111 research outputs found
An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks
QoS identification for untrustworthy Web services is critical in QoS
management in the service computing since the performance of untrustworthy Web
services may result in QoS downgrade. The key issue is to intelligently learn
the characteristics of trustworthy Web services from different QoS levels, then
to identify the untrustworthy ones according to the characteristics of QoS
metrics. As one of the intelligent identification approaches, deep neural
network has emerged as a powerful technique in recent years. In this paper, we
propose a novel two-phase neural network model to identify the untrustworthy
Web services. In the first phase, Web services are collected from the published
QoS dataset. Then, we design a feedforward neural network model to build the
classifier for Web services with different QoS levels. In the second phase, we
employ a probabilistic neural network (PNN) model to identify the untrustworthy
Web services from each classification. The experimental results show the
proposed approach has 90.5% identification ratio far higher than other
competing approaches.Comment: 8 pages, 5 figure
Managing trust and reliability for indoor tracking systems
Indiana University-Purdue University Indianapolis (IUPUI)Indoor tracking is a challenging problem. The level of accepted error is on a much
smaller scale than that of its outdoor counterpart. While the global positioning system has
become omnipresent, and a widely accepted outdoor tracking system it has limitations in
indoor environments due to loss or degradation of signal. Many attempts have been made
to address this challenge, but currently none have proven to be the de-facto standard. In
this thesis, we introduce the concept of opportunistic tracking in which tracking takes
place with whatever sensing infrastructure is present – static or mobile, within a given
indoor environment. In this approach many of the challenges (e.g., high cost, infeasible
infrastructure deployment, etc.) that prohibit usage of existing systems in typical
application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges
do still exist when it comes to provide an accurate positional estimate of an entities
location in an indoor environment, namely: sensor classification, sensor selection, and
multi-sensor data fusion. We propose an enhanced tracking framework that through the
infusion of QoS-based selection criteria of trust and reliability we can improve the overall
accuracy of the tracking estimate. This improvement is predicated on the introduction of
learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly
identified and evaluated based upon their specific behavioral characteristics through
performance evaluation. This in-depth evaluation of sensors provides the basis for
improving the sensor selection process. A side effect of obtaining this improved accuracy
is the cost, found in the form of system runtime. This thesis provides a solution for this
tradeoff between accuracy and cost through an optimization function that analyzes this
tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an
object as it moves indoors. We demonstrate that through this improved sensor
classification, selection, data fusion, and tradeoff optimization we can provide an
improvement, in terms of accuracy, over other existing indoor tracking systems
Trust aware system for social networks: A comprehensive survey
Social networks are the platform for the users to get connected with other social network users based on their interest and life styles. Existing social networks have millions of users and the data generated by them are huge and it is difficult to differentiate the real users and the fake users. Hence a trust worthy system is recommended for differentiating the real and fake users. Social networking enables users to send friend requests, upload photos and tag their friends and even suggest them the web links based on the interest of the users. The friends recommended, the photos tagged and web links suggested may be a malware or an untrusted activity. Users on social networks are authorised by providing the personal data. This personal raw data is available to all other users online and there is no protection or methods to secure this data from unknown users. Hence to provide a trustworthy system and to enable real users activities a review on different methods to achieve trustworthy social networking systems are examined in this paper
A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications
Rapid popularity of Internet of Things (IoT) and cloud computing permits
neuroscientists to collect multilevel and multichannel brain data to better
understand brain functions, diagnose diseases, and devise treatments. To ensure
secure and reliable data communication between end-to-end (E2E) devices
supported by current IoT and cloud infrastructure, trust management is needed
at the IoT and user ends. This paper introduces a Neuro-Fuzzy based
Brain-inspired trust management model (TMM) to secure IoT devices and relay
nodes, and to ensure data reliability. The proposed TMM utilizes node
behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference
System and weighted-additive methods respectively to assess the nodes
trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2
simulation results confirm the robustness and accuracy of the proposed TMM in
identifying malicious nodes in the communication network. With the growing
usage of cloud based IoT frameworks in Neuroscience research, integrating the
proposed TMM into the existing infrastructure will assure secure and reliable
data communication among the E2E devices.Comment: 17 pages, 10 figures, 2 table
IoT trust and reputation: a survey and taxonomy
IoT is one of the fastest-growing technologies and it is estimated that more
than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation
among IoT entities is essential. Several trust management models have been
proposed in the IoT environment; however, these schemes have not fully
addressed the IoT devices features, such as devices role, device type and its
dynamic behavior in a smart environment. As a result, traditional trust and
reputation models are insufficient to tackle these characteristics and
uncertainty risks while connecting nodes to the network. Whilst continuous
study has been carried out and various articles suggest promising solutions in
constrained environments, research on trust and reputation is still at its
infancy. In this paper, we carry out a comprehensive literature review on
state-of-the-art research on the trust and reputation of IoT devices and
systems. Specifically, we first propose a new structure, namely a new taxonomy,
to organize the trust and reputation models based on the ways trust is managed.
The proposed taxonomy comprises of traditional trust management-based systems
and artificial intelligence-based systems, and combine both the classes which
encourage the existing schemes to adapt these emerging concepts. This
collaboration between the conventional mathematical and the advanced ML models
result in design schemes that are more robust and efficient. Then we drill down
to compare and analyse the methods and applications of these systems based on
community-accepted performance metrics, e.g. scalability, delay,
cooperativeness and efficiency. Finally, built upon the findings of the
analysis, we identify and discuss open research issues and challenges, and
further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin
IoT trust and reputation: a survey and taxonomy
IoT is one of the fastest-growing technologies and it is estimated that more
than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation
among IoT entities is essential. Several trust management models have been
proposed in the IoT environment; however, these schemes have not fully
addressed the IoT devices features, such as devices role, device type and its
dynamic behavior in a smart environment. As a result, traditional trust and
reputation models are insufficient to tackle these characteristics and
uncertainty risks while connecting nodes to the network. Whilst continuous
study has been carried out and various articles suggest promising solutions in
constrained environments, research on trust and reputation is still at its
infancy. In this paper, we carry out a comprehensive literature review on
state-of-the-art research on the trust and reputation of IoT devices and
systems. Specifically, we first propose a new structure, namely a new taxonomy,
to organize the trust and reputation models based on the ways trust is managed.
The proposed taxonomy comprises of traditional trust management-based systems
and artificial intelligence-based systems, and combine both the classes which
encourage the existing schemes to adapt these emerging concepts. This
collaboration between the conventional mathematical and the advanced ML models
result in design schemes that are more robust and efficient. Then we drill down
to compare and analyse the methods and applications of these systems based on
community-accepted performance metrics, e.g. scalability, delay,
cooperativeness and efficiency. Finally, built upon the findings of the
analysis, we identify and discuss open research issues and challenges, and
further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin
Trust Evaluation in the IoT Environment
Along with the many benefits of IoT, its heterogeneity brings a new challenge to establish a trustworthy environment among the objects due to the absence of proper enforcement mechanisms. Further, it can be observed that often these encounters are addressed only concerning the security and privacy matters involved. However, such common network security measures are not adequate to preserve the integrity of information and services exchanged over the internet. Hence, they remain vulnerable to threats ranging from the risks of data management at the cyber-physical layers, to the potential discrimination at the social layer. Therefore, trust in IoT can be considered as a key property to enforce trust among objects to guarantee trustworthy services. Typically, trust revolves around assurance and confidence that people, data, entities, information, or processes will function or behave in expected ways. However, trust enforcement in an artificial society like IoT is far more difficult, as the things do not have an inherited judgmental ability to assess risks and other influencing factors to evaluate trust as humans do. Hence, it is important to quantify the perception of trust such that it can be understood by the artificial agents. In computer science, trust is considered as a computational value depicted by a relationship between trustor and trustee, described in a specific context, measured by trust metrics, and evaluated by a mechanism. Several mechanisms about trust evaluation can be found in the literature. Among them, most of the work has deviated towards security and privacy issues instead of considering the universal meaning of trust and its dynamic nature. Furthermore, they lack a proper trust evaluation model and management platform that addresses all aspects of trust establishment. Hence, it is almost impossible to bring all these solutions to one place and develop a common platform that resolves end-to-end trust issues in a digital environment. Therefore, this thesis takes an attempt to fill these spaces through the following research work. First, this work proposes concrete definitions to formally identify trust as a computational concept and its characteristics. Next, a well-defined trust evaluation model is proposed to identify, evaluate and create trust relationships among objects for calculating trust. Then a trust management platform is presented identifying the major tasks of trust enforcement process including trust data collection, trust data management, trust information analysis, dissemination of trust information and trust information lifecycle management. Next, the thesis proposes several approaches to assess trust attributes and thereby the trust metrics of the above model for trust evaluation. Further, to minimize dependencies with human interactions in evaluating trust, an adaptive trust evaluation model is presented based on the machine learning techniques. From a standardization point of view, the scope of the current standards on network security and cybersecurity needs to be expanded to take trust issues into consideration. Hence, this thesis has provided several inputs towards standardization on trust, including a computational definition of trust, a trust evaluation model targeting both object and data trust, and platform to manage the trust evaluation process
Surface EMG decomposition using a novel approach for blind source separation
We introduce a new method to perform a blind deconvolution of the surface electromyogram (EMG) signals generated by isometric muscle contractions. The method extracts the information from the raw EMG signals detected only on the skin surface, enabling longtime noninvasive monitoring of the electromuscular properties. Its preliminary results show that surface EMG signals can be used to determine the number of active motor units, the motor unit firing rate and the shape of the average action potential in each motor unit
Noninvasive methods for children\u27s cholesterol level determination
Today, there is a controversy about the role of cholesterol in infants and the measurement and management of blood cholesterol in children. Several scientific evidences are supporting relationship between elevated blood cholesterol in children and high cholesterol in adults and development of adult arteriosclerotic diseases such as cardiovascular and cerebrovascular disease. Therefore controlling the level of blood cholesterol in children is very important for the health of the whole population. Non-invasive methods are much more convenient for the children because of their anxieties about blood examinations. In this paper we will present a new try to find non-invasive methods for determining the level of blood cholesterol in children with the use of intelligent system
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