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On the Optimal Distribution of Sensors in a Random Field

ACM TRANSACTIONS ON SENSOR NETWORKS 2005年第2期1卷 301-306页

作者： Zhang, Xin Wicker, Stephen B. Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

This article considers the problem of distributing sensors to observe a random field while meeting a series of energy and coverage constraints. In the network under consideration, N identical sensors are to be distributed, with each sensor sending data to a single fusion center at a fixed rate Delta R. The problem of sensor allocation to the coverage area is reduced to the problem of allocating sensors to cells by first partitioning the coverage field into cells, and then assuming that sensors in the same cells make the same observations. It is shown that the resulting sensor allocation problem is equivalent to a discrete form of the rate allocation problem, a well-known problem in the field of information theory. General guidelines for solving this problem are provided.

关键词： Theory Algorithms Information theory distributed source coding rate allocation sensor allocation

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Biometric template protection in multimodal authentication systems based on error correcting codes

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JOURNAL OF COMPUTER SECURITY 2010年第1期18卷 161-185页

作者： Argyropoulos, Savvas Tzovaras, Dimitrios Ioannidis, Dimosthenis Damousis, Yannis Strintzis, Michael G. Braun, Martin Boverie, Serge Ctr Res & Technol Hellas Informat & Telemat Inst GR-57001 Thermi Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki Greece Continental Automot GmbH Regensburg Germany

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

关键词： Multimodal biometrics authentication biometric template security distributed source coding

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Greedy Iterative and Meta-Heuristic Clustering With Coded Caching and Slepian-Wolf Compression for Correlated Content

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IEEE ACCESS 2024年 12卷 12323-12334页

作者： Rosen, Benjamin Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn ZA-2000 Johannesburg South Africa

Content caching has emerged as an effective approach to combat the increasing strains on our current network infrastructure. This method is further improved when combining caching with source coding. However, additional complexity is incurred by creating this hybrid method, as the source coding component comes with associated feasibility constraints and decoding costs. This paper presents an approach to balance this complexity with the coding gains by selecting the best-performing subset of files to compress, while the others are left uncoded. This problem is shown to be NP-hard in general and difficult to solve in an iteration-free manner. To this end, two novel approaches are outlined: an iterative-based solution, which uses the features of the entropy function to derive the most suitable files to compress jointly, and a meta-heuristic version, which is based on the Genetic Algorithm. When compared to an exhaustive search, the proposed solutions are found to be sub-optimal but falling above the 90th percentile of all possible solutions on average. Significantly, the iterative method produces results within one percentile of the meta-heuristic approach yet it finds a solution 2.31 times faster. The iterative approach has an additional benefit, in that it is able to predict the relative gains when adding more files to a compression group. It is thus able to terminate prematurely if the estimated gains are less than a chosen threshold.

关键词： Encoding Metaheuristics Entropy Complexity theory Iterative methods Servers Content management Cache storage Slepian-wolf coding distributed source coding coded caching iterative optimisation

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distributed Computing and the Graph Entropy Region

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IEEE TRANSACTIONS ON INFORMATION THEORY 2014年第6期60卷 3435-3449页

作者： Shayevitz, Ofer Tel Aviv Univ Dept Elect Engn Syst IL-6997801 Tel Aviv Israel

Two remote senders observe X and Y, respectively, and can noiselessly send information via a common relay node to a receiver that observes Z. The receiver wants to compute a function f(X, Y, Z) of these possibly related observations, without error. We study the average number of bits that need to be conveyed to that end by each sender to the relay and by the relay to the receiver, in the limit of multiple instances. We relate these quantities to the entropy region of a probabilistic graph with respect to a Cartesian representation of its vertex set, which we define as a natural extension of graph entropy. General properties and bounds for the graph entropy region are derived, and mapped back to special cases of the distributed computing setup.

关键词： distributed source coding zero-error information theory graph entropy

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Audio coding in wireless acoustic sensor networks

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SIGNAL PROCESSING 2015年 107卷 141-152页

作者： Zahedi, Adel Ostergaard, Jan Jensen, Soren Holdt Bech, Soren Naylor, Patrick Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark Bang & Olufsen DK-7600 Struer Denmark Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England

In this paper, we consider the problem of source coding for a wireless acoustic sensor network where each node in the network makes its own noisy measurement of the sound field, and communicates with other nodes in the network by sending and receiving encoded versions of the measurements. To make use of the correlation between the sources available at the nodes, we consider the possibility of combining the measurement and the received messages into one single message at each node instead of forwarding the received messages and separate encoding of the measurement. Moreover, to exploit the correlation between the messages received by a node and the node's measurement of the source, we propose to use the measurement as side information and thereby form a distributed source coding (DSC) problem. Assuming that the sources are Gaussian, we then derive the rate-distortion function (RDF) for the resulting remote DSC problem under covariance matrix distortion constraints. We further show that for this problem, the Gaussian source is the worst to code. Thus, the Gaussian RDF provides an upper bound to other sources such as audio signals. We then turn our attention to audio signals. We consider an acoustical model based on the room impulse response (RIR) and provide simulation results for the rate-distortion performance in a practical setup where a set of microphones record the sound in a standard listening room. Since our reconstruction scheme and distortion measure are defined over the direct sound source, coding and dereverberation are performed in a joint manner. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Wireless acoustic sensor network distributed source coding Rate-distortion function Covariance distortion constraint Zero-error coding

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One-bit distributed sensing and coding for field estimation in sensor networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2008年第9期56卷 4433-4445页

作者： Wang, Ye Ishwar, Prakash Saligrama, Venkatesh Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Univ Calif Berkeley Elect Res Lab Berkeley CA 94720 USA

This paper formulates and studies a general distributed field reconstruction problem using a dense network of noisy one-bit randomized scalar quantizers in the presence of additive observation noise of unknown distribution. A constructive quantization, coding, and field reconstruction scheme is developed and an upper-bound to the associated mean squared error (MSE) at any point and any snapshot is derived in terms of the local spatio-temporal smoothness properties of the underlying field. It is shown that when the noise, sensor placement pattern, and the sensor schedule satisfy certain weak technical requirements, it is possible to drive the MSE to zero with increasing sensor density at points of field continuity, while ensuring that the per-sensor bit rate and sensing-related network overhead rate simultaneously go to zero. A general condition for the optimization of the rate of decay of the MSE upper-bound with sensor density is derived. Specific MSE versus sensor density decay rates are illustrated for the constant and Lipschitz-1 fields with the former attaining order-optimal scaling behavior.

关键词： distributed source coding dithered scalar quantization field estimation oversampled analog-to-digital conversion sensor networks scaling law

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Systematic DFT Frames: Principle, Eigenvalues Structure, and Applications

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第15期61卷 3774-3785页

作者： Vaezi, Mojtaba Labeau, Fabrice McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

Motivated by a host of recent applications requiring some amount of redundancy, frames are becoming a standard tool in the signal processing toolbox. In this paper, we study a specific class of frames, known as discrete Fourier transform (DFT) codes, and introduce the notion of systematic frames for this class. This is encouraged by a new application of frames, namely, distributed source coding that uses DFT codes for compression. Studying their extreme eigenvalues, we show that, unlike DFT frames, systematic DFT frames are not necessarily tight. Then, we come up with conditions for which these frames can be tight. In either case, the best and worst systematic frames are established in the minimum mean squared reconstruction error sense. Eigenvalues of DFT frames and their subframes play a pivotal role in this work. Particularly, we derive some bounds on the extreme eigenvalues DFT subframes which are used to prove most of the results;these bounds are valuable independently.

关键词： BCH-DFT codes systematic frames parity eigen-value optimal reconstruction quantization erasures distributed source coding Vandermonde matrix

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Residual-Quantization Based Code Design for Compressing Noisy sources With Arbitrary Decoder Side Information

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IEEE TRANSACTIONS ON COMMUNICATIONS 2016年第4期64卷 1711-1725页

作者： Wei, Yi-Peng Lin, Shih-Chun Lin, Song-Jheng Su, Hsuan-Jung Poor, H. Vincent Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 10617 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

This paper considers noisy Wyner-Ziv coding (WZC), in which a remote noisy source is compressed with side information at the decoder only. The decoder side information is not limited to being Gaussian, thus this noisy WZC problem cannot be transformed into the conventional one tailored for noiseless sources. A new coding structure, named the residual-quantization (RQ) based noisy WZC, is proposed to solve the problem. This scheme explicitly constructs the theoretical auxiliary random variable to facilitate optimal reconstruction of the noisy source. In this two-stage encoder, the noisy source is quantized twice and the quantization error (residue) of the first stage is the input of the second stage. By sending only the quantization index of the second stage to the decoder, the corresponding code rate can theoretically approach the noisy WZC bound. Moreover, the RQ-based noisy WZC is implemented using graph-based codes. The main challenge is that it is necessary to design a codebook that is simultaneously good for source and channel coding for the first stage quantization, since this quantization code also acts as a channel code at the decoder. This problem is solved by constructing a low-density parity check (LDPC) code with edge degrees optimized for channel coding, and enhancing its performance for source coding by using a modified reinforced belief-propagation quantization algorithm. Simulation results show that the noisy Wyner-Ziv bounds can be practically approached by our implementation. In addition, the proposed implementation offers more flexibility in the code rates compared with the existing practical designs, making it more suitable for emerging applications such as fronthaul compression.

关键词： Fronthaul compression distributed source coding residual-quantization LDPC LDGM channel coding

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Functional Compression Through Graph Coloring

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IEEE TRANSACTIONS ON INFORMATION THEORY 2010年第8期57卷 3901-3917页

作者： Doshi, Vishal Shah, Devavrat Medard, Muriel Effros, Michelle MIT Informat & Decis Syst Lab Dept EECS Cambridge MA 02139 USA MIT Elect Res Lab Dept EECS Cambridge MA 02139 USA CALTECH Data Compress Lab Pasadena CA 91125 USA

Motivated by applications to sensor networks and privacy preserving databases, we consider the problem of functional compression. The objective is to separately compress possibly correlated discrete sources such that an arbitrary but fixed deterministic function of those sources can be computed given the compressed data from each source. We consider both the lossless and lossy computation of a function. Specifically, we present results of the rate regions for three instances of the problem where there are two sources: 1) lossless computation where one source is available at the decoder;2) under a special condition, lossless computation where both sources are separately encoded;and 3) lossy computation where one source is available at the decoder. For all of these instances, we present a layered architecture for distributed coding: first preprocess data at each source using colorings of certain characteristic graphs and then use standard distributed source coding (a la Slepian and Wolfs scheme) to compress them. For the first instance, our results extend the approach developed by Orlitsky and Roche (2001) in the sense that our scheme requires simpler structure of coloring rather than independent sets as in the previous case. As an intermediate step to obtain these results, we obtain an asymptotic characterization of conditional graph coloring for an OR product of graphs generalizing a result of Korner (1973), which should be of interest in its own right.

关键词： distributed computing distributed source coding functional compression

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Multiple-source Slepian-Wolf coding Under a Linear Equation Correlation Model

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IEEE TRANSACTIONS ON COMMUNICATIONS 2012年第9期60卷 2402-2407页

作者： Li, Shizheng Ramamoorthy, Aditya Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

In this work we present practical coding schemes for the problem of lossless distributed source coding for multiple sources. We consider two scenarios - the classical Slepian-Wolf case where there is no feedback from the terminal to the sources and a case where there is feedback from the terminal to the source encoders. The correlation model of interest is given by a system of linear equations, a generalization of the work of Stankovic et al. '06. We propose a transformation of correlation model and a way to determine proper decoding schedules, both of which are required to obtain the optimal sum rate. Our scheme allows us to exploit more correlations than those in the previous work. Simulation results show that the proposed coding scheme has lower sum rate than previous work in both scenarios.

关键词： Slepian-Wolf distributed source coding rate adaptive codes LDPC

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