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This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design. To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering. Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector, closeness, betweenness, and degree. Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods.This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/04234/2020); by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Agreement, through the European Regional Development Fund (ERDF); also by FCT and the ESF (European Social Fund) through the Regional Operational Programme (ROP) Norte 2020, under PhD grant 2020.06685.BD.info:eu-repo/semantics/publishedVersio

Corner Centrality of Nodes in Multilayer Networks: A Case Study in the Network Analysis of Keywords

In this paper, we present a new method to measure the nodes’ centrality in a multilayer network. The multilayer network represents nodes with different relations between them. The nodes have an initial relevance or importance value. Then, the node’s centrality is obtained according to this relevance along with its relationship to other nodes. Many methods have been proposed to obtain the node’s centrality by analyzing the network as a whole. In this paper, we present a new method to obtain the centrality in which, in the first stage, every layer would be able to define the importance of every node in the multilayer network. In the next stage, we would integrate the importance given by each layer to each node. As a result, the node that is perceived with a high level of importance for all of its layers, and the neighborhood with the highest importance, obtains the highest centrality score. This score has been named the corner centrality. As an example of how the new measure works, suppose we have a multilayer network with different layers, one per research area, and the nodes are authors belonging to an area. The initial importance of the nodes (authors) could be their h-index. A paper published by different authors generates a link between them in the network. The authors can be in the same research area (layer) or different areas (different layers). Suppose we want to obtain the centrality measure of the authors (nodes) in a concrete area (target layer). In the first stage, every layer (area) receives the importance of every node in the target layer. Additionally, in the second stage, the relative importance given for every layer to every node is integrated with the importance of every node in its neighborhood in the target layer. This process can be repeated with every layer in the multilayer network. The method proposed has been tested with different configurations of multilayer networks, with excellent results. Moreover, the proposed algorithm is very efficient regarding computational time and memory requirements

Towards Agent-Based Model Specification of Smart Grid: A Cognitive Agent-Based Computing Approach

A smart grid can be considered as a complex network where each node represents a generation unit or a consumer, whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling paradigm. The agent-based modeling is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the agent-based modeling paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of the model. The model specification is important not only for understanding but also for replication of the model. To fill this gap, this study focuses on specification methods for smart grid modeling. We adopt two specification methods named as Overview, design concept, and details and Descriptive agent-based modeling. By using specification methods, we provide tutorials and guidelines for model developing of smart grid starting from conceptual modeling to validated agent-based model through simulation. The specification study is exemplified through a case study from the smart grid domain. In the case study, we consider a large set of network, in which different consumers and power generation units are connected with each other through different configuration. In such a network, communication takes place between consumers and generating units for energy transmission and data routing. We demonstrate how to effectively model a complex system such as a smart grid using specification methods. We analyze these two specification approaches qualitatively as well as quantitatively. Extensive experiments demonstrate that Descriptive agent-based modeling is a more useful approach as compared with Overview, design concept, and details method for modeling as well as for replication of models for the smart grid