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Multiple Description Analog Joint Source-Channel Coding For Parallel Channels With Side Information Multiple Description Analog Joint Source-Channel Coding For Parallel Channels With Side Information

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Date added: 10/28/2012
Date modified: 02/07/2013
Filesize: 367.15 kB
Downloads: 1059

 

With the purpose of reducing the coding complexity and delay of the separation-based schemes, an analog joint source-channel coding scheme is proposed for transmissions through parallel AWGN channels with side information at the receiver. This scheme divides the bidimensional source space into a set of hexagons and transmits the relative position of the source vectors inside the corresponding hexagon by using two complimentary analog mappings. The results are satisfactory, specially taking into consideration the low complexity and delay of the proposed scheme.

Distributed estimation of statistical correlation measures for spatial inference in WSNs Distributed estimation of statistical correlation measures for spatial inference in WSNs

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Date added: 10/28/2012
Date modified: 02/07/2013
Filesize: Unknown
Downloads: 745

 

This work shows how to obtain distributively important statistical measures such as the semivariogram and the covariogram in a Wireless Sensor Network. These statistics describe the spatial dependence of the sensed area and allow making inferences about unknown field data. In practice, these are complex measures that require global knowledge such as the distance between every pair of nodes, which is not available in a distributed scenario. Then, motivated by the distributed nature of a Wireless Sensor Network and the requirement of making estimations in many real applications, we propose a distributed method to obtain an approximation of these measures, based only on the local samples of the nodes. Our method only requires knowing, at each node, the geographic position of its neighbours. Additionally, we show that introducing random movements of the nodes, the quality of the results can be improved. Simulation results are presented to evaluate the performance of our algorithm.

Optimum distortion exponent in parallel fading channels by using analog joint source-channel coding Optimum distortion exponent in parallel fading channels by using analog joint source-channel coding

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Date added: 10/28/2012
Date modified: 12/28/2013
Filesize: 207.9 kB
Downloads: 1175

 

An extended analog joint source-channel coding (JSCC) multiple description (MD) scheme is introduced. This new scheme extends a previously presented analog JSCCMD scheme in order to work at different bandwidth ratios. This scheme is suitable for transmissions through parallel AWGN on-off channels and parallel slow-fading channels. The strengths of the proposed scheme in comparison with other coding alternatives are its coding/decoding simplicity and low delay, and its optimality in terms of distortion exponent in the fading parallel channels.

Field Estimation in Wireless Sensor Networks Using Distributed Kriging Field Estimation in Wireless Sensor Networks Using Distributed Kriging

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Date added: 10/28/2012
Date modified: 12/28/2013
Filesize: 398.05 kB
Downloads: 1839

 

In this paper, we tackle the problem of spatial interpolation for distributed estimation in Wireless Sensor Networks by using a geostatistical technique called kriging. We present a novel Distributed Iterative Kriging Algorithm (DIKA) which is composed of two main phases. First, the spatial dependence of the field is exploited by calculating semivariograms in an iterative way. Second, the kriging system of equations is solved by an initial set of nodes in a distributed manner, providing some initial interpolation weights to each node. In our algorithm, the estimation accuracy can be improved by iteratively adding new nodes and updating appropriately the weights, which leads to a reduction in the kriging variance. As a consequence, each cluster is constructed adaptively by the set of nodes that achieves the best estimation over the sub-area covered by them. We analyze the most influential parameters to implement this algorithm. Finally, we evaluate the performance of our algorithm and we also analyze its complexity.

Distributed Subspace Projection in Wireless Sensor Networks using Computational Codes Distributed Subspace Projection in Wireless Sensor Networks using Computational Codes

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Date added: 10/28/2012
Date modified: 12/28/2013
Filesize: 4.33 MB
Downloads: 841

 In this paper, we develop a new power-efficient algorithm for Wireless Sensor Networks (WSN) in order to obtain, in a distributed manner, the Projection of an observed sampled spatial field on a subspace of lower dimension. This is an important problem that is motivated in various applications where there are well defined subspaces of interest (e.g. spectral maps in cognitive radios). As opposed to traditional Gossip Algorithms used for subspace projection assuming separation of channel coding and computation, our algorithm combines Computational Coding and a modification of existing Gossip Algorithms, achieving important savings in convergence time and yielding an exponential decrease in energy consumption as the size of the network increases.

Network Topology Optimization for Accelerating Consensus Algorithms under Power Constraints Network Topology Optimization for Accelerating Consensus Algorithms under Power Constraints

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Date added: 10/28/2012
Date modified: 12/28/2013
Filesize: Unknown
Downloads: 1220

 

The average consensus algorithm is a well known distributed process in which the nodes iteratively communicate with the nodes within their communication range in order to obtain an estimation of the global average. These repeated communications, when performed in a uniformly randomly deployed network, such as a Wireless Sensor Network, lead to several nodes consuming much more power than others, thus reducing the lifetime of the whole network. This paper proposes a fully distributed method that allows the network nodes to suitably decide which subset of communications provides the best performance during the consensus process in terms of convergence time and power efficiency. Our method simultaneously improves both the convergence of the consensus algorithm and the lifetime of the whole network. Moreover, as a benchmark, we propose a convex optimization problem whose results can be compared with those obtained by our distributed approach. Simulation results are presented to show the efficiency of our proposal, comparing our two methods with existing approaches in the related literature.

Link scheduling in sensor networks for asymmetric average consensus Link scheduling in sensor networks for asymmetric average consensus

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Date added: 10/28/2012
Date modified: 12/28/2013
Filesize: Unknown
Downloads: 879

 

Wireless Sensor Networks constitute a recent technology where the nodes cooperate to obtain, in a totally distributed way, certain function of the sensed data. One example is the average consensus algorithm, which allows every node to converge to the global average. However, this algorithm presents two major drawbacks in practice. The first one is that instantaneous symmetric links are required, which are hard to ensure in practice because of the presence of wireless interferences. The second one is that all the nodes are required to communicate with all of their local neighbours in every iteration, which can lead to an unbounded delay. In order to solve these issues, we propose a novel link scheduling protocol that activates certain suitable links in each iteration, leading to a new scheme of communications where the links are asymmetric and the communications are performed in a asynchronous manner. This new scheme only requires connectivity and symmetric links on average to guarantee convergence, which are ensured by our link scheduling protocol.

In-network Computation of The Transition Matrix for Distributed Subspace Projection In-network Computation of The Transition Matrix for Distributed Subspace Projection

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Date added: 10/28/2012
Date modified: 12/28/2013
Filesize: 454.97 kB
Downloads: 963

In this paper, we develop a novel strategy to compute the transition matrix for the projection problem in a distributed fashion through gossiping in Wireless Sensor Networks. So far, the transition matrix had to be computed off-line by a third party and then provided to the network. The Subspace Projection Problem is useful in various application scenarios (e.g. spectral spatial maps in cognitive radios) and consists of projecting the observed sampled spatial field into a subspace of interest with lower dimension. Although the actual exact computation of the optimal transition matrix is not feasible in a distributed way, we develop an algorithm that is based on well known results from linear algebra and a distributed genetic algorithm in order to compute an approximation of the optimal matrix to a desired precision.

 

Power-Aware Joint Sensor Selection and Routing for Distributed Estimation: A Convex Optimization App Power-Aware Joint Sensor Selection and Routing for Distributed Estimation: A Convex Optimization App

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Date added: 10/26/2012
Date modified: 12/28/2013
Filesize: Unknown
Downloads: 850

 

This paper considers the problem of power-efficient distributed estimation of vector parameters related to localized phenomena so that both the subset of sensor selection and the routing structure in a wireless sensor network are optimized jointly in order to obtain the best possible estimation performance at a given querying node, for a given total power budget. We first formulate our problem as an optimization problem and show that it is NP-Hard. Then, we design two algorithms: a fixed-tree relaxation-based and a novel and very efficient local distributed optimization to optimize jointly the sensor selection and the routing structure. We also provide a lower bound for our optimization problem and show that our local distributed optimization algorithm provides a performance that is close to this bound. Although there is no guarantee that the gap between this lower bound and the optimal solution of the main problem is always small, our numerical experiments support that this gap is actually very small in many cases. An important result from our work is that because of the interplay between the communication cost over the links and the gains in estimation accuracy obtained by choosing certain sensors, the traditional shortest-path-tree routing structure, widely used in practice, is no longer optimal, that is, our routing structures provide a better trade-off between the overall power efficiency and the final estimation accuracy obtained at the querying node. Comparing to more conventional sensor selection and fixed routing algorithms, our proposed joint sensor selection and routing algorithms yields a significant amount of energy saving.

In-Network Iterative Distributed Estimation for Power-Constrained Wireless Sensor Networks In-Network Iterative Distributed Estimation for Power-Constrained Wireless Sensor Networks

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Date added: 10/26/2012
Date modified: 12/28/2013
Filesize: Unknown
Downloads: 1197

 

In this paper, we consider the problem of power-efficient distributed estimation of a localized event in the large-scale Wireless Sensor Networks (WSNs). In order to increase the power efficiency in these networks, we develop a joint optimization problem that involves both selecting a subset of active sensors and the routing structure so that the quality of estimation at a given querying node is the best possible subject to a total imposed communication cost. We first formulate our problem as an optimization problem and show that it is NP-Hard. Then, we design two algorithms: a fixed-tree relaxation-based and a novel and very efficient iterative distributed to optimize jointly the sensor selection and the routing structure. We also provide a lower bound for our optimization problem and show that our iterative distributed algorithm provides a performance that is close to this bound. Although there is no guarantee that the gap between this lower bound and the optimal solution of the main problem is always small, our numerical experiments support that this gap is actually very small in many cases. An important result from our work is the fact that because of the interplay between communication cost and gain in estimation when fusing measurements from different sensors, the traditional Shortest Path Tree (SPT) routing structure, widely used in practice, is no longer optimal, that is, our routing structures provide a better trade-off between the overall communication cost and estimation accuracy. Comparing to more conventional sensor selection and fixed routing algorithms, our proposed joint sensor selection and routing algorithms yield a significant amount of energy saving.