Departamento de Ingeniería de Telecomunicación
URI permanente para esta comunidadhttps://hdl.handle.net/10953/39
En esta Comunidad se recogen los documentos generados por el Departamento de Ingeniería de Telecomunicación y que cumplen los requisitos de Copyright para su difusión en acceso abierto.
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Examinando Departamento de Ingeniería de Telecomunicación por Autor "Cuevas-Martínez, Juan-Carlos"
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Ítem A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime ofWireless Sensor Networks(MDPI, 2020-04-18) Yuste-Delgado, Antonio-Jesús; Cuevas-Martínez, Juan-Carlos; Triviño-Cabrera, AliciaClustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station (BS) to select the CHs. To do so, the BS requires extensive data from the nodes, which sometimes need complex hardware inside each node or a significant number of control messages. Alternatively, the nodes in distributed clustering algorithms decide about which the CHs are by exchanging information among themselves. Both centralized and distributed clustering algorithms usually alternate the nodes playing the role of the CHs to dynamically balance the energy consumption among all the nodes in the network. This paper presents a distributed approach to form the clusters dynamically, but it is occasionally supported by the Base Station. In particular, the Base Station sends three messages during the network lifetime to reconfigure the skip value of the network. The skip, which stands out as the number of rounds in which the same CHs are kept, is adapted to the network status in this way. At the beginning of each group of rounds, the nodes decide about their convenience to become a CH according to a fuzzy-logic system. As a novelty, the fuzzy controller is as a Tagaki–Sugeno–Kang model and not a Mandami-one as other previous proposals. The clustering algorithm has been tested in a wide set of scenarios, and it has been compared with other representative centralized and distributed fuzzy-logic based algorithms. The simulation results demonstrate that the proposed clustering method is able to extend the network operability.Ítem Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks(MDPI, Basel, Switzerland., 2024-08-27) Muñoz-Exposito, Jose-Enrique; Yuste-Delgado, Antonio-Jesús; Triviño-Cabrera, Alicia; Cuevas-Martínez, Juan-CarlosWireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN’s goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.