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74
Distributed source coding for sensor networks
 In IEEE Signal Processing Magazine
, 2004
"... n recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. MIT Technology Review ranked wireless sensor networks that consist of many tiny, low ..."
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Cited by 221 (4 self)
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n recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. MIT Technology Review ranked wireless sensor networks that consist of many tiny, lowpower and cheap wireless sensors as the number one emerging technology. Unlike PCs or the Internet, which are designed to support all types of applications, sensor networks are usually mission driven and application specific (be it detection of biological agents and toxic chemicals; environmental measurement of temperature, pressure and vibration; or realtime area video surveillance). Thus they must operate under a set of unique constraints and requirements. For example, in contrast to many other wireless devices (e.g., cellular phones, PDAs, and laptops), in which energy can be recharged from time to time, the energy provisioned for a wireless sensor node is not expected to be renewed throughout its mission. The limited amount of energy available to wireless sensors has a significant impact on all aspects of a wireless sensor network, from the amount of information that the node can process, to the volume of wireless communication it can carry across large distances. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies; it relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies for sensor networks is distributed source coding (DSC), which refers to the compression of multiple correlated sensor outputs [1]–[4] that do not communicate with each other (hence distributed coding). These sensors send their compressed outputs to a central point [e.g., the base station (BS)] for joint decoding. I
On the Interdependence of Routing and Data Compression in MultiHop Sensor Networks
, 2002
"... We consider a problem of broadcast communication in a multihop sensor network, in which samples of a random field are collected at each node of the network, and the goal is for all nodes to obtain an estimate of the entire field within a prescribed distortion value. The main idea we explore in this ..."
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Cited by 137 (9 self)
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We consider a problem of broadcast communication in a multihop sensor network, in which samples of a random field are collected at each node of the network, and the goal is for all nodes to obtain an estimate of the entire field within a prescribed distortion value. The main idea we explore in this paper is that of jointly compressing the data generated by different nodes as this information travels over multiple hops, to eliminate correlations in the representation of the sampled field. Our main contributions are: (a) we obtain, using simple network flow concepts, conditions on the rate/distortion function of the random field, so as to guarantee that any node can obtain the measurements collected at every other node in the network, quantized to within any prescribed distortion value; and (b), we construct a large class of physicallymotivated stochastic models for sensor data, for which we are able to prove that the joint rate/distortion function of all the data generated by the whole network grows slower than the bounds found in (a). A truly novel aspect of our work is the tight coupling between routing and source coding, explicitly formulated in a simple and analytically tractable modelto the best of our knowledge, this connection had not been studied before.
Spatiotemporal correlation: theory and applications for wireless sensor networks
 Computer Networks Journal (Elsevier
, 2004
"... Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the physical phenomenon const ..."
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Cited by 116 (12 self)
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Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. These spatial and temporal correlations along with the collaborative nature of the WSN bring significant potential advantages for the development of efficient communication protocols wellsuited for the WSN paradigm. In this paper, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to model the spatial and temporal correlations in WSN. The objective of this framework is to enable the development of efficient communication protocols which exploit these advantageous intrinsic features of the WSN paradigm. Based on this framework, possible approaches are discussed to exploit spatial and temporal correlation for efficient medium access and reliable event transport in WSN, respectively.
The Distributed KarhunenLoève Transform
 IEEE Trans. Inform. Theory
, 2003
"... The KarhunenLoeve transform (KLT) is a key element of many signal processing tasks, including approximation, compression, and classification. Many recent applications involve distributed signal processing where it is not generally possible to apply the KLT to the signal; rather, the KLT must be ..."
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Cited by 90 (15 self)
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The KarhunenLoeve transform (KLT) is a key element of many signal processing tasks, including approximation, compression, and classification. Many recent applications involve distributed signal processing where it is not generally possible to apply the KLT to the signal; rather, the KLT must be approximated in a distributed fashion.
DataGathering Wireless Sensor Networks: Organization and Capacity
 Computer Networks
, 2003
"... In this paper we study the transport capacity of a datagathering wireless sensor network under di#erent communication organizations. In particular, we consider using a flat as well as a hierarchical/clustering architecture to realize manytoone communications. The capacity of the network under thi ..."
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Cited by 85 (3 self)
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In this paper we study the transport capacity of a datagathering wireless sensor network under di#erent communication organizations. In particular, we consider using a flat as well as a hierarchical/clustering architecture to realize manytoone communications. The capacity of the network under this manytoone data gathering scenario is reduced compared to random onetoone communication due to the unavoidable creation of a point of tra#c concentration at the data collector/receiver. We introduce the overall throughput bound of # = per node, where W is the transmission capacity, and show under what conditions it can be achieved and under what conditions it cannot. When those conditions are not met, we constructively show how # = # is achieved with high probability as the number of sensors goes to infinity. We also show how the introduction of clustering can improve the throughput. We discuss the tradeo#s between achieving capacity and energy consumption, how transport capacity might be a#ected by considering innetwork processing and the implications this study has on the design of practical protocols for largescale datagathering wireless sensor networks.
On Compressing Encrypted Data
 IN IEEE TRANS. SIGNAL PROCESSING
, 2004
"... When it is desired to transmit redundant data over an insecure and bandwidthconstrained channel, it is customary to first compress the data and then encrypt it. In this paper, we investigate the novelty of reversing the order of these steps, i.e., first encrypting and then compressing, without com ..."
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Cited by 67 (5 self)
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When it is desired to transmit redundant data over an insecure and bandwidthconstrained channel, it is customary to first compress the data and then encrypt it. In this paper, we investigate the novelty of reversing the order of these steps, i.e., first encrypting and then compressing, without compromising either the compression efficiency or the informationtheoretic security. Although counterintuitive, we show surprisingly that, through the use of coding with side information principles, this reversal of order is indeed possible in some settings of interest without loss of either optimal coding efficiency or perfect secrecy. We show that in certain scenarios our scheme requires no more randomness in the encryption key than the conventional system where compression precedes encryption. In addition to proving the theoretical feasibility of this reversal of operations, we also describe a system which implements compression of encrypted data.
Coding for the SlepianWolf problem with turbo codes
 Proc. of IEEE Globecom
, 2001
"... Abstract – This paper proposes a practical coding scheme for the SlepianWolf problem of separate encoding of correlated sources. Finitestate machine (FSM) encoders, concatenated in parallel, are used at the transmit side and an iterative turbo decoder is applied at the receiver. Simulation results ..."
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Cited by 62 (0 self)
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Abstract – This paper proposes a practical coding scheme for the SlepianWolf problem of separate encoding of correlated sources. Finitestate machine (FSM) encoders, concatenated in parallel, are used at the transmit side and an iterative turbo decoder is applied at the receiver. Simulation results of system performance are presented for binary sources with different amounts of correlation. Obtained results show that the proposed technique outperforms by far both an equivalent uncoded system and a system coded with traditional (nonconcatenated) FSM coding. I.
Geometric programming duals of channel capacity and rate distortion
 IEEE TRANS. INFORM. THEORY
, 2004
"... We show that the Lagrange dual problems of the channel capacity problem with input cost and the rate distortion problem are simple geometric programs. Upper bounds on channel capacity and lower bounds on rate distortion can be efficiently generated from their duals. For channel capacity, the geomet ..."
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Cited by 38 (1 self)
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We show that the Lagrange dual problems of the channel capacity problem with input cost and the rate distortion problem are simple geometric programs. Upper bounds on channel capacity and lower bounds on rate distortion can be efficiently generated from their duals. For channel capacity, the geometric programming dual characterization is shown to be equivalent to the minmax Kullback–Leibler (KL) characterization in [10], [14]. For rate distortion, the geometric programming dual is extended to rate distortion with twosided state information. A “duality by mapping ” is then given between the Lagrange dual problems of channel capacity with input cost and rate distortion, which resolves several apparent asymmetries between their primal problems in the familiar form of mutual information optimization problems. Both the primal and dual problems can be interpreted in a common framework of free energy optimization from statistical physics.
Distributed Compression In Dense Sensor Networks
 IEEE Signal Processing Magazine
, 2002
"... this article, we propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental conc ..."
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Cited by 27 (2 self)
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this article, we propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. In this article, we review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework
On the Feasibility of LargeScale Wireless Sensor Networks
 In Proc. 40th Allerton Conf. on Communication, Control and Computing
, 2002
"... We consider the problem of rate/distortion with side information available only at the decoder. For the case of jointlyGaussian source X and side information Y, and meansquared error distortion, Wyner proved in 1976 that the rate/distortion function for this problem is identical to the conditional ..."
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Cited by 20 (2 self)
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We consider the problem of rate/distortion with side information available only at the decoder. For the case of jointlyGaussian source X and side information Y, and meansquared error distortion, Wyner proved in 1976 that the rate/distortion function for this problem is identical to the conditional rate/distortion function RXY, assuming the side information Y is available at the encoder. In this paper we construct a structured class of asymptotically optimal quantizers for this problem: under the assumption of high correlation between source X and side information Y, we show there exist quantizers within our class whose performance comes arbitrarily close to Wyner’s bound. As an application illustrating the relevance of the highcorrelation asymptotics, we also explore the use of these quantizers in the context of a problem of data compression for sensor networks, in a setup involving a large number of devices collecting highly correlated measurements within a confined area. An important feature of our formulation is that, although the pernode throughput of the network tends to zero as network size increases, so does the amount of information generated by each transmitter. This is a situation likely to be encountered often in practice, which allows us to cast under new—and more “optimistic”—light some negative results on the transport capacity of largescale wireless networks. Index terms: Rate/distortion, rate/distortion with side information, quantization, vector quantization, lattice quantization, lattice codes, hexagonal lattice, source coding, network information theory, adhoc networks, sensor networks, multihop radio networks, wireless networks, throughput, capacity.