Structural changes in data communication in wireless sensor networks

Cabral, Raquel; Aquino, André L. L.; Frery, Alejandro C.; Rosso, Osvaldo A.; Ramírez, J.A.

doi:10.2478/s11534-013-0293-2

Cited by 3 publications

(2 citation statements)

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“…(2010) and Cabral et al. (2013). Large synthetic instances based on Binomial Tree (42 instances): Given that the optimal solution for large MBT instances is often unknown, we generated a new benchmark with known optimal solutions using the following procedure.…”

Section: Computational Resultsmentioning

confidence: 94%

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A matheuristic approach for the minimum broadcast time problem using a biased random‐key genetic algorithm

Lima

Aquino

Nogueira

et al. 2022

Int Trans Operational Res

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The Minimum Broadcast Time (MBT) is a well‐known data dissemination problem whose goal is to find a broadcast scheme that minimizes the number of steps needed to execute the broadcast operation. The problem has many applications in distributed systems and, in particular, the Industry 4.0 domain. Because Industry 4.0 applications rely primarily on the use of large‐scale machine to machine communications, they need data dissemination techniques that combine high reliability with low communication latency. This work proposes a Biased Random‐Key Genetic Algorithm and a matheuristic for the MBT. We carry out experiments with our algorithms on instances commonly used in the literature (hypercube, shuffle exchange, cube‐connected cycles, de Bruijn, Harary graphs), and also on massive synthetic instances (up to 1000 vertices), allowing to cover many possibilities of real industry topologies. Our proposal is also compared with state‐of‐the‐art exact methods and heuristics. Experimental results show that our algorithm is able to outperform the best‐known heuristics for the MBT, and also that it is a very good alternative for large instances that cannot be solved by current exact methods.

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Section: Computational Resultsmentioning

confidence: 94%

“…We have chosen the small-world model because it is commonly used to represent communication networks in industrial scenarios, as suggested in Guidoni et al (2010) and Cabral et al (2013).…”

Section: Instancesmentioning

confidence: 99%

A matheuristic approach for the minimum broadcast time problem using a biased random‐key genetic algorithm

Lima

Aquino

Nogueira

et al. 2022

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

Variability analysis of complex networks measures based on stochastic distances

Cabral

Frery

Ramírez

2014

Physica A: Statistical Mechanics and its Applications

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Complex networks can model the structure and dynamics of different types of systems. It has been shown that they are characterized by a set of measures.In this work, we evaluate the variability of complex networks measures face to perturbations and, for this purpose, we impose controlled perturbations and quantify their effect. We analyze theoretical models (random, small-world and scale-free) and real networks (a collaboration network and a metabolic networks) along with the shortest path length, vertex degree, local cluster coefficient and betweenness centrality measures.In such analysis, we propose the use of three stochastic quantifiers: the Kullback-Leibler divergence and the Jensen-Shannon and Hellinger distances.The sensitivity of these measures was analyzed with respect to the following perturbations: edge addition, edge removal, edge rewiring and node removal, all of them applied at different intensities. The results reveal that the evalu- * Corresponding author. ated measures are influenced by these perturbations. Additionally, hypotheses tests were performed to verify the behavior of the degree distribution to identify the intensity of the perturbations that leads to break this property.Complex networks are systems whose structure is irregular, complex and dynamically evolving in time [1]. In recent years, a number of measures have been developed to quantify the structure and behavior of such systems, which provide a framework that allows its characterization, analysis and modeling, reflecting different features of the network such as, connectivity, centrality, cycles, distances, among others.The choice of an appropriate measure for the characterization of a network is performed by evaluating its behavior, and depends mainly on three factors: (i) data availability, (ii) storage capacity and processing and (iii) interest in characterizing the behavior of the measures. In this procedure, the network is mapped into a feature vector [2]; however, in many cases the mapping is not complete and does not accurately describe the network's real structure. In such cases, it is important to evaluate the performance of the measures when unexpected changes occur in the networks. For instance, what is the behavior of the measures if the network loses links or nodes? or, do these changes break the properties used to describe the network structure? To address such problems, it is necessary to compare different states of the network. In this work, we investigate the use of methods from the Information Theory, in particular the concept of Stochastic Quantifiers, as means to quantify the

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Data Sampling in Sensor Networks: New Trends in Smart City Applications

Aquino¹

2018

Int J Sens Netw Data Commun

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Structural changes in data communication in wireless sensor networks

Cited by 3 publications

References 20 publications

A matheuristic approach for the minimum broadcast time problem using a biased random‐key genetic algorithm

A matheuristic approach for the minimum broadcast time problem using a biased random‐key genetic algorithm

Variability analysis of complex networks measures based on stochastic distances

Data Sampling in Sensor Networks: New Trends in Smart City Applications

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