QoS Categories Activeness-Aware Adaptive EDCA Algorithm for Dense IoT Networks

IEEE 802.11 networks have a great role to play in supporting and deploying of the Internet of Things (IoT). The realization of IoT depends on the ability of the network to handle a massive number of stations and transmissions, and to support Quality of Service (QoS). IEEE 802.11 networks enable the QoS by applying the Enhanced Distributed Channel Access (EDCA) with static parameters regardless of existing network capacity or which Access Category (AC) of QoS is already active. Our objective in this paper is to improve the efficiency of the uplink access in 802.11 networks; therefore we proposed an algorithm called QoS Categories Activeness-Aware Adaptive EDCA Algorithm (QCAAAE) which adapts Contention Window (CW) size, and Arbitration Inter-Frame Space Number (AIFSN) values depending on the number of associated Stations (STAs) and considering the presence of each AC. For different traffic scenarios, the simulation results confirm the outperformance of the proposed algorithm in terms of throughput (increased on average 23%) and retransmission attempts rate (decreased on average 47%) considering acceptable delay for sensitive delay services.Comment: 17 pages, 10 figure

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Efficient email classification approach based on semantic methods

Emails have become one of the major applications in daily life. The continuous growth in the number of email users has led to a massive increase of unsolicited emails, which are also known as spam emails. Managing and classifying this huge number of emails is an important challenge. Most of the approaches introduced to solve this problem handled the high dimensionality of emails by using syntactic feature selection. In this paper, an efficient email filtering approach based on semantic methods is addressed. The proposed approach employs the WordNet ontology and applies different semantic based methods and similarity measures for reducing the huge number of extracted textual features, and hence the space and time complexities are reduced. Moreover, to get the minimal optimal features’ set, feature dimensionality reduction has been integrated using feature selection techniques such as the Principal Component Analysis (PCA) and the Correlation Feature Selection (CFS). Experimental results on the standard benchmark Enron Dataset showed that the proposed semantic filtering approach combined with the feature selection achieves high computational performance at high space and time reduction rates. A comparative study for several classification algorithms indicated that the Logistic Regression achieves the highest accuracy compared to Naïve Bayes, Support Vector Machine, J48, Random Forest, and radial basis function networks. By integrating the CFS feature selection technique, the average recorded accuracy for the all used algorithms is above 90%, with more than 90% feature reduction. Besides, the conducted experiments showed that the proposed work has a highly significant performance with higher accuracy and less time compared to other related works. Keywords: Email classification, Spam, WordNet ontology, Semantic similarity, Features reductio

Shared Sensor Networks Fundamentals, Challenges, Opportunities, Virtualization Techniques, Comparative Analysis, Novel Architecture and Taxonomy

The rabid growth of today’s technological world has led us to connecting every electronic device worldwide together, which guides us towards the Internet of Things (IoT). Gathering the produced information based on a very tiny sensing devices under the umbrella of Wireless Sensor Networks (WSNs). The nature of these networks suffers from missing sharing among them in both hardware and software, which causes redundancy and more budget to be used. Thus, the appearance of Shared Sensor Networks (SSNs) provides a real modern revolution in it. Where it targets making a real change in its nature from domain specific networks to concurrent running domain networks. That happens by merging it with the technology of virtualization that enables the sharing feature over different levels of its hardware and software to provide the optimal utilization of the deployed infrastructure with a reduced cost. This article is concerned with surveying the idea of SSNs, the difference between it and the traditional WSNs, the requirements for its construction, challenges facing it, and the opportunities that are provided by it, then describing our proposed architectures. As a result of using virtualization technology as a basic block in building SSNs, using different types of virtualization will produce different types of SSNs that will give different usages to it. This article proposes a novel approach of taxonomy for SSNs that is based on the used virtualization techniques, and it describes the needs and usages of each one. It presents a wide array of previously proposed solutions comparing them to each other and a brief description of the issues addressed by each category of that taxonomy. Additionally, the shared sensor architecture and shared network architecture were depicted. Finally, some of its applications in some daily life fields are listed

Interactive Effects of Arbuscular Mycorrhizal Inoculation with Nano Boron, Zinc, and Molybdenum Fertilization on Stevioside Contents of Stevia (Stevia rebaudiana, L.) Plants

Stevia (Stevia rebaudiana, L.) is receiving increasing global interest as a diabetes-focused herb associated with zero-calorie stevioside sweetener glycoside production. This study was conducted to determine whether the arbuscular mycorrhiza (AM), as a biofertilizer integrated with nano boron (B), zinc (Zn), and molybdenum (Mo), would improve stevia growth and stevioside content. A factorial experiment with four replicates was conducted to evaluate the effect of AM at 0, 150, and 300 spore/g soil and three nano microelements B at 100 mg/L, Zn at 100 mg/L, and Mo at 40 mg/L on growth performance, stevioside, mineral contents, and biochemical contents of stevia. Results indicated that the combination of AM at 150 and B at 100 mg/L significantly increased plant height, number of leaves, fresh and dry-stem, and herbal g/plant during the 2019 and 2020 growing seasons. Chlorophyll content was increased by the combination between AM at 150 spore/g soil and B at 100 mg/L during both seasons. Stevioside content in leaves was increased by AM at 150 spore/g soil and B at 100 mg/L during the second season. In addition, N, P, K, Zn, and B in the leaf were increased by applying the combination of AM and nano microelements. Leaf bio constituent contents were increased with AM at 150 spore/g soil and B at 100 mg/L during both seasons. The application of AM and nano B can be exploited for high growth, mineral, and stevioside contents as a low-calorie sweetener product in stevia