Quantized Network Coding for correlated sources

Abstract

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.

Compressive network coding for wireless sensor networks: Spatio-temporal coding and optimization design

“…4 we can derive that the relative recovery error of our scheme is much lower than the conventional spatio-temporal schemes [13][14] while they have the same compression gain (also have the same computational complexity). Besides, the performance of our scheme is better than spatial compression schemes [7][8][9] in terms of both recovery error and compression gain. Moreover, the decrease in the amount of redundant information is considerable by utilizing the spatial and temporal correlations step by step.…”

“…In the simulation figures, 'Spatial' refers to the category of compression schemes which only considers the spatial compression, such as [7][8][9]; 'Spatio-temporal' denotes the category of compression schemes which considers the spatial and temporal compression separately, such as [13][14]; 'Clustered spatio-temporal' denotes the proposed scheme in this paper. the temporal dimension when the row size of matrix Φ is fixed to 24.…”

“…This scheme possesses an energy-efficient property by reducing the total number of transmissions and receptions. Nabaee et al [8][9] constructed a data gathering technique by mining the spatial correlation of sensor data. This technique can achieve a good approximation of the original data with small amount of data received.…”

HCOR: a high-throughput coding-aware opportunistic routing for inter-flow network coding in wireless mesh networks

“…Since then, many various studies have proved the benefits of network coding [10,11] and given many coding methods to achieve the gain of network coding for different networks [12][13][14][15][16][17]. For inter-flow network coding, COPE [18] was proposed in 2008 as the first practical XOR (exclusive or)-based network coding scheme.…”

Sparse random linear network coding for data compression in WSNs

“…This framework is also applied in [5], where data reconstruction is performed progressively to reduce the decoding delay. In [6], linear combination resulting from real-field NC are quantized before transmission. The price to be paid by all these technique is larger headers and an incompatibility with classical NC.…”