## Power, Spatio-Temporal Bandwidth, and Distortion in Large Sensor Networks

Citations: | 6 - 0 self |

### BibTeX

@MISC{Gastpar_power,spatio-temporal,

author = {Michael Gastpar and Martin Vetterli},

title = {Power, Spatio-Temporal Bandwidth, and Distortion in Large Sensor Networks},

year = {}

}

### OpenURL

### Abstract

Abstract—For a class of sensor networks, the task is to monitor an underlying physical phenomenon over space and time through an imperfect observation process. The sensors can communicate back to a central data collector over a noisy channel. The key parameters in such a setting are the fidelity (or distortion) at which the underlying physical phenomenon can be estimated by the data collector, and the cost of operating the sensor network. This is a network joint source-channel communication problem, involving both compression and communication. It is well known that these two tasks may not be addressed separately without sacrificing optimality, and the optimal performance is generally unknown. This paper presents a lower bound on the best achievable end-to-end distortion as a function of the number of sensors, their total transmit power, the number of degrees of freedom of the underlying source process, and the spatio-temporal communication bandwidth. Particular coding schemes are studied, and it is shown that in some cases, the lower bound is tight in a scaling-law sense. By contrast, it is shown that the standard practice of separating source from channel coding may incur an exponential penalty in terms of communication resources, as a function of the number of sensors. Hence, such code designs effectively prevent scalability. Finally, it is outlined how the results extend to cases involving missing synchronization and channel fading. Index Terms—CEO problem, information theory, joint sourcechannel coding, sensor networks, separation theorem.

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Citation Context ...endering of the considered sensor network. There are L = 4 underlying physical phenomena (depicted by the empty circles), M =10 sensing devices (the black disks), and N =4base stations (the squares). =-=[5]-=-, [6], and extensions thereof in [7]. This paper generalizes this analysis to multiple data sources and multiple base stations, with random parameters both in the source observation mechanism and in t... |

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Citation Context ...tion theory, joint sourcechannel coding, sensor networks, separation theorem. I. INTRODUCTION PERFORMANCE limits for various types of sensor networks are currently under investigation, see, e.g., [1]–=-=[4]-=-. While a majority of the efforts concerns either the sampling and compression problem, or the capacity problem, the present paper investigates the joint problem of compression and communication. The ... |

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2 | Vetterli received the Dipl. El.-Ing. degree from ETH Zurich (ETHZ), Switzerland, in 1981, the MS degree from Stanford University - Martin - 1986 |

1 | On the role of feedback in large sensor networks - Gastpar - 2004 |

1 | received the Dipl. El.-Ing. degree from the Swiss Federal Institute of Technology - Gastpar - 1997 |

1 | was a Research Assistant at Stanford and EPFL, and has worked for Siemens and AT&T Bell Laboratories - He - 1986 |

1 | received the Best Paper Award of EURASIP in 1984 for his paper on multidimensional subband coding, the Research Prize of the Brown Bovery Corporation (Switzerland) in 1986 for his doctoral thesis, and the - He - 1996 |