Source-Channel Communication in Sensor Networks

By Michael C. Gastpar, Martin Vetterli. International Symposium on Information Processing in Sensor Networks, 2003

307 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f16a60edc8a268e1a133128b61f3869467f23e25

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

On the capacity of large Gaussian relay networks (2005, background): “Remark 4: Extensions of this result, involving multiple underlying sources, multiple data collection points and fading effects, and addressing the tradeoff between the total sensor power and the achievable distortion more explicitly, have been presented in [30]–[32], [20].”
Spatial correlation-based collaborative medium access control in wireless sensor networks (2006, methodology): “For example, in [6], a joint source-channel coding paradigm is proposed for optim al performance in a class of sensor networks.”
Stochastic consensus over noisy networks with Markovian and arbitrary switches (2010, background): “It was shown in Gastpar and Vetterli (2003) that for a Gaussian sensor network, where multiple sensors measure a random scalar Gaussian field in noise and forward their noisy measurements to a fusion center for reconstruction of the source, the analog forwarding scheme is asymptotically optimal and…”
Compressive wireless sensing (2006, background): “CWS is based on a distributed matched sourcechannel communication architecture and is inspired by recent results in wireless communications [4, 5, 10, 1, 12] and compressive sampling theory [2.”
Spatio-temporal correlation: theory and applications for wireless sensor networks (2004, methodology): “For example, in [6], a joint source-channel coding paradigm is proposed for optimal performance in a class of sensor networks.”
Cooperative Communications in Resource-Constrained Wireless Networks (2007, background): “Consider an equivalent of the AF relaying scheme [33] where each sensor transmits an amplified version of its observation through the Gaussian multiple access channel.”
Uncoded transmission is exactly optimal for a simple Gaussian "sensor" network (2007, methodology): “In this section, we merely quote the simple bound used previously in [3], [ 4 ], [5].”
On Capacity Under Receive and Spatial Spectrum-Sharing Constraints (2007, background): “Proof [Proof of Theorem 7]: For the achievability, we proceed along the lines of [32], [33] by analyzing the following coding scheme: During time slot , sensor transmits”
Compressed Sensing for Networked Data (2008, methodology): “While this task can be accomplished in a number of ways, one particularly attractive technique leverages the theory of CS and corresponds to delivering random projections of the sensor network data to the FC by exploiting recent results on uncoded (analog) coherent transmission schemes in wireless s”