An Overview of Medium Access Control and Radio Duty Cycling Protocols for Internet of Things

The Internet of Things (IoT) applications such as smart grids, smart agriculture, smart cities, and e-healthcare are popular nowadays. Generally, IoT end devices are extremely sensitive to the utilization of energy. The medium access control (MAC) layer is responsible for coordination and access of the IoT devices. It is essential to design an efficient MAC protocol for achieving high throughput in IoT. Duty cycling is a fundamental process in wireless networks and also an energy-saving necessity if nodes are required to operate for more than a few days. Numerous MAC protocols along with different objectives have been proposed for the IoT. However, to the best of our knowledge, only limited work has been performed dedicated to covering MAC and radio duty cycling (RDC). Therefore, in this study, we propose a systematic cataloging system and use if to organize the most important MAC and RDC proposals. In this catalog, each protocol has been categorized into main ideas, advantages, applications, limitations, innovative features, and potential future improvements. Our critical analysis is different from previous research studies, as we have fully covered all recent studies in this domain. We discuss challenges and future research directions

A Step toward Next-Generation Advancements in the Internet of Things Technologies

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world

A Novel Cluster Matching-Based Improved Kernel Fisher Criterion for Image Classification in Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is a popular approach to reducing distributional discrepancies between labeled source and the unlabeled target domain (TD) in machine learning. However, current UDA approaches often align feature distributions between two domains explicitly without considering the target distribution and intra-domain category information, potentially leading to reduced classifier efficiency when the distribution between training and test sets differs. To address this limitation, we propose a novel approach called Cluster Matching-based Improved Kernel Fisher criterion (CM-IKFC) for object classification in image analysis using machine learning techniques. CM-IKFC generates accurate pseudo-labels for each target sample by considering both domain distributions. Our approach employs K-means clustering to cluster samples in the latent subspace in both domains and then conducts cluster matching in the TD. During the model component training stage, the Improved Kernel Fisher Criterion (IKFC) is presented to extend cluster matching and preserve the semantic structure and class transitions. To further enhance the performance of the Kernel Fisher criterion, we use a normalized parameter, due to the difficulty in solving the characteristic equation that draws inspiration from symmetry theory. The proposed CM-IKFC method minimizes intra-class variability while boosting inter-class variants in all domains. We evaluated our approach on benchmark datasets for UDA tasks and our experimental findings show that CM-IKFC is superior to current state-of-the-art methods

Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection

Cyberbullying has emerged as a pervasive issue in the digital age, necessitating advanced techniques for effective detection and mitigation. This research explores the integration of word embeddings, emotional features, and federated learning to address the challenges of centralized data processing and user privacy concerns prevalent in previous methods. Word embeddings capture semantic relationships and contextual information, enabling a more nuanced understanding of text data, while emotional features derived from text extend the analysis to encompass the affective dimension, enhancing cyberbullying identification. Federated learning, a decentralized learning paradigm, offers a compelling solution to centralizing sensitive user data by enabling collaborative model training across distributed devices, preserving privacy while harnessing collective intelligence. In this study, we conduct an in-depth investigation into the fusion of word embeddings, emotional features, and federated learning, complemented by the utilization of BERT, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) models. Hyperparameters and neural architecture are explored to find optimal configurations, leading to the generation of superior results. These techniques are applied in the context of cyberbullying detection, using publicly available multi-platform (social media) cyberbullying datasets. Through extensive experiments and evaluations, our proposed framework demonstrates superior performance and robustness compared to traditional methods. The results illustrate the enhanced ability to identify and combat cyberbullying incidents effectively, contributing to the creation of safer online environments. Particularly, the BERT model consistently outperforms other deep learning models (CNN, DNN, LSTM) in cyberbullying detection while preserving the privacy of local datasets for each social platform through our improved federated learning setup. We have provided Differential Privacy based security analysis for the proposed method to further strengthen the privacy and robustness of the system. By leveraging word embeddings, emotional features, and federated learning, this research opens new avenues in cyberbullying research, paving the way for proactive intervention and support mechanisms. The comprehensive approach presented herein highlights the substantial strengths and advantages of this integrated methodology, setting a foundation for future advancements in cyberbullying detection and mitigation

A robust regression-based stock exchange forecasting and determination of correlation between stock markets

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges-New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies-Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)

Analysis of codon usage patterns in Hirudinaria manillensis reveals a preference for GC-ending codons caused by dominant selection constraints

Abstract Background Hirudinaria manillensis is an ephemeral, blood-sucking ectoparasite, possessing anticoagulant capacities with potential medical applications. Analysis of codon usage patterns would contribute to our understanding of the evolutionary mechanisms and genetic architecture of H. manillensis, which in turn would provide insight into the characteristics of other leeches. We analysed codon usage and related indices using 18,000 coding sequences (CDSs) retrieved from H. manillensis RNA-Seq data. Results We identified four highly preferred codons in H. manillensis that have G/C-endings. Points generated in an effective number of codons (ENC) plot distributed below the standard curve and the slope of a neutrality plot was less than 1. Highly expressed CDSs had lower ENC content and higher GC content than weakly expressed CDSs. Principal component analysis conducted on relative synonymous codon usage (RSCU) values divided CDSs according to GC content and divided codons according to ending bases. Moreover, by determining codon usage, we found that the majority of blood-diet related genes have undergone less adaptive evolution in H. manillensis, except for those with homologous sequences in the host species. Conclusions Codon usage in H. manillensis had an overall preference toward C-endings and indicated that codon usage patterns are mediated by differential expression, GC content, and biological function. Although mutation pressure effects were also notable, the majority of genetic evolution in H. manillensis was driven by natural selection