Joint source-network coding for large-scale sensor networks

Cruz, Susana B.; Maierbacher, Gerhard; Barros, João

doi:10.1109/isit.2011.6034160

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…This can be done by using the 2-Hadamard transform of order q [31], which can be computed with complexity O(q log 2 q). Note that compared to similar existing algorithms [15]- [17], [32] whose complexity with respect to the Galois field size is O(q dc ), d c >> 2, our algorithm achieves a significantly reduced complexity of check node processing, which is only O(q 2 ). Overall, the computational complexity of the decoding algorithm is O(k(Nd 2 v q + L ′ 2d 2 c q 2 ) + 2N 3 ), where N is the number of variable nodes, L ′ is the number of check nodes (equal to the number of rows in the coding matrix A ′ ) and k is the number iterations.…”

Section: Complexitymentioning

confidence: 95%

Reconstruction of Network Coded Sources From Incomplete Datasets

Bourtsoulatze¹,

Thomos²,

Frossard³

2013

Preprint

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In this paper, we investigate the problem of recovering source information from an incomplete set of network coded data. We first study the theoretical performance of such systems under maximum a posteriori (MAP) decoding and derive the upper bound on the probability of decoding error as a function of the system parameters. We also establish the sufficient conditions on the number of network coded symbols required to achieve decoding error probability below a certain level. We then propose a low complexity iterative decoding algorithm based on message passing for decoding the network coded data of a particular class of statistically dependent sources that present pairwise linear correlation. The algorithm operates on a graph that captures the network coding constraints, while the knowledge about the source correlation is directly incorporated in the messages exchanged over the graph. We test the proposed method on both synthetic data and correlated image sequences and demonstrate that the prior knowledge about the source correlation can be effectively exploited at the decoder in order to provide a good reconstruction of the transmitted data in cases where the network coded data available at the decoder is not sufficient for exact decoding.

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Section: Complexitymentioning

confidence: 95%

Reconstruction of Network Coded Sources From Incomplete Datasets

Bourtsoulatze¹,

Thomos²,

Frossard³

2013

Preprint

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show abstract

“…and the matrix product F prod ( , ) in (34) is defined in (18). The constants c 1 and c 2 are also defined as follows:…”

Section: Theorem 4 Consider a Qnc Scenario Where For All V ∈ V Thementioning

confidence: 99%

“…However, one has to perform joint source network decoding in order to achieve theoretical performance limits, which may not be feasible because of its computational complexity [15]. Different solutions have been proposed to tackle this practicality issue [16][17][18], by using low-density codes and sum product algorithm [19] for decoding.…”

mentioning

confidence: 99%

Quantized Network Coding for correlated sources

Nabaee

Labeau

2014

J Wireless Com Network

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In this paper, we present a data gathering technique for sensor networks that exploits correlation between sensor data at different locations in the network. Contrary to distributed source coding, our method does not rely on knowledge of the source correlation model in each node although this knowledge is required at the decoder node. Similar to network coding, our proposed method (which we call Quantized Network Coding) propagates mixtures of packets through the network. The main conceptual difference between our technique and other existing methods is that Quantized Network Coding operates on the field of real numbers and not on a finite field. By exploiting principles borrowed from compressed sensing, we show that the proposed technique can achieve a good approximation of the network data at the sink node with only a few packets received and that this approximation gets progressively better as the number of received packets increases. We explain in the paper the theoretical foundation for the algorithm based on an analysis of the restricted isometry property of the corresponding measurement matrices. Extensive simulations comparing the proposed Quantized Network Coding to classic network coding and packet forwarding scenarios demonstrate its delay/distortion advantage.

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“…In [20], a different approach was taken, yet still sub-optimal in terms of the required rates. Alternatively, in [21,22] the authors suggest a sum-product-based algorithm, nevertheless, in this case, efficient decoding requires a network with bounded degree nodes. Hence, our goal in this work is to design efficient coding schemes which are both rate-optimal and applicable to general network topology.…”

Section: Introductionmentioning

confidence: 99%

Efficient Joint Network-Source Coding for Multiple Terminals with Side Information

Avin¹,

Borokhovich²,

Cohen³

et al. 2011

Preprint

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Consider the problem of source coding in networks with multiple receiving terminals, each having access to some kind of side information. In this case, standard coding techniques are either prohibitively complex to decode, or require network-source coding separation, resulting in sub-optimal transmission rates. To alleviate this problem, we offer a joint network-source coding scheme based on matrix sparsification at the code design phase, which allows the terminals to use an efficient decoding procedure (syndrome decoding using LDPC), despite the network coding throughout the network. Via a novel relation between matrix sparsification and rate-distortion theory, we give lower and upper bounds on the best achievable sparsification performance. These bounds allow us to analyze our scheme, and, in particular, show that in the limit where all receivers have comparable side information (in terms of conditional entropy), or, equivalently, have weak side information, a vanishing density can be achieved. As a result, efficient decoding is possible at all terminals simultaneously. Simulation results motivate the use of this scheme at non-limiting rates as well.

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Joint source-network coding for large-scale sensor networks

Cited by 6 publications

References 11 publications

Reconstruction of Network Coded Sources From Incomplete Datasets

Reconstruction of Network Coded Sources From Incomplete Datasets

Quantized Network Coding for correlated sources

Efficient Joint Network-Source Coding for Multiple Terminals with Side Information

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