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


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.


Wireless sensor networks Distributed turbo coding Joint coding Energy efficiency 



Additive white Gaussian noise


Bose–Chaudhuri–Hocquenghem code


Bit error rate


Block error probability


Binary phase-shift keying


Cooperative cluster


Cluster head


Connected K-neighborhood


Coder and decoder


Cyclic redundancy check code


Channel state information


Carrier sense multiple access


Conditional weight enumerating function


Distributed channel coding


Distributed multiple-sensor cooperative turbo coding


Distributed turbo code


Distributed turbo product code


Forward error control


Hybrid energy efficient distributed clustering


Hamming code


Input-redundancy weight enumerating function


Low density parity check


Low energy adaptive clustering hierarchy


Log-likelihood ratio


Maximum a posteriori probability


Modulation and coding scheme


Multiple-input multiple-output


Parallel concatenated block code


Parallel concatenated convolutional code


Parallel concatenated hybrid code


Robust clustering with cooperative transmission


Relay node


Reed–Solomon code


Recursive systematic convolutional code


Soft-information relaying


Soft-input soft-output decoding—minimum distance search


Source node


Time-division multiple access


Time slot


Wireless sensor network


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


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


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


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


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


Input-redundancy weight enumerating function of C S


Number of codewords with information weight w and parity weight j


Conditional weight enumerating function of codeword C S


Conditional weight enumerating function of codeword C D


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


Transmit power



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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