Results 1  10
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79
The impact of spatial correlation on routing with compression in wireless sensor networks
 In ACM/IEEE International Symposium on Information Processing in Sensor Networks (IPSN 2004
"... The efficacy of data aggregation in sensor networks is a function of the degree of spatial correlation in the sensed phenomenon. The recent literature has examined a variety of schemes that achieve greater data aggregation by routing data with regard to the underlying spatial correlation. A well kno ..."
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Cited by 142 (12 self)
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The efficacy of data aggregation in sensor networks is a function of the degree of spatial correlation in the sensed phenomenon. The recent literature has examined a variety of schemes that achieve greater data aggregation by routing data with regard to the underlying spatial correlation. A well known conclusion from these papers is that the nature of optimal routing with compression depends on the correlation level. In this work, we show the existence of a simple, practical and static correlationunaware clustering scheme that satisfies a minmax nearoptimality condition. The implication for system design is that a static correlationunaware scheme can perform as well as sophisticated adaptive schemes for joint routing and compression.
Distributed compressed sensing
, 2005
"... Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algori ..."
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Cited by 84 (21 self)
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Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study in detail three simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the SlepianWolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In two of our three models, the results are asymptotically bestpossible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.
Networked SlepianWolf: Theory, Algorithms and Scaling Laws
 IEEE Transactions on Information Theory
, 2003
"... Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. We consider the minimization of cost functions which are the product of a function of the rate and a function of the path weight. We consider both the data ..."
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Cited by 54 (7 self)
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Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. We consider the minimization of cost functions which are the product of a function of the rate and a function of the path weight. We consider both the data gathering scenario, which is relevant in sensor networks, and general tra#c matrices, relevant for general networks. The minimization is achieved by jointly optimizing (a) the transmission structure, which we show consists in general of a superposition of trees from each of the source nodes to its corresponding sink nodes, and (b) the rate allocation across the source nodes, which is done by SlepianWolf coding. We show that the overall minimization can be achieved in two concatenated steps.
Powerefficient sensor placement and transmission structure for data gathering under distortion constraints
 in IPSN ’04
, 2004
"... We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for commun ..."
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Cited by 42 (5 self)
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We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for communication. We assume that the nodes use either joint entropy coding based on explicit communication between sensor nodes, where coding is done when side information is available, or SlepianWolf coding where nodes have knowledge of network correlation statistics. We consider both maximum and average distortion bounds. We prove that this optimization is NPcomplete since it involves an interplay between the spaces of possible transmission structures given radio reachability limitations, and feasible placements satisfying distortion bounds. We address this problem by first looking at the simplified problem of optimal placement in the onedimensional case. An analytical solution is derived for the case when there is a simple aggregation scheme, and numerical results are provided for the cases when joint entropy encoding is used. We use the insight from our 1D analysis to extend our results to the 2D case and compare it to typical uniform random placement and shortestpath tree. Our algorithm for twodimensional placement and transmission structure provides two to three fold reduction in
Network Correlated Data Gathering with Explicit Communication: NPCompleteness and Algorithms
"... We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we ..."
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Cited by 39 (8 self)
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We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy based coding model with explicit communication where coding is simple and the transmission structure optimization is di#cult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NPhard. We propose some e#cient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.
Efficient gathering of correlated data in sensor networks
 in Proc. of ACM Intl. symposium on Mobile ad hoc networking and computing, 2005
, 2005
"... In this paper, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data ..."
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Cited by 38 (0 self)
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In this paper, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the datagathering node. We define the problem of selecting such a set of sensors as the connected correlationdominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network. We develop a set of energyefficient distributed algorithms and competitive centralized heuristics to select a connected correlationdominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but nonexhaustive) centralized approximation algorithm that returns a solution within O(log n) of the optimal size. Based on the approximation algorithm, we design a class of centralized heuristics that are empirically shown to return nearoptimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique – even in dynamic conditions.
Structurefree data aggregation in sensor networks
 IEEE Transactions on Mobile Computing
, 2006
"... Abstract—Data aggregation protocols can reduce the communication cost, thereby extending the lifetime of sensor networks. Prior works on data aggregation protocols have focused on treebased or clusterbased structured approaches. Although structured approaches are suited for data gathering applicat ..."
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Cited by 29 (4 self)
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Abstract—Data aggregation protocols can reduce the communication cost, thereby extending the lifetime of sensor networks. Prior works on data aggregation protocols have focused on treebased or clusterbased structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for eventbased applications. The goal of our work is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. As packets need to converge spatially and temporally for data aggregation, we propose two corresponding mechanisms—DataAware Anycast at the MAC layer and Randomized Waiting at the application layer. We model the performance of the combined protocol that uses both the approaches and show that our analysis matches with the simulations. Using extensive simulations and experiments on a testbed with implementation in TinyOS, we study the performance and potential of structurefree data aggregation. Index Terms—Anycasting, data aggregation, sensor networks, structurefree. 1
Gathering Correlated Data in Sensor Networks
 In Proc. ACM Joint Workshop on Foundations of Mobile Computing (DIALMPOMC
, 2004
"... In this paper, we consider energyefficient gathering of correlated data in sensor networks. We focus on singleinput coding strategies in order to aggregate correlated data. For foreign coding we propose the MEGA algorithm which yields a minimumenergy data gathering topology in O ( n 3) time. We a ..."
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Cited by 27 (4 self)
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In this paper, we consider energyefficient gathering of correlated data in sensor networks. We focus on singleinput coding strategies in order to aggregate correlated data. For foreign coding we propose the MEGA algorithm which yields a minimumenergy data gathering topology in O ( n 3) time. We also consider selfcoding for which the problem of finding an optimal data gathering tree was recently shown to be NPcomplete; with LEGA, we present the first approximation algorithm for this problem with approximation ratio 2(1 + √ 2) and running time O(m + n log n). Categories and Subject Descriptors:
Compressive data gathering for largescale wireless sensor networks
 in Proc. ACM Mobicom’09
, 2009
"... 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 la ..."
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Cited by 19 (2 self)
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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 largescale 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 ns2 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.
Data gathering tours in sensor networks
 IN IPSN
, 2006
"... A basic task in sensor networks is to interactively gather data from a subset of the sensor nodes. When data needs to be gathered from a selected set of nodes in the network, existing communication schemes often behave poorly. In this paper, we study the algorithmic challenges in efficiently routing ..."
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Cited by 18 (5 self)
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A basic task in sensor networks is to interactively gather data from a subset of the sensor nodes. When data needs to be gathered from a selected set of nodes in the network, existing communication schemes often behave poorly. In this paper, we study the algorithmic challenges in efficiently routing a fixedsize packet through a small number of nodes in a sensor network, picking up data as the query is routed. We show that computing the optimal routing scheme to visit a specific set of nodes is NPcomplete, but we develop approximation algorithms that produce plans with costs within a constant factor of the optimum. We enhance the robustness of our initial approach to accommodate the practical issues of limitedsized packets as well as network link and node failures, and examine how different approaches behave with dynamic changes in the network topology. Our theoretical results are validated via an implementation of our algorithms on the TinyOS platform and a controlled simulation study using Matlab and TOSSIM.