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

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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.

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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

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

Transmitted signal by the ith SN and the RN

\(h_{S_{i}R}\), \(h_{S_{i}D}\), h RD :

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

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

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

N 0 :

Energy density of the AWGN

d 0, \(d_{S_{i}R}\), \(d_{S_{i}D}\), d RD :

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

α :

Pathloss exponent

\(P_{t,S_{i}}\), P t,R :

Transmit power at the node S i and R

\(\gamma_{S_{i}R}\), \(\gamma_{S_{i}D}\), γ RD :

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

Γ SR , Γ RD :

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

C S , C R , C D :

Codeword generated at the node S i , R and D

U k , P k :

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

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

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

E enc, E dec :

Energy consumed by encoder and decoder

E elec, E amp :

Energy consumed by transmitter/receiver circuit and the amplifier

P rad :

Transmit power

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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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Authors and Affiliations

  1. Key Lab of Universal Wireless Communication, Ministry of Education, Beijing University of Posts & Telecommunications (BUPT), Beijing, China

    Yuexing Peng

  2. School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia

    Yonghui Li

  3. Guangdong Petrochemical Equipment Fault Diagnosis Key Laboratory, Guangdong University of Petrochemical Technology, Guangdong, China

    Lei Shu

  4. School of Information & Communication Engineering, BUPT, Beijing, China

    Wenbo Wang

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  1. Yuexing Peng
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  2. Yonghui Li
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  3. Lei Shu
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  4. Wenbo Wang
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Correspondence to Lei Shu.

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Peng, Y., Li, Y., Shu, L. et al. An energy-efficient clustered distributed coding for large-scale wireless sensor networks. J Supercomput 66, 649–669 (2013). https://doi.org/10.1007/s11227-012-0848-9

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