407 research outputs found
An empirical evaluation of m-health service usersā behaviours: A case of Bangladesh
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Mobile health (m-health) services are revolutionising healthcare in the developing world by improving accessibility, affordability, and availability. Although these services are revolutionising healthcare in various ways, there are growing concerns regarding users' service quality perceptions and overall influence on satisfaction and usage behaviours. In developing countries, access to healthcare and low healthcare costs are insufficient if users lack confidence in healthcare service quality.
Bangladesh's Directorate General of Health Services (DGHS) provides the only government-sponsored m-health service available to the entire population. DGHS's m-health service, available since 2009, is yet to be evaluated in terms of users' perceptions of the quality of service and its impact on satisfaction and usage. Hence, this study developed a conceptual model for evaluating the associations between overall DGHS m-health service quality, satisfaction, and usage behaviours. This study operationalised overall m-health service quality as a higher-order construct with three dimensions- platform quality, information quality, and outcome quality, and nine corresponding subdimensions-privacy, systems availability, systems reliability, systems efficiency, responsiveness, empathy, assurance, emotional benefit, and functional benefit.
Moreover, researchers in various service domains, including- healthcare, marketing, environmental protection, and information systems, evaluated and confirmed the influence of social and personal norms on satisfaction and behavioural outcomes like- intention to use. Despite this, no research has been conducted to determine whether these normative components affect m-health users' service satisfaction and usage behaviours. As a result, this study included social and personal norms along with overall service quality into the conceptual model to assess the influence of these variables on users' satisfaction and m-health service usage behaviours. Data was collected from two districts in Bangladesh- Dhaka and Rajshahi, utilising the online survey approach. A total of 417 usable questionnaires were analysed using partial least squares structural equation modelling to investigate the relationships between the constructs in Warp PLS.
The study confirms that all three dimensions of service quality and their corresponding subdimensions influence users' overall perceptions of DGHS m-health service quality. Moreover, overall DGHS m-health service quality has a significant direct association with satisfaction and an indirect association with usage behaviours through satisfaction. While social norms do not influence satisfaction and usage behaviours within the DGHS m-health context, personal norms directly influence users' satisfaction and indirectly influence usage behaviours through satisfaction. Theoretically, the study contributes by framing the influence of users' overall m-health service quality perceptions, social and personal norms on their actual usage behaviours rather than the intention to use. It also extends the existing knowledge by assessing and comparing m-health users' continuous and discontinuous behaviours. Methodologically this study confirms the usefulness of partial least squares structural equational modelling to analyse a complex model including a higher order construct (i.e., overall perceived service quality). Practically, the study demonstrates the importance of users' satisfaction in addition to service quality, as service quality only affects usage behaviours through satisfaction in the current study context. Additionally, knowing that personal norms significantly influence service satisfaction motivates providers of m-health services to strive to enhance users' personal norms toward m-health service to enhance service satisfaction and usage. Overall, the study will help enhance patient outcomes and m-health service usage
Security and Privacy for Modern Wireless Communication Systems
The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in nodeāedgeācloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions
The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.Ā©2023 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed
Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II
The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above
Visual Privacy Mitigation Strategies in Social Media Networks and Smart Environments
The contemporary use of technologies and environments has led to a vast collection and sharing of visual data, such as images and videos. However, the increasing popularity and advancements in social media platforms and smart environments have posed a significant challenge in protecting the privacy of individualsā visual data, necessitating a better understanding of the visual privacy implications in these environments. These concerns can arise intentionally or unintentionally from the individual, other entities in the environment, or a company. To address these challenges, it is necessary to inform the design of the data collection process and deployment of the system by understanding the visual privacy implications of these environments. However, ensuring visual privacy in social media networks and smart environments presents significant research challenges. These challenges include accounting for an individualās subjectivity towards visual privacy, the influence of visual privacy leakage in the environment, and the environmentās infrastructure design and ownership. This dissertation employs a range of methodologies, including user studies, machine learning, and statistics to explore social media networks and smart environments and their visual privacy risks. Qualitative and quantitative studies were conducted to understand privacy perspectives in social media networks and smart city environments.
The findings reveal that individuals and stakeholders possess inherited bias and subjectivity when considering privacy in these environments, leading to a need for visual privacy mitigation and risk analysis. Furthermore, a new visual privacy risk score using visual features and computer vision is developed to investigate and discover visual privacy leakage. However, using computer vision methods for visual privacy mitigation introduces additional privacy and fairness risks while developing and deploying visual privacy systems and machine learning algorithms. This necessitates the creation of interactive audit strategies to consider the broader impacts of research on the community. Overall, this dissertation contributes to advancing visual privacy solutions in social media networks and smart environments by investigating xiii and quantifying the visual privacy concerns and perspectives of individuals and stakeholders, advocating for the need for responsible visual privacy mitigation methods in these environments. It also strengthens the ability of researchers, stakeholders, and companies to protect individuals from visual privacy risks throughout the machine learning pipeline
Optimising WLANs Power Saving: Context-Aware Listen Interval
Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, a number of power saving approaches have been devised including Static Power Save Mode (SPSM), Adaptive PSM (APSM), and Smart Adaptive PSM (SAPSM). However, the existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements.
This thesis proposes a novel Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. We focused on the network activity of a single smartphone application while ignoring the network activity of applications running simultaneously.
We introduced a context-aware network traffic classification approach based on ML classifiers to classify the network traffic of wireless devices in WLANs. Smartphone applicationsā network traffic reflecting a diverse array of network behaviour and interactions were used as contextual inputs for training ML classifiers of output traffic, constructing an ML classification model. A real-world dataset is constructed, based on nine smartphone applicationsā network traffic, this is used firstly to evaluate the performance of five ML classifiers using cross-validation, followed by conducting extensive experimentation to assess the generalisation capacity of the selected classifiers on unseen testing data. The experimental results further validated the practical application of the selected ML classifiers and indicated that ML classifiers can be usefully employed for classifying the network traffic of smartphone applications based on different levels of behaviour and interaction.
Furthermore, to optimise the sleep and awake cycles of the WNIC in accordance with the smartphone applicationsā network activity. Four CALI power saving modes were developed based on the classified output traffic. Hence, the ML classification model classifies the new unseen samples into one of the classes, and the WNIC will be adjusted to operate into one of CALI power saving modes. In addition, the performance of CALIās power saving modes were evaluated by comparing the levels of energy consumption with existing benchmark power saving approaches using three varied sets of energy parameters. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to SAPSM power saving approach, which also employs an ML classifier
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Scalable Schedule-Aware Bundle Routing
This thesis introduces approaches providing scalable delay-/disruption-tolerant routing capabilities in scheduled space topologies. The solution is developed for the requirements derived from use cases built according to predictions for future space topology, like the future Mars communications architecture report from the interagency operations advisory group. A novel routing algorithm is depicted to provide optimized networking performance that discards the scalability issues inherent to state-of-the-art approaches. This thesis also proposes a new recommendation to render volume management concerns generic and easily exchangeable, including a new simple management technique increasing volume awareness accuracy while being adaptable to more particular use cases. Additionally, this thesis introduces a more robust and scalable approach for internetworking between subnetworks to increase the throughput, reduce delays, and ease configuration thanks to its high flexibility.:1 Introduction
1.1 Motivation
1.2 Problem statement
1.3 Objectives
1.4 Outline
2 Requirements
2.1 Use cases
2.2 Requirements
2.2.1 Requirement analysis
2.2.2 Requirements relative to the routing algorithm
2.2.3 Requirements relative to the volume management
2.2.4 Requirements relative to interregional routing
3 Fundamentals
3.1 Delay-/disruption-tolerant networking
3.1.1 Architecture
3.1.2 Opportunistic and deterministic DTNs
3.1.3 DTN routing
3.1.4 Contact plans
3.1.5 Volume management
3.1.6 Regions
3.2 Contact graph routing
3.2.1 A non-replication routing scheme
3.2.2 Route construction
3.2.3 Route selection
3.2.4 Enhancements and main features
3.3 Graph theory and DTN routing
3.3.1 Mapping with DTN objects
3.3.2 Shortest path algorithm
3.3.3 Edge and vertex contraction
3.4 Algorithmic determinism and predictability
4 Preliminary analysis
4.1 Node and contact graphs
4.2 Scenario
4.3 Route construction in ION-CGR
4.4 Alternative route search
4.4.1 Yenās algorithm scalability
4.4.2 Blocking issues with Yen
4.4.3 Limiting contact approaches
4.5 CGR-multicast and shortest-path tree search
4.6 Volume management
4.6.1 Volume obstruction
4.6.2 Contact sink
4.6.3 Ghost queue
4.6.4 Data rate variations
4.7 Hierarchical interregional routing
4.8 Other potential issues
5 State-of-the-art and related work
5.1 Taxonomy
5.2 Opportunistic and probabilistic approaches
5.2.1 Flooding approaches
5.2.2 PROPHET
5.2.3 MaxProp
5.2.4 Issues
5.3 Deterministic approaches
5.3.1 Movement-aware routing over interplanetary networks
5.3.2 Delay-tolerant link state routing
5.3.3 DTN routing for quasi-deterministic networks
5.3.4 Issues
5.4 CGR variants and enhancements
5.4.1 CGR alternative routing table computation
5.4.2 CGR-multicast
5.4.3 CGR extensions
5.4.4 RUCoP and CGR-hop
5.4.5 Issues
5.5 Interregional routing
5.5.1 Border gateway protocol
5.5.2 Hierarchical interregional routing
5.5.3 Issues
5.6 Further approaches
5.6.1 Machine learning approaches
5.6.2 Tropical geometry
6 Scalable schedule-aware bundle routing
6.1 Overview
6.2 Shortest-path tree routing for space networks
6.2.1 Structure
6.2.2 Tree construction
6.2.3 Tree management
6.2.4 Tree caching
6.3 Contact segmentation
6.3.1 Volume management interface
6.3.2 Simple volume manager
6.3.3 Enhanced volume manager
6.4 Contact passageways
6.4.1 Regional border deļ¬nition
6.4.2 Virtual nodes
6.4.3 Pathļ¬nding and administration
7 Evaluation
7.1 Methodology
7.1.1 Simulation tools
7.1.2 Simulator extensions
7.1.3 Algorithms and scenarios
7.2 Oļ¬ine analysis
7.3 Eliminatory processing pressures
7.4 Networking performance
7.4.1 Intraregional unicast routing tests
7.4.2 Intraregional multicast tests
7.4.3 Interregional routing tests
7.4.4 Behavior with congestion
7.5 Requirement fulļ¬llment
8 Summary and Outlook
8.1 Conclusion
8.2 Future works
8.2.1 Next development steps
8.2.2 Contact graph routin
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