Clustering in wireless sensor networks has been widely discussed in the literature as a strategy to reduce power consumption. However, aspects such as cluster formation and cluster head (CH) node assignment strategies have a significant impact on quality of service, as energy savings imply restrictions in application usage and data traffic within the network. Regarding the first aspect, this article proposes a hierarchical routing protocol based on the k-d tree algorithm, taking a partition data structure of the space to organize nodes into clusters. For the second aspect, we propose a reactive mechanism for the formation of CH nodes, with the purpose of improving delay, jitter, and throughput, in contrast with the low-energy adaptive clustering hierarchy/hierarchy-centralized protocol and validating the results through simulation.
Opportunistic networks (OppNet) a new paradigm emerges after the great advancement of wireless technology and MANETs (Mobile Ad-hoc Networks). Store-Carry-forward phenomena is used to route the messages in OppNets; therefore, it is called delay tolerant network (DTNs). OppNet is such a heterogeneous flexible network that works in hurricane affected regions and volcanoes environment. The objective of this article is to determine, evaluate, elect and synthesize all terrific quality research work on the basis of Research Questions (RQ) which is significant to use for opportunistic routing. Systematic literature review on state-of-the-art researches on routing protocols are presented with comparative analysis. To eliciting relevant work for review, advanced exploration is conducted on contrasting digital libraries. This work gives the opportunity to the readers to understand what has been done in field of Opportunistic Routing (OR), what parameters effect the performance of protocol, name of the organization; who works in this field. At last, issues, challenges, and upcoming future terms of this network are discussed.
Predictive maintenance in machines aims to anticipate failures. In rotating machines, the component that suffers the most wear and tear is the bearings. Currently, based on the Industry 4.0 paradigm, advances have been made in obtaining data, specifically, vibration signals that can be used to predict deterioration using various techniques. In this study, we have applied vibration analysis to obtain features that can be used in an optimal Machine Learning model using a public dataset from CWRU, widely used in research, which contains data on bearing failures. The main objective of this research is to detect bearing failures using a minimum set of observations and selecting the minimum number of features. To achieve this, frequency domain vibration analysis, combined with envelope analysis, is utilized as an effective method for detecting bearing failures. The results were further improved by incorporating an optimal bandwidth determined using the kurtogram. When the results of the envelope analysis are applied to various machine learning models, using the calculated amplitudes as predictors, the Kernel Naive Bayes model achieved an accuracy of 94.4%. Meanwhile, the Decision Tree (Fine Tree) and KNN (Fine KNN) models demonstrate exceptional accuracy, achieving a perfect accuracy rate of 100%.
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