Applicability of Generalized Metropolis-Hastings Algorithm in Wireless Sensor Networks

Kenyeres, Martin; Kenyeres, Jozef

doi:10.1109/eurocon.2019.8861554

Cited by 3 publications

(13 citation statements)

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“…In the literature, one can find many deterministic approaches, e.g., the Maximum Degree weights algorithm, the Metropolis-Hastings algorithm (MH), the Best Constant weights algorithms, the Convex Optimized weights algorithm, etc. [1,13,19]. Probably, the most frequently quoted stochastic consensus-based algorithms are the Push-Sum algorithm, the Push-Pull algorithm, the Broadcast gossip algorithm, the Pairwise gossip algorithm, the Geographic gossip algorithm, etc.…”

Section: Consensus-based Algorithms For Data Aggregationmentioning

confidence: 99%

“…In this paper, we focus our attention on MH, which was proposed by Nicolas Metropolis et al in the 1950s and extended by Wilfred Keith Hastings approximately twenty years later [1]. Since its definition, it has found the application in various areas, e.g., simulating multivariate distributions, block-at-a-time scans, acceptance-rejection sampling, etc.…”

Section: Generalized Metropolis-hastings For Data Aggregationmentioning

confidence: 99%

“…Since its definition, it has found the application in various areas, e.g., simulating multivariate distributions, block-at-a-time scans, acceptance-rejection sampling, etc. [1]. Lately, Schwarz et al have defined its generalized variant (referred to as the generalized Metropolis-Hastings algorithm and labeled as GMH) and derived the convergence conditions over arbitrary graphs including critical topologies such as bipartite regular graphs [26].…”

Section: Generalized Metropolis-hastings For Data Aggregationmentioning

confidence: 99%

“…This algorithm poses a multi-functional distributed linear consensus algorithm able to estimate various aggregate functions, namely the arithmetic mean, the sum, and the graph order. Its weight matrix is symmetric and doubly stochastic, and each graph edge is allocated a weight determined by the degrees of two linked vertices [1]. Thus, this algorithm represents a fully-distributed approach in contrast to many other distributed consensus-based algorithms for data aggregation that require global information for the optimal initial configuration and the proper operating (e.g., the maximum degree, the Laplacian spectrum, etc.)…”

Section: Generalized Metropolis-hastings For Data Aggregationmentioning

confidence: 99%

“…This paper is an extension of work originally presented in the Proceedings of the EUROCON 2019 -18th International Conference on Smart Technologies [1].…”

Section: Introductionmentioning

confidence: 97%

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Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Kenyeres¹,

Kenyeres²

2020

Adv. sci. technol. eng. syst. j.

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Over the last decades, numerous distributed consensus-based algorithms have found a wide application as a complementary mechanism for data aggregation in wireless sensor networks. In this paper, we provide an analysis of the generalized Metropolis-Hastings algorithm for data aggregation with a fully-distributed stopping criterion. The goal of the implemented stopping criterion is to effectively bound the algorithm execution over wireless sensor networks. In this paper, we analyze and compare the performance of the mentioned algorithm with various mixing parameters for distributed averaging, for distributed summing, and for distributed graph order estimation. The algorithm is examined under different configurations of the implemented stopping criterion over random geometric graphs by applying two metrics, namely the mean square error and the number of the iterations for the consensus. The goal of this paper is to examine the applicability of the analyzed algorithm with the stopping criterion to estimating the investigated aggregate functions in wireless sensor networks. In addition, the performance of the algorithm is compared to the average consensus algorithm bounded by the same stopping criterion.

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Section: Consensus-based Algorithms For Data Aggregationmentioning

confidence: 99%

Section: Generalized Metropolis-hastings For Data Aggregationmentioning

confidence: 99%

Section: Generalized Metropolis-hastings For Data Aggregationmentioning

confidence: 99%

Section: Generalized Metropolis-hastings For Data Aggregationmentioning

confidence: 99%

“…This paper is an extension of work originally presented in the Proceedings of the EUROCON 2019 -18th International Conference on Smart Technologies [1].…”

Section: Introductionmentioning

confidence: 97%

See 3 more Smart Citations

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Kenyeres¹,

Kenyeres²

2020

Adv. sci. technol. eng. syst. j.

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Generalized Metropolis-Hastings Algorithm for Distributed Averaging with Uniform Quantization Scheme

Kenyeres

Kenyeres²

2019

2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA)

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Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

et al. 2023

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The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%.

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Applicability of Generalized Metropolis-Hastings Algorithm in Wireless Sensor Networks

Cited by 3 publications

References 14 publications

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Generalized Metropolis-Hastings Algorithm for Distributed Averaging with Uniform Quantization Scheme

Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

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