### Abstract

In this paper we consider the problem of estimating a vector source in a sensor network, where each sensor in the network makes a local observation of this vector source. We assume that the local observations are distorted and noise corrupted versions of the original signal source, i.e., the observation of m-th sensor y
_{m} and the signal source x are related through y
_{m} = H
_{m}x + n
_{m} where the matrix H
_{m} represents the distortion (filtering) effect and n
_{m} denotes the additive noise. Each sensor encodes its observation separately and sends the encoded message to a central processor (CP), whose task is to form an estimate of source x. Our objective is to design quantizers such that the distortion in the source estimate formed by the CP is minimized, subject to the sum quantization rate constraint. Realizing the resemblance of this problem to the classical CEO problem [2] in multiterminal source coding, and inspired by the work in [3], we propose a successive coding and decoding strategy, based on Wyner-Ziv coding concept. Numerical evaluation of the sum rate-distortion performance of the proposed algorithm reveals an interesting trade off between sum rate, target distortion, and number of sensor nodes which are participating in the sequential coding.

Original language | English (US) |
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Title of host publication | IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC |

State | Published - 2007 |

Externally published | Yes |

Event | 8th IEEE Signal Processing Advances in Wireless Communications, SPAWC 2007 - Helsinki, Finland Duration: Jun 17 2007 → Jun 20 2007 |

### Other

Other | 8th IEEE Signal Processing Advances in Wireless Communications, SPAWC 2007 |
---|---|

Country | Finland |

City | Helsinki |

Period | 6/17/07 → 6/20/07 |

### Fingerprint

### ASJC Scopus subject areas

- Signal Processing
- Engineering(all)

### Cite this

*IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC*[4401395]

**Sequential source coding with side information for sensor networks.** / Vosoughi, Azadeh; Scaglione, Anna.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC.*, 4401395, 8th IEEE Signal Processing Advances in Wireless Communications, SPAWC 2007, Helsinki, Finland, 6/17/07.

}

TY - GEN

T1 - Sequential source coding with side information for sensor networks

AU - Vosoughi, Azadeh

AU - Scaglione, Anna

PY - 2007

Y1 - 2007

N2 - In this paper we consider the problem of estimating a vector source in a sensor network, where each sensor in the network makes a local observation of this vector source. We assume that the local observations are distorted and noise corrupted versions of the original signal source, i.e., the observation of m-th sensor y m and the signal source x are related through y m = H mx + n m where the matrix H m represents the distortion (filtering) effect and n m denotes the additive noise. Each sensor encodes its observation separately and sends the encoded message to a central processor (CP), whose task is to form an estimate of source x. Our objective is to design quantizers such that the distortion in the source estimate formed by the CP is minimized, subject to the sum quantization rate constraint. Realizing the resemblance of this problem to the classical CEO problem [2] in multiterminal source coding, and inspired by the work in [3], we propose a successive coding and decoding strategy, based on Wyner-Ziv coding concept. Numerical evaluation of the sum rate-distortion performance of the proposed algorithm reveals an interesting trade off between sum rate, target distortion, and number of sensor nodes which are participating in the sequential coding.

AB - In this paper we consider the problem of estimating a vector source in a sensor network, where each sensor in the network makes a local observation of this vector source. We assume that the local observations are distorted and noise corrupted versions of the original signal source, i.e., the observation of m-th sensor y m and the signal source x are related through y m = H mx + n m where the matrix H m represents the distortion (filtering) effect and n m denotes the additive noise. Each sensor encodes its observation separately and sends the encoded message to a central processor (CP), whose task is to form an estimate of source x. Our objective is to design quantizers such that the distortion in the source estimate formed by the CP is minimized, subject to the sum quantization rate constraint. Realizing the resemblance of this problem to the classical CEO problem [2] in multiterminal source coding, and inspired by the work in [3], we propose a successive coding and decoding strategy, based on Wyner-Ziv coding concept. Numerical evaluation of the sum rate-distortion performance of the proposed algorithm reveals an interesting trade off between sum rate, target distortion, and number of sensor nodes which are participating in the sequential coding.

UR - http://www.scopus.com/inward/record.url?scp=48049094377&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=48049094377&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:48049094377

SN - 1424409551

SN - 9781424409556

BT - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

ER -