Mobile-IP ad-hoc network MPLS-based with QoS support.

The support for Quality of Service (QoS) is the main focus of this thesis. Major issues and challenges for Mobile-IP Ad-Hoc Networks (MANETs) to support QoS in a multi-layer manner are considered discussed and investigated through simulation setups. Different parameters contributing to the subjective measures of QoS have been considered and consequently, appropriate testbeds were formed to measure these parameters and compare them to other schemes to check for superiority. These parameters are: Maximum Round-Trip Delay (MRTD), Minimum Bandwidth Guaranteed (MBG), Bit Error Rate (BER), Packet Loss Ratio (PER), End-To-End Delay (ETED), and Packet Drop Ratio (PDR) to name a few. For network simulations, NS-II (Network Simulator Version II) and OPNET simulation software systems were used.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .A355. Source: Masters Abstracts International, Volume: 44-03, page: 1444. Thesis (M.Sc.)--University of Windsor (Canada), 2005

A multilayer non-repudiation system: a Suite-B approach

&nbsp;Security provisioning is an essential part in the design of any communication systems, which becomes more critical for wireless systems. The consideration and comparisons of security algorithms in various Open Systems Interconnection layers is a difficult task, because there are many performance metrics involved. The aim of this novel research article is to present research results for the design of a wireless system revolving around the practical and low-cost implementation of Suite-B algorithms in different layers. Suite-B, promulgated by the National Security Agency, is a set of cryptographic algorithms, including non-repudiation. The end results include the deployment of Suite-B algorithms at the application, transport, and network layers and the protocol flow at each layer.<br /

Understanding New Emerging Technologies Through Hermeneutics. An Example from mHealth

New technologies such as mHealth have entered the health domain as an innovative technology to connect people suffering from a chronic disease with healthcare services to reduce the pressure on healthcare systems. The primary driver for these technologies is data and they contain valuable information. Understanding what the data means and the accuracy of the data can be complex. Hermeneutics has been applied in previous Information Systems studies that interpret data to provide a meaning about unexplored and complex phenomenon. This paper provides background information about Hermeneutics and an example of Hermeneutics applied in a new mHealth study

Bushfire disaster monitoring system using low power wide area networks (LPWAN)

Some applications, including disaster monitoring and recovery networks, use low-powerwide-area networks (LPWAN). LPWAN sensors capture data bits and transmit them to public carriernetworks (e.g., cellular networks) via dedicated gateways. One of the challenges encountered indisaster management scenarios revolves around the carry/forward sensed data and geographicallocation information dissemination to the disaster relief operatives (disaster relief agency; DRA) toidentify, characterise, and prioritise the affected areas. There are network topology options to reachits destination, including cellular, circuit switched, and peer-to-peer networks. In the context ofnatural disaster prediction, it is vital to access geographical location data as well as the timestamp.This paper proposes the usage of Pseudo A Number (PAN), that is, the calling party address, which isused by every network to include the location information instead of the actual calling party addressof the gateway in LPWAN. This PAN information can be further analysed by the DRA to identify theaffected areas and predict the complications of the disaster impacts in addition to the past historyof damages. This paper aims to propose a solution that can predict disaster proceedings basedon propagation and the velocity of impact using vector calculation of the location data and thetimestamp, which are transmitted by sensors through the PAN of the gateway in LPWAN

Using Machine Learning to address Data Accuracy and Information Integrity in Digital Health Delivery

Today, much of healthcare delivery is digital. In particular, there exists a plethora of mHealth solutions being developed. This in turn necessitates the need for accurate data and information integrity if superior mHealth is to ensue. Lack of data accuracy and information integrity can cause serious harm to patients and limit the benefits of mHealth technology. The described exploratory case study serves to investigate data accuracy and information integrity in mHealth, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information

Data accuracy considerations with mHealth

With the plethora of mHealth solutions developed being digital, this necessitates the need for accurate data and information integrity. Lack of data accuracy and information integrity in mHealth can cause serious harm to patients and limit the benefits of such promising technology. Thus, this exploratory study investigates data accuracy and information integrity in mHealth by examining a mobile health solution for diabetes, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information

Systematic predictive analysis of personalized life expectancy using smart devices

With the emergence of technologies such as electronic health and mobile health (eHealth/mHealth), cloud computing, big data, and the Internet of Things (IoT), health related data are increasing and many applications such as smartphone apps and wearable devices that provide wellness and fitness tracking are entering the market. Some apps provide health related data such as sleep monitoring, heart rate measuring, and calorie expenditure collected and processed by the devices and servers in the cloud. These requirements can be extended to provide a personalized life expectancy (PLE) for the purpose of wellbeing and encouraging lifestyle improvement. No existing works provide this PLE information that is developed and customized for the individual. This article is based on the concurrent models and methodologies to calculate and predict life expectancy (LE) and proposes an idea of using multi-phased approaches to the solution as the project requires an immense and broad range of work to accomplish. As a result, the current prediction of LE, which was found to be up to a maximum of five years could potentially be extended to a lifetime prediction by utilizing generic health data. In this article, the novel idea of the solution proposing a PLE on an individual basis, which can be extended to lifetime is presented in addition to the existing works

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

Time‐domain heart rate variability features for automatic congestive heart failure prediction

Aims: Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non-invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non-invasive biomarker to classify healthy adults and those with heart failure. Methods and results: We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre-processing, we performed time-domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50 ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five-fold cross-validation using four machine learning classification algorithms: support vector machine, k-nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5 min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92. Conclusions: The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non-invasive HRV, to screen congestive heart failure individuals in primary care