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Practical sourcenetwork decoding
 in ISWCS 2009, 2009
"... Abstract—When correlated sources are to be communicated over a network to more than one sink, joint sourcenetwork coding is, in general, required for information theoretically optimal transmission. Whereas on the encoder side simple randomized schemes based on linear codes suffice, the decoder is r ..."
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Cited by 7 (2 self)
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Abstract—When correlated sources are to be communicated over a network to more than one sink, joint sourcenetwork coding is, in general, required for information theoretically optimal transmission. Whereas on the encoder side simple randomized schemes based on linear codes suffice, the decoder is required to perform joint sourcenetwork decoding which is computationally expensive. Focusing on maximum aposteriori decoders (or, in the case of continuous sources, conditional mean estimators), we show how to exploit (structural) knowledge about the network topology as well as the source correlations giving rise to an efficient decoder implementation (in some cases even with linear dependency on the number of nodes). In particular, we show how to statistically represent the overall system (including the packets) by a factorgraph on which the sumproduct algorithm can be run. A proofofconcept is provided in the form of a working decoder for the case of three sources and two sinks. I.
LowComplexity Coding and SourceOptimized Clustering for LargeScale Sensor Networks
, 2008
"... We consider the distributed source coding problem in which correlated data picked up by scattered sensors has to be encoded separately and transmitted to a common receiver, subject to a ratedistortion constraint. Although neartooptimal solutions based on Turbo and LDPC codes exist for this proble ..."
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Cited by 4 (1 self)
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We consider the distributed source coding problem in which correlated data picked up by scattered sensors has to be encoded separately and transmitted to a common receiver, subject to a ratedistortion constraint. Although neartooptimal solutions based on Turbo and LDPC codes exist for this problem, in most cases the proposed techniques do not scale to networks of hundreds of sensors. We present a scalable solution based on the following key elements: (a) distortionoptimized index assignments for lowcomplexity distributed quantization, (b) sourceoptimized hierarchical clustering based on the KullbackLeibler distance and (c) sumproduct decoding on specific factor graphs exploiting the correlation of the data.
Feedback Power Control Strategies in Wireless Sensor Networks with Joint Channel Decoding
 SENSORS
, 2009
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Decentralized detection in clustered sensor networks
 IEEE Trans. Aerosp. Electron. Syst
"... We investigate decentralized detection in clustered sensor networks with hierarchical multilevel fusion. We focus on simple majoritylike fusion strategies, leading to closedform analytical performance evaluation. The sensor nodes observe a binary phenomenon and transmit their own data to an acces ..."
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Cited by 2 (0 self)
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We investigate decentralized detection in clustered sensor networks with hierarchical multilevel fusion. We focus on simple majoritylike fusion strategies, leading to closedform analytical performance evaluation. The sensor nodes observe a binary phenomenon and transmit their own data to an access point (AP), possibly through intermediate fusion centers (FCs). We investigate the impact of uniform and nonuniform clustering on the system performance, evaluated in terms of probability of decision error on the phenomenon status at the AP. Our results show that, under a majoritylike fusion rule, uniform clustering leads to the minimum performance degradation, which depends only on the number of decision levels rather than on the specific clustered topology. We then extend our approach, taking into account the impact of spatial variations of the phenomenon, noisy communication links, and weighed fusion rules. Finally the proposed distributed detection schemes are characterized with simulation and experimental results (relative to IEEE 802.15.4based networks), which confirm the analytical predictions.
Research Article LowComplexity OneDimensional Edge Detection in Wireless Sensor Networks
"... License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In various wireless sensor network applications, it is of interest to monitor the perimeter of an area of interest. For example, one may need to check if there is a le ..."
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License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In various wireless sensor network applications, it is of interest to monitor the perimeter of an area of interest. For example, one may need to check if there is a leakage of a dangerous substance. In this paper, we model this as a problem of onedimensional edge detection, that is, detection of a spatially nonconstant onedimensional phenomenon, observed by sensors which communicate to an access point (AP) through (possibly noisy) communication links. Two possible quantization strategies are considered at the sensors: (i) binary quantization and (ii) absence of quantization. We first derive the minimum mean square error (MMSE) detection algorithm at the AP. Then, we propose a simplified (suboptimum) detection algorithm, with reduced computational complexity. Noisy communication links are modeled either as (i) binary symmetric channels (BSCs) or (ii) channels with additive white Gaussian noise (AWGN). 1. Introduction and Related
SourceOptimized Clustering and Distributed Quantization for LargeScale Sensor Networks
 ACCEPTED FOR THE ACM TRANSACTIONS ON SENSOR NETWORKS, JUNE 2008
, 2008
"... We consider the distributed source coding problem in which correlated measurements picked up by scattered sensors have to be encoded separately and transmitted to a common receiver, subject to a ratedistortion constraint. Although neartooptimal solutions based e.g. on LDPC codes have been propose ..."
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We consider the distributed source coding problem in which correlated measurements picked up by scattered sensors have to be encoded separately and transmitted to a common receiver, subject to a ratedistortion constraint. Although neartooptimal solutions based e.g. on LDPC codes have been proposed for this problem, in most cases the devised techniques do not scale to networks of hundreds of sensors. We present a scalable solution based on the following key elements: (a) distortionoptimized index assignments for lowcomplexity distributed quantization, (b) sourceoptimized hierarchical clustering based on the Kullback Leibler distance and (c) sumproduct decoding on specific factor graphs exploiting the correlation of the data. Simulation results underline the effectiveness of the proposed techniques.
Chapter 1 REDUCEDCOMPLEXITY DECENTRALIZED DETECTION OF SPATIALLY NONCONSTANT PHENOMENA
"... In this paper, we study sensor networks with decentralized detection of a spatially nonconstant phenomenon, whose status might change independently from sensor to sensor. In particular, we consider binary phenomena characterized by a fixed number of status changes (from state “0 ” to state “1”) acr ..."
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In this paper, we study sensor networks with decentralized detection of a spatially nonconstant phenomenon, whose status might change independently from sensor to sensor. In particular, we consider binary phenomena characterized by a fixed number of status changes (from state “0 ” to state “1”) across the sensors. This is realistic for sensor networking scenarios where abrupt spatial variations of the phenomenon under observation need to be estimated, e.g., an abrupt temperature increase, as could be the case in the presence of a fire in a specific zone of the monitored surface. In such scenarios, we derive the minimum mean square error (MMSE) fusion algorithm at the access point (AP). The improvement brought by the use of quantization at the sensors is investigated. Finally, we derive simplified (suboptimum) fusion algorithms at the AP, with a computational complexity lower than that of schemes with MMSE fusion at the AP. Keywords: Decentralized detection, nonconstant phenomena, minimum mean square error (MMSE), simplified fusion rule, computational complexity. 1.