We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm . While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently [1,2], in this paper, we propose an orthogonal approach to previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Our simulations show the power of our proposed algorithms, revealing their potential to effect significant energy savings (from 10%- 65%) for typical sensor data corresponding to a multitude of sensor modalities.

Inspired by a recently proposed constructive framework for the distributed source coding problem, 1 we propose a powerful constructive approach to the watermarking problem, emphasizing the dual roles of "source codes" and "channel codes." In our framework, we explore various source and channel codes to achieve watermarks that are robust to attackers in terms of maximizing the distortion between the corrupted coded-source signal and the original signal while holding the distortion between the coded-source signal and the original signal constant. We solve the resulting combinatorial optimization problem using an original technique based on robust optimization and convex programming. Keywords: Data Hiding, Digital Watermarking, Multimedia, Convex Optimization, Robustness 1. INTRODUCTION Digital watermarking (data hiding) is an emerging research area that has received a considerable amount of attention in recent years. The basic idea behind digital watermarking is to embed information...

In this paper we address visual communications via printing channels from an information-theoretic point of view as communications with side information. The solution to this problem addresses important aspects of multimedia data processing, security and management, since printed documents are still the most common form of visual information representation. Two practical approaches to side information communications for printed documents are analyzed in the paper. The first approach represents a layered joint source-channel coding for printed documents. This approach is based on a self-embedding concept where information is first encoded assuming a Wyner-Ziv set-up and then embedded into the original data using a Gel'fand-Pinsker construction and taking into account properties of printing channels. The second approach is based on Wyner-Ziv and Berger-Flynn-Gray set-ups and assumes two separated communications channels where an appropriate distributed coding should be elaborated. The first printing channel is considered to be a direct visual channel for images ("analog" channel with degradations). The second "digital channel" with constrained capacity is considered to be an appropriate auxiliary channel. We demonstrate both theoretically and practically how one can benefit from this sort of "distributed paper communications".

In this paper, we consider some basic concepts behind the design of existing robust perceptual hashing techniques for content identification. We show the limits of robust hashing from the communication perspectives as well as propose an approach capable to overcome these shortcomings in certain setups. The consideration is based on both achievable rate and probability of error. We use a fact that most of robust hashing algorithms are based on dimensionality reduction using random projections and quantization. Therefore, we demonstrate the corresponding achievable rate and probability of error based on the random projections and compare with the results for the direct domain. The effect of dimensionality reduction is studied and the corresponding approximations are provided based on Johnson-Lindenstrauss lemma. A side information assisted robust perceptual hashing is proposed as a solution to the above shortcomings. Notations: We use capital letters to denote scalar random variables X and X to denote vector random variables, corresponding small letters x and x to denote the realizations of scalar and vector random variables, respectively. All vectors without sign tilde are assumed to be of the length N and with the sign tilde of length L with the corresponding subindexes. The binary representation of vectors will be denoted as bx with the corresponding subindexing. We use X ∼ pX(x) or simply X ∼ p(x) to indicate that a random variable X is distributed according to pX(x). N(µ,σ2 X) stands for Gaussian distribution with mean µ and variance σ2 X. ||.|| denotes Euclidean vector norm and Q(.) stands for Q-function.

Digital watermarking can be viewed as channel coding with side information at the encoder (CC-SI); the original data to be watermarked is known to the encoder but not the decoder. Likewise, distributed source coding is rate distortion with side information at the decoder (RD-SI); a noisy observation of the source data to be compressed is available to the decoder but not the encoder. For a Gaussian channel or source, CC-SI and RD-SI are shown to be informationtheoretic duals. Ideal coding schemes are presented, and the schemes are interpreted geometrically to highlight dual elements and quantities. 1. Introduction The duality between channel coding (CC) for the Gaussian channel and rate distortion (RD) for a Gaussian source has been known for years [5]. Recently, interest has been renewed in two similar scenarios: channel coding with side information at the encoder (CC-SI) and rate distortion with side information at the decoder (RD-SI). CC-SI relates directly to digital watermarking o...

This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for largescale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.

In this paper we study the problem of information dissemination in dense multi-hop sensor networks characterized by highly correlated sample measurements. In particular, we investigate the benefits, and trade-offs, of exploiting correlations via cooperatively compressing the data as it hops around the network. First, we study two extreme cooperation strategies, namely no cooperation and network-wide cooperation. We show that network-wide cooperation achieves logarithmic growth rate for the transport traffic with the network size whereas the schedule length growth rate remains linear. Next, we analyze a two-phase cooperation strategy which localizes cooperation within regions of the network in an attempt to assess the performance of strategies bounded by the two aforementioned extremes. Finally, we extend two-phase cooperation to a multi-phase hierarchical cooperation strategy where the number of phases depends on the number of nodes and the size of the cooperation set. The rationale behind this strategy is to achieve logarithmic scaling laws at the expense of more complexity in coordinating nodes' cooperation. In addition, hierarchical cooperation opens room for optimizing the transport traffic and schedule length for a given network size.

Inspired by a recently proposedconstructive framework for the distributed source coding problem [1], we propose a powerful constructive approach to the watermarking problem, emphasizing the dual roles of distributed source coding with side information at the decoder and channel coding with side information at the encoder. In our framework, we explore various source and channel codes to close the gap on the achievable capacity of watermarking systems [2]. We propose two methods of solution, one which is based on optimal rate-distortion quantizers and the other basedon robust optimization and convex programming. The resulting watermarking schemes,when subjectedto additive white gaussiannoise (AWGN) attacks, achieve results which are comparable to or better than the best watermarking schemes in the literature. 1. INTRODUCTION Digital watermarking (data hiding) is an emerging research area that has received a considerable amount of attention in recent years. The basic idea behind digital...

In this paper, we propose a novel approach for efficiently sensing a remote field using wireless sensor networks. Our approach, the infer algorithm, is fully distributed, has low overhead and saves considerable energy compared to using just the data aggregation communication paradigm. This is accomplished by using a distributed algorithm to put nodes into sleep mode for a given period of time, thereby trading off energy usage for the accuracy of the data received at the sink. Bayesian inference is used to infer the missing data from the nodes that were not active during each sensing epoch. As opposed to other methods that have been considered, such as wavelet compression and distributed source coding, our algorithm has lower overhead in terms of both inter-node communication and computational complexity. Our simulations show that on average our algorithm produces energy savings of 59% while still maintaining data that is accurate to within 7.9%. We also show how the parameters of the algorithm may be tuned to optimize network lifetime for a desired level of data accuracy.

Achieving energy efficiency to prolong the network lifetime is an important design criterion for wireless sensor networks. In this paper, we propose a novel approach that exploits the broadcast nature of the wireless medium for energy conservation in spatially correlated wireless sensor networks. Since wireless transmission is inherently broadcast, when one sensor node transmits, other nodes in its coverage area can receive the transmitted data. When data collected by different sensors are correlated, each sensor can utilize the data it overhears from other sensors to compress its own data and conserve energy in its own transmissions. We apply this idea to a class of cluster-based wireless sensor networks in which each sensing node transmits collected data directly to its cluster head using time division multiple access (TDMA). We formulate the problem in which sensors in each cluster collaborate their transmitting, receiving, and compressing activities to optimize their lifetimes. We show that this lifetime optimization problem can be solved by a sequence of linear programming problems. We also propose a heuristic scheme, which has low complexity and achieves near optimal performance. Important characteristics of wireless sensor networks such as node startup cost and packet loss due to transmission errors are also considered. Numerical results show that by exploiting the broadcast nature of the wireless medium, our control schemes achieve significant improvement in the sensors ’ lifetimes.