Random-tree Diameter and the Diameter-constrained MST

Abdalla, Ayman M.; Deo, Narsingh

doi:10.1080/00207160211289

Cited by 22 publications

(25 citation statements)

References 8 publications

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“…In contrast, since the embedded trees [9], Gaussian elimination [5], and loopy BP [7] algorithms rely on sequential message passing either on a tree (for the first two methods) or on the entire graph (for loopy BP), their timeper-iteration will increase with the diameter of the n e t~o r k ,~ which grows with N [16].…”

Section: Scalability and Self-organizationmentioning

confidence: 99%

Robust distributed estimation in sensor networks using the embedded polygons algorithm

Delouille

Neelamani

Baraniuk

2004

Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks

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We propose a new iterative distributed algorithm for linear minimum mean-squared-error (LMMSE) estimation in sensor networks whose measurements follow a Gaussian hidden Markov graphical model with cycles. The embedded polygons algorithm decomposes a loopy graphical model into a number of linked embedded polygons and then applies a parallel block Gauss-Seidel iteration comprising local LMMSE estimation on each polygon (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and polygons. The algorithm is robust to temporary communication faults such as link failures and sleeping nodes and enjoys guaranteed convergence under mild conditions. A simulation study indicates that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually be counterproductive.

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Section: Scalability and Self-organizationmentioning

confidence: 99%

Robust distributed estimation in sensor networks using the embedded polygons algorithm

Delouille

Neelamani

Baraniuk

2004

Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks

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“…On the other hand, since embedded trees [7] and Gaussian elimination [9] rely on sequential message passing on a tree, their time per iteration will increase with the diameter of the network, 7 which grows with [19].…”

Section: B Convergencementioning

confidence: 99%

Robust Distributed Estimation Using the Embedded Subgraphs Algorithm

Delouille

Neelamani

Baraniuk

2006

IEEE Trans. Signal Process.

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Abstract-We propose a new iterative, distributed approach for linear minimum mean-square-error (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a number of linked embedded subgraphs and applies the classical parallel block Jacobi iteration comprising local LMMSE estimation in each subgraph (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and subgraphs. Our primary application is sensor networks, where the model encodes the correlation structure of the sensor measurements, which are assumed to be Gaussian. The resulting LMMSE estimation problem involves a large matrix inverse, which must be solved in-network with distributed computation and minimal intersensor communication. By invoking the theory of asynchronous iterations, we prove that ESA is robust to temporary communication faults such as failing links and sleeping nodes, and enjoys guaranteed convergence under relatively mild conditions. Simulation studies demonstrate that ESA compares favorably with other recently proposed algorithms for distributed estimation. Simulations also indicate that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, sensor network energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually prove counterproductive. Our results can be replicated using MATLAB code from www.dsp.rice.edu/software.

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“…This problem is NP-Hard [12], therefore we employ a heuristic algorithm to produce the k-hop execution plan efficiently. Specifically, we adapt a solution proposed by Abdalla et al [1] for the diameter-constrained minimum spanning tree problem, since the hop-constrained maximum spanning tree problem is a simplification of the bounded-diameter minimum spanning tree problem [15]. Notice that the dummy node added to the SD-graph ensures that there always exists at least one spanning tree with maximum height k (k ≥ 1).…”

Section: K-hop Execution Planmentioning

confidence: 99%

“…However, the filter points may fail to prune any point of a server depending on the data distribution. When no gain can be obtained from querying the servers consecutively, the parallelism should be preserved, in order to minimize the latency and therefore also the response time 1 . To address this tradeoff, this paper introduces a novel framework, called SkyPlan, for processing distributed skyline queries that generates execution plans aiming at optimizing the performance of query processing.…”

Section: Skyplan Frameworkmentioning

confidence: 99%

Efficient execution plans for distributed skyline query processing

Rocha-Junior

Vlachou

Doulkeridis

et al. 2011

Proceedings of the 14th International Conference on Extending Database Technology

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In this paper, we study the generation of efficient execution plans for skyline query processing in large-scale distributed environments. In such a setting, each server stores autonomously a fraction of the data, thus all servers need to process the skyline query. An execution plan defines the order in which the individual skyline queries are processed on different servers, and influences the performance of query processing. Querying servers consecutively reduces the amount of transferred data and the number of queried servers, since skyline points obtained by one server prune points in the subsequent servers, but also increases the latency of the system. To address this trade-off, we introduce a novel framework, called SkyPlan, for processing distributed skyline queries that generates execution plans aiming at optimizing the performance of query processing. Thus, we quantify the gain of querying consecutively different servers. Then, execution plans are generated that maximize the overall gain, while also taking into account additional objectives, such as bounding the maximum number of hops required for the query or balancing the load on different servers fairly. Finally, we present an algorithm for distributed processing based on the generated plan that continuously refines the execution plan during in-network processing. Our framework consistently outperforms the state-of-the-art algorithm.

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Random-tree Diameter and the Diameter-constrained MST

Cited by 22 publications

References 8 publications

Robust distributed estimation in sensor networks using the embedded polygons algorithm

Robust distributed estimation in sensor networks using the embedded polygons algorithm

Robust Distributed Estimation Using the Embedded Subgraphs Algorithm

Efficient execution plans for distributed skyline query processing

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