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Learning Nagios 4
This book will introduce Nagios to readers who are interested in monitoring their systems. All the concepts in the book are explained in a simplified manner, presented in an easy-to-understand language with lots of tips, tricks, and illustrations. This book is great for system administrators interested in using Nagios to monitor their systems. It will also help professionals who have already worked with earlier versions of Nagios to understand the new features of Nagios 4 and provides usable solutions to real-life problems related to Nagios administration. To effectively use this book, system administration knowledge is required. If you want to create your own plug-ins, knowledge of scripting languages like Perl, shell and Python is expected.
eBook
Cacti 0.8 Network Monitoring
In Detail Cacti is a network monitoring tool that provides graphic solutions to your everyday monitoring issues. It has a wide variety of features and misusing them can mean that you are not monitoring your network as closely as you think. This book takes you through all of the key features of Cacti and shows how to use them for maximum effectiveness. This book will teach you how to use Cacti effectively to monitor your network through its web interface leaving aside all the heavy chunks of code. You will be introduced to all the features of Cacti in an easy-to-understand format. This book introduces Cacti and goes through its complete installation and setup. After a quick look, it will teach you to use Cacti's amazing graph templating and user management features. You will learn to customize graphs and make them better looking and easier to understand. It will teach you to provide the paths to any external script or command using Cacti. Then it will take you through importing and managing new templates and also customizing them. Creating users and assigning permissions to them is the next step in this book. Towards the end, you will learn to take backups and restore the system. This book teaches you to monitor your network, customize the output graph and input source, and take backups Approach With loads of screenshots and illustrations and easy step-by-step instructions, this book is ideal for beginners in the network monitoring business. Who this book is for This book is for anyone who wants to manage a network using Cacti. You don't have to be a Linux Guru to use this book.
eBook
Cacti 0.8 network monitoring
With loads of screenshots and illustrations and easy step-by-step instructions, this book is ideal for beginners in the network monitoring business. This book is for anyone who wants to manage a network using Cacti. You don't have to be a Linux Guru to use this book.
eBook
A Comprehensive Review of Recent Developments in VANET for Traffic, Safety & Remote Monitoring Applications
Strategic integration of Wireless Sensor Networks (WSNs) and IoT (Internet of Things) into VANET infrastructure is crucial for ensuring vehicular safety, mobility management, and vehicular applications. The integration collects information on traffic and road conditions without relying on traditional internet connectivity. It also addresses applications such as early warnings in areas with limited coverage, safety and health emergency messages in highly congested zones, and air monitoring without depending on traditional TCP/IP internet connectivity. This article provides a comprehensive view of network technologies, data acquisition devices, clustering techniques, and energy-efficient routing protocols to optimize Vehicle-to-Everything (V2X) communications in VANETs. This study also addresses how to leverage the frequency channels of the 802.11p protocol and expands the possibilities for developing numerous applications dedicated to remote ambient, traffic and safety monitoring without compromising network performance. Moreover, the recent developments of clustering algorithms and energy-efficient schemes for these VANET applications are analyzed from a novel perspective.
Journal Article
End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring
Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.
Journal Article
Cloud-Based Remote Patient Monitoring System with Abnormality Detection and Alert Notification
The availability, accessibility, and affordability of good healthcare services to remote, rural, and developing parts of the world is a major challenge. To resolve this dynamically growing issue of global importance, there is a need to devise an integrated and intelligent solution for the delivery of health monitoring services along with abnormality detection and alert notification. In this work, a remote patient monitoring system (RPMS) has been presented. Internet of things (IoT) and integrated cloud computing technologies are used for the implementation. The system can continuously measure different physiological parameters with the appropriate degree of accuracy required by medical standards. A mobile application has been developed for Android devices, which acts as a gateway between RPMS and the Cloud. The developed mobile application offers visualization and storage of physiological parameters locally as well as in Cloud along with real-time data transmission for remote monitoring and further analysis. In case of an abnormal event and emergency, the system can generate an alert notification to the local user and remote supervisor. The RPMS has been implemented and validated on the state-of-the-art patient monitoring system. A series of tests have been carried out to validate the system’s effectiveness and reliability for measuring different physiological parameters and its remote monitoring in real-time. In addition to this performance analysis of the cloud-based system for real-time data transmission has also been carried out.
Journal Article
wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool
Background
Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (
wTO
). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network.
Results
Here, we present an
R
package for calculating the weighted topological overlap (
wTO
), that, in contrast to existing packages, explicitly addresses the sign of the
wTO
values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of
p
-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (
CN
) from two or more networks into our
R
package. To graphically inspect the resulting networks, the
R
package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set.
Conclusion
In this work, we developed a software package that allows the computation of
wTO
networks,
CN
s and a visualization tool in the
R
statistical environment. It is publicly available on CRAN repositories under the GPL −2 Open Source License (
https://cran.r-project.org/web/packages/wTO/
).
Journal Article
Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images
With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.
Journal Article
Remote Early Warning System for Mountain Floods with Robust ZigBee Wireless Networks
In recent years, mountain torrents (sudden floods in a gully) occur frequently, and the losses caused by various natural disasters are increasing. Remote disaster warning has become an important part of disaster prevention and reduction. However, various disaster warning systems based on sensor networks face the problem of poor robustness. Therefore, a remote early warning system for mountain torrents and natural disasters is designed in this paper considering the robustness of ZigBee wireless networks. The system relies on specific sensors for rainfall monitoring, water level monitoring, and vibration to design the hardware of a remote early warning system for mountain torrents and natural disasters. In software design, through the implementation of a sensor data compression algorithm, the robustness of mass data acquisition process is increased, and corresponding early warnings are realized according to the structure of mountain torrent natural disaster monitoring algorithm development. According to monitoring data and the early warning mode, possible geological disasters can be obtained to achieve the purpose of robust early warning of mountain torrents and natural disasters. The experimental results show that the difference between the X- and Y-axis coordinates of the location area obtained by the designed system and the actual flash flood area is small, and data from both humidity and air pressure obtained after the application of the system in this paper are close to the true values.
Journal Article