaussian noise

BCH

Bose–Chaudhuri–Hocquenghem code

BER

Bit error rate

BLEP

Block error probability

BPSK

Binary phase-shift keying

CC

Cooperative cluster

CH

Cluster head

CKN

Connected K-neighborhood

CODEC

Coder and decoder

CRC

Cyclic redundancy check code

CSI

Channel state information

CSMA

Carrier sense multiple access

CWEF

Conditional weight enumerating function

DCC

Distributed channel coding

DMSCTC

Distributed multiple-sensor cooperative turbo coding

DTC

Distributed turbo code

DTPC

Distributed turbo product code

FEC

Forward error control

HEED

Hybrid energy efficient distributed clustering

HM

Hamming code

IRWEF

Input-redundancy weight enumerating function

LDPC

Low density parity check

LEACH

Low energy adaptive clustering hierarchy

LLR

Log-likelihood ratio

MAP

Maximum a posteriori probability

MCS

Modulation and coding scheme

MIMO

Multiple-input multiple-output

PCBC

Parallel concatenated block code

PCCC

Parallel concatenated convolutional code

PCHC

Parallel concatenated hybrid code

RCCT

Robust clustering with cooperative transmission

RN

Relay node

RS

Reed–Solomon code

RSC

Recursive systematic convolutional code

SIR

Soft-information relaying

SISO-MDS

Soft-input soft-output decoding—minimum distance search

SN

Source node

TDMA

Time-division multiple access

TS

Time slot

WSN

Wireless sensor network

Notations

\(\boldsymbol{x}_{S_{i}}\), xR

Transmitted signal by the ith SN and the RN

\(h_{S_{i}R}\), \(h_{S_{i}D}\), hRD

CIR of the S i -to-R, S i -to-D, R-to-D link

\(\boldsymbol{n}_{S_{i}R}\), \(\boldsymbol{n}_{S_{i}D}\), nRD

AWGN of the S i -to-R, S i -to-D, R-to-D link

N0

Energy density of the AWGN

d0, \(d_{S_{i}R}\), \(d_{S_{i}D}\), dRD

Reference distance, and the distance between the nodes S i , R and D

α

Pathloss exponent

\(P_{t,S_{i}}\), Pt,R

Transmit power at the node S i and R

\(\gamma_{S_{i}R}\), \(\gamma_{S_{i}D}\), γRD

Instantaneous SNR of the S i -to-R, S i -to-D, R-to-D link

ΓSR, ΓRD

Average SNR of the S i -to-R and R-to-D link

CS, CR, CD

Codeword generated at the node S i , R and D

Uk, Pk

Systematic bits and parity bits generated at the kth SN

\(\boldsymbol{U}'_{k}\), \(\boldsymbol{P}'_{k}\)

Systematic bits and parity bits generated at the RN

\(\boldsymbol{L}_{S_{i}R}\), \(\boldsymbol{L}_{S_{i}D}\), LRD

LLR information of the link of S i -to-R, S i -to-D, R-to-D

\(\boldsymbol{L}^{(S)}_{S_{k}}\), \(\boldsymbol{L}^{(P)}_{S_{k}}\), \(\boldsymbol{L}^{(e)}_{S_{k}}\), \(\boldsymbol{L}^{(a)}_{S_{k}}\)

LLR information of the systematic bits and parity bits, the exterior information and the a priori information of the kth codeword generated at the kth SN

\(\boldsymbol{L}^{(S)}_{R_{k}}\), \(\boldsymbol{L}^{(P)}_{R_{k}}\), \(\boldsymbol{L}^{(e)}_{R_{k}}\), \(\boldsymbol{L}^{(a)}_{R_{k}}\)

LLR information of the systematic bits and parity bits, the exterior information and the a priori information of the codeword generated at RN

\(A^{C_{S}}(W,Z_{S})\)

Input-redundancy weight enumerating function of C S

\(A^{C_{S}}_{w,j}\)

Number of codewords with information weight w and parity weight j

\(A^{C_{S}}_{w}(Z_{S})\)

Conditional weight enumerating function of codeword C S

\(A^{C_{D}}_{w}(Z_{S},Z_{R})\)

Conditional weight enumerating function of codeword C D

P(e|γ)

Conditional block error ratio

\(E_{T_{x}}(N,d)\), \(E_{R_{x}}(N)\)

Energy consumed by the transmitter circuitry and the receiver circuitry

Eenc, Edec

Energy consumed by encoder and decoder

Eelec, Eamp

Energy consumed by transmitter/receiver circuit and the amplifier

Prad

Transmit power

Notes

Acknowledgements

Authors would like to thank Professor Mei Yang at the University of Nevada for providing the energy consumption data of several coding schemes.

This work was supported in part by the National Natural Science Foundation of China under Grant 61171106, National Basic Research Program of China (973 Program) under Grant 2012CB316005, National Key Technology R&D Program of China under Grant 2012ZX03-004-005, Research Fund for the Doctoral Program of Higher Education under Grant 20090005120002, and the fundamental research funds for the central universities.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Key Lab of Universal Wireless Communication, Ministry of EducationBeijing University of Posts & Telecommunications (BUPT)BeijingChina
  2. 2.School of Electrical and Information EngineeringThe University of SydneySydneyAustralia
  3. 3.Guangdong Petrochemical Equipment Fault Diagnosis Key LaboratoryGuangdong University of Petrochemical TechnologyGuangdongChina
  4. 4.School of Information & Communication EngineeringBUPTBeijingChina

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