Approximate Decoding Approaches for Network Coded Correlated Data

This paper considers a framework where data from correlated sources are transmitted with help of network coding in ad-hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth bottlenecks. We first show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples about the possible of our algorithms that can be deployed in sensor networks and distributed imaging applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our low complexity solution for delivery of correlated data sources.

Network coding of correlated data with approximate decoding

Pascal Frossard, Hyung Gon Park, Nikolaos Thomos

We consider the problem of distributed delivery of correlated data from sensors in ad hoc network topologies. We propose to use network coding in order to exploit the path diversity in the network for efficient delivery of the sensor information. We further show that the correlation between the data sources can be exploited at receivers for efficient approximate decoding when the number of received data packets is not sufficient for perfect decoding. We analyze how the decoding performance is influenced by the choice of the network coding parameters and in particular by the size of finite fields. We determine the optimal field size that maximizes the expected decoding performance, which actually represents a trade-off between information loss incurred by quantizing the source data and the error probability in the reconstructed data. Moreover, we show that the decoding performance improves when the accuracy of the correlation estimation increases. We have illustrated our network coding based algorithms with approximate decoding in sensor networks and video coding applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our solution for distributed delivery of correlated information in ad hoc networks.

2010

Adaptive Distributed Algorithms for Power-Efficient Data Gathering in Sensor Networks

Jugoslava Acimovic, Razvan Cristescu

In this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributed algorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution wavelet-based algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide distributed algorithms that perform network division into groups of different sizes. The distribution of the group sizes within the network is the result of an optimal trade-off between the local communication inside each group needed to perform decorrelation, the communication needed to bring the processed data (coefficients) to the sink and the local computation cost, which grows as the network becomes larger. Our experimental results show clearly that important gains in power consumption can be obtained with respect to the case of not performing any distributed decorrelating processing.

2005

Transmission of Correlated Information Sources with Network Coding

Pascal Frossard, Hyung Gon Park, Nikolaos Thomos

This paper addresses the problem of the distributed delivery of correlated data sources with help of network coding. Network coding provides an alternative to routing algorithms and offers improved system performance, robustness and throughput, with no need of deploying sophisticated routing strategies. However, the performance is directly driven by the number of innovative data packets that reach the receiver. If the number of received innovative data packets is significantly small, the decoder cannot perfectly recover the transmitted information. However, we show that the correlation between the data sources can be used at decoder for effective approximate decoding. We analytically investigate the impact of the network coding algorithm, and in particular, of the size of finite fields on the decoding performance. Then, we determine an optimal field size that minimizes the expected decoding error, which represents a trade-off between quantization of the source data and probability of decoding error. The network coding with approximate decoding algorithm is implemented in illustrative multimedia streaming and sensor network applications. In both cases, the experimental results confirm the field size analysis and illustrate the effectiveness of approximate decoding of correlated data.

2010