For large-scale sensor networks deployed for data gathering, energy efficiency is critical. Eliminating the data correlation is a promising technique for energy efficiency. Compressive Data Gathering (CDG) [8], which ...
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ISBN:
(纸本)9781424492688
For large-scale sensor networks deployed for data gathering, energy efficiency is critical. Eliminating the data correlation is a promising technique for energy efficiency. Compressive Data Gathering (CDG) [8], which employs distributed coding to compress data correlation, is an important approach in this area. However, the CDG scheme uses a uniform pattern in data transmission, where all nodes transmit the same amount of data regardless of their hop distances to the sink, making it inefficient in saving transmission costs in 2-D networks. In this paper, the Major Coefficient Recovery (MCR) scheme is proposed, where the Discrete Cosine Transformation (DCT) is applied in a distributed fashion to the original sensed data. A non-uniform data transmission pattern is proposed by exploiting the energy concentration property of DCT and QR decomposition techniques so that sensors with larger hop-count can transmit fewer messages for network energy efficiency. The sink node recovers only the major coefficients of the DCT to reconstruct the original data accurately. MCR reduces the transmission overhead to O(kn - k(2)), an improvement by O(log n) over CDG in both 1-D and 2-D cases. The recovery performance of MCR is verified by extensive simulations.
It is well known that for discrete-time, stationary sources, most lossy source coding techniques have operational rate-distortion functions that approach the Shannon rate-distortion function with respect to squared er...
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ISBN:
(纸本)9781467325790
It is well known that for discrete-time, stationary sources, most lossy source coding techniques have operational rate-distortion functions that approach the Shannon rate-distortion function with respect to squared error to within an additive constant as distortion approaches zero. With the goal of investigating similar phenomena for continuous-time sources, this paper investigates the low-distortion performance of distributed coding of continuous-time, stationary, Gaussian sources based on high-rate sampling. It is found that for bandlimited sources and nonbandlimited sources whose spectra have sufficiently light, e.g., exponentially decreasing, tails, distributed source coding is asymptotically as good as centralized coding in the small distortion regime. On the other hand, for spectra with tails that decay as a power (greater than one) of frequency, it is found that for small distortions the distributed rate-distortion function is a constant times larger than the Shannon rate-distortion, where the constant decreases as the power increases. For example, it is approximately 1.2 when the power is 2. The conclusion is that for a stationary Gaussian source and asymptotically small distortion, the ratio of the distributed to centralized rate-distortion function is a function of the weight of the tail of the source spectrum. In the process of finding the ratio, the low distortion form of the centralized rate-distortion function is found for sources whose spectra have exponential and power law tails.
Formal process languages inheriting the concurrency and communication features of process algebras are convenient formalisms to model distributed applications, especially when they are equipped with formal verificatio...
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ISBN:
(纸本)9781479984909
Formal process languages inheriting the concurrency and communication features of process algebras are convenient formalisms to model distributed applications, especially when they are equipped with formal verification tools (e.g., model-checkers) to help hunting for bugs early in the development process. However, even starting from a fully verified formal model, bugs are likely to be introduced while translating (generally by hand) the concurrent model-which relies on high-level and expressive communication primitives-into the distributed implementation-which often relies on low-level communication primitives. In this paper, we present DLC, a compiler that enables distributed code to be generated from models written in a formal process language called LNT, which is equipped with a rich verification toolbox named CADP. The generated code can be either executed in an autonomous way (i.e., without requiring additional code to be defined by the user), or connected to external software through user-modifiable C functions. We present an experiment where DLC generates a distributed implementation from the LNT model of the Raft consensus algorithm.
A scheme of key frame encoding/decoding is presented for video coding by means of distributed compressive video sensing. The proposed method is referred to as video compressive sensing by intra predictive coding. The ...
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ISBN:
(纸本)9781479920280
A scheme of key frame encoding/decoding is presented for video coding by means of distributed compressive video sensing. The proposed method is referred to as video compressive sensing by intra predictive coding. The experimental result shows that the proposed method reduces the data size of the key frame information transmitted between an encoder and a decoder. It is demonstrated that the method outperforms the conventional DCVS with respect to the code bit size and noise immunity.
Inspired by recent results showing that Wyner-Ziv coding using a combination of source and channel coding may be more efficient than pure channel coding, we have applied coset codes for the source coding part in the t...
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ISBN:
(纸本)9781424479948
Inspired by recent results showing that Wyner-Ziv coding using a combination of source and channel coding may be more efficient than pure channel coding, we have applied coset codes for the source coding part in the transform domain for Wyner-Ziv coding of video. The framework is a mixed-resolution approach where reduced encoding complexity is achieved by low resolution encoding of non-reference frames and regular encoding of the reference frames. Different from our previous works, no motion estimation is carried at the encoder, neither for the low resolution frames nor the reference frames. The entropy coders of the H.264/AVC codec were tuned to improve their performance for encoding the cosets. An encoding mode with lowest encoding complexity than H.264/AVC intra mode is achieved. Experimental results show a competitive rate-distortion performance especially at low bit rates.
Considering data recovery challenges in the cloud infrastructure, this article focuses on application of Local Reconstruction Codes (LRC) and Regenerating Codes (RC) in RAID, and suggests data placement methods for im...
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ISBN:
(纸本)9781450348843
Considering data recovery challenges in the cloud infrastructure, this article focuses on application of Local Reconstruction Codes (LRC) and Regenerating Codes (RC) in RAID, and suggests data placement methods for improved recovery performance in case of drive or cluster node failure. Besides, this work presents an efficient method of scaling regenerating codes that allows for RC use in dynamic clusters and optimal recovery speeds.
In this paper, we ignore transmission issues and focus on the total number of bits to transmit to the collector to form a reconstruction of the field with a given MSE. We assume that all sensors can transmit bits to t...
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ISBN:
(纸本)0780376293
In this paper, we ignore transmission issues and focus on the total number of bits to transmit to the collector to form a reconstruction of the field with a given MSE. We assume that all sensors can transmit bits to the collector without error. With this assumption, with total number of bits as the cost measure, and with the style of coding, it can be argued that sensor-to-sensor relaying offers no advantages. This problem is similar to image coding and transmission, except that the quantization, encoding and transmission are constrained to take place separately at each sensor (pixel location), in contrast to traditional image coding and transmission, wherein the entire image is available for quantization, encoding, and transmission. Due to the need to separately encode values from separate sensors, we pursue a Slepian-Wolf style coding approach.
We propose a new method for low-complexity compression of multispectral images. We develop on a novel approach to coding signals with side information based on recent advances in compressed sensing and universal scala...
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ISBN:
(纸本)9781479999880
We propose a new method for low-complexity compression of multispectral images. We develop on a novel approach to coding signals with side information based on recent advances in compressed sensing and universal scalar quantization. Our approach can be interpreted as a variation of quantized compressed sensing, where the most significant bits are discarded at the encoder and recovered at the decoder from the side information. The image is reconstructed using weighted total variation minimization, incorporating side information in the weights while enforcing consistency with the recovered quantized coefficient values. Our experiments validate our approach and confirm the improvements in rate-distortion performance.
This paper is concerned with transform coding of correlated sources in conjunction with variable rate quantization at high resolution. The approach builds on our prior work on optimality conditions for transform codin...
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ISBN:
(纸本)9780769546568
This paper is concerned with transform coding of correlated sources in conjunction with variable rate quantization at high resolution. The approach builds on our prior work on optimality conditions for transform coding in the point-to-point setting. The first contribution involves transform coding with decoder side information. In this setting, side information is only available to the decoder, whereas the encoder knows the joint statistics. The necessary and sufficient condition for optimality of a unitary transform in the side information setting is derived, namely, such transform minimizes a conditional divergence-based measure of inter-dependence of the transform coefficients, given the side information. This optimality result subsumes prior, known results that were restricted to the Gaussian case, where the conditional Karhunen-Loeve transform is optimal. The second contribution involves distributed transform coding, where two correlated sources are to be transform coded separately, but decoded jointly. The necessary and sufficient condition for optimality of unitary transforms in the distributed coding setting is derived. It is then specialized to produce closed form optimal transforms for specific source densities, including the case of jointly Gaussian sources.
Adaptive network coded cooperation (ANCC) scheme has been shown to have better performance than the conventional repetition-based schemes and the space-time coded cooperation (STCC) frameworks for data transmissions f...
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ISBN:
(纸本)9781424480166
Adaptive network coded cooperation (ANCC) scheme has been shown to have better performance than the conventional repetition-based schemes and the space-time coded cooperation (STCC) frameworks for data transmissions from a large collection of terminals to a common destination in large wireless networks. However, the random selection strategy for ANCC protocol may generate many short cycles in the distributed low-density generator-matrix(LDGM) codes, which may cause error-floor and performance degradation. In this paper, an optimized relay selection strategy for ANCC is proposed. By exploiting information interaction between destination and terminals before data communication, and matching the instantaneous network topology, the proposed method generates ensembles of distributed LDGM codes free of short cycles. Simulation results demonstrate that the proposed relay selection protocol significantly outperforms the random relay selection strategy for various network sizes and different fixed numbers of selected terminals.
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