Dense sensor networks that are also energy efficient: when 'more' is 'less'

Hong, Y.-W. Peter; Scaglione, Anna; Manohar, Rajit

doi:10.1109/milcom.2005.1606138

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…DSC plays a particularly important role in dense wireless sensor networks where the number of sensors can be very large. Several works focusing on the scalability properties of sensor networks have appeared recently [28,31,45,46,49]. The main issue is to understand if the use of DSC can be sufficient to compensate for the increase of data transmission as the number of sensors grows.…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

“…More optimistic results were recently proposed in [31,49], where it was shown that, for a given distortion, an upper-bound (independent of N ) on the total information rate can be given. Recent works have also shown that if the observed phenomenon can be represented with a finite number of degrees of freedom, it is then possible to efficiently trade-off the density of sensors with the sensing resolution at each sensor [28,29] (Notice that the approach presented in [29] requires some communication between neighbouring sensors).…”

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Distributed source coding

Pearlman

Said

2011

Digital Signal Compression

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I want to thank all my friends from Switzerland (Guillaume, Stefano, Ron, Cédric,. . . ) that have come to visit me in London and have made my travels back home a real enjoyment every time.Obviously, I am infinitely thankful to all my family and in particular to my parents. They have always supported me in my decisions and have done all they could do to help me reach my objectives.Finally, you would not be reading this thesis if it was not for one person who convinced me to jump on the opportunity and move to London, whilst being aware that it would make her life much more complicated. I will probably never find the words to express all my gratitude to her. At least, I can start with a: Merci beaucoup Mélanie.

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Section: Wireless Sensor Networkmentioning

confidence: 99%

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confidence: 99%

Distributed source coding

Pearlman

Said

2011

Digital Signal Compression

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A Level-Crossing Flash Asynchronous Analog-to-Digital Converter

Akopyan

Manohar

Apsel

12th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC'06)

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Distributed sensor networks, human body implants, and hand-held electronics have tight energy budgets that necessitate low power circuits. Most of these devices include an analog-to-digital converter (ADC) to process analog signals from the physical world. We describe a new topology for an asynchronous analog-to-digital converter, dubbed LCF-ADC, that has several major advantages over previously-designed ADCs, including reduced energy consumption and/or a simplification of the analog circuits required for its implementation.In this paper we describe the design of the LCF-ADC architecture, and present simulation results that show low power consumption. We discuss both theoretical considerations that determine the performance of our ADC as well as a proposed implementation. Comparisons with previously designed asynchronous analog-to-digital converters show the benefits of the LCF-ADC architecture. In 180 nm CMOS, our ADC is expected to consume 43 µW at 160 kHz, and 438 µW at 5 MHz.

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Distributed Sampling and Compression of Scenes with Finite Rate of Innovation in Camera Sensor Networks

Gehrig

Dragotti

Data Compression Conference (DCC'06)

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We study the problem of distributed sampling and compression in sensor networks when the sensors are digital cameras that acquire a 3-D visual scene of interest from different viewing positions. We assume that sensors cannot communicate among themselves, but can process their acquired data and transmit it to a common central receiver. The main task of the receiver is then to reconstruct the best possible estimation of the original scene and the natural issue, in this context, is to understand the interplay in the reconstruction between sampling and distributed compression.In this paper, we show that if the observed scene belongs to the class of signals that can be represented with a finite number of parameters, we can determine the minimum number of sensors that allows perfect reconstruction of the scene. Then, we present a practical distributed coding approach that leads to a rate-distortion behaviour at the decoder that is independent of the number of sensors, when this number increases beyond the critical sampling. In other words, we show that the distortion at the decoder does not depend on the number of sensors used, but only on the total number of bits that can be transmitted from the sensors to the receiver.

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Dense sensor networks that are also energy efficient: when 'more' is 'less'

Cited by 5 publications

References 15 publications

Distributed source coding

Distributed source coding

A Level-Crossing Flash Asynchronous Analog-to-Digital Converter

Distributed Sampling and Compression of Scenes with Finite Rate of Innovation in Camera Sensor Networks

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