Prediction techniques based on data are applied in a broad range of applications such as bioinformatics, disease spread, and mobile intrusion detection, just to name a few. With the rapid emergence of on-line technologies numerous techniques for collecting and storing data for prediction-based analysis have been proposed in the literature. With the growing size of global population, the spread of epidemics is increasing at an alarming rate. Consequently, public and private health care officials are in a dire need of developing technological solutions for managing epidemics. Most of the existing syndromic surveillance and disease detection systems deal with a small portion of a real dataset. From the communication network perspective, the results reported in the literature generally deal with commonly known network topologies. Scalability of a disease detection system is a real challenge when it comes to modeling and predicting disease spread across a large population or large scale networks. In this dissertation, we address this challenge by proposing a hierarchical aggregation approach that classifies a dynamic disease spread phenomena at different scalability levels. Specifically, we present a finite state model (SEIR-FSM) for predicting disease spread, the model manifests itself into three different levels of data aggregation and accordingly makes prediction of disease spread at various scales. We present experimental results of this model for different disease spread behaviors on all levels of granularity. Subsequently, we present a mechanism for mapping the population interaction network model to a wireless mobile network topology. The objective is to analyze the phenomena of malware spread based on vulnerabilities. The goal is to develop and evaluate a wireless mobile intrusion detection system that uses a Hidden Markov model in connection with the FSM disease spread model (HMM-FSM). Subsequently, we propose a software-based architecture that acts as a network function virtualization (NFV) to combat malware spread in IoT based networks. Taking advantage of the NFV infrastructure's potential to provide new security solutions for IoT environments to combat malware attacks. We propose a scalable and generalized IDS that uses a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) learning model for predicting malware attacks in a timely manner for the NFV to deploy the appropriate countermeasures. The analysis utilizes the susceptible (S), exposed (E), infected (I), and resistant (R) (SEIR) model to capture the dynamics of the spread of the malware attack and subsequently provide a patching mechanism for the network. Our analysis focuses primarily on the feasibility and the performance evaluation of the NFV RNN-LSTM proposed model.

NR 20140805

No description available.

Current authentication methods for wireless networks are difficult to maintain. They often rely on globally shared secrets or heavyweight public-key infrastructure. Wireless Authentication using Remote Passwords (WARP) mitigates authentication woes by providing usable mechanisms for both administrators and end-users. Administrators grant access by simply adding users' personal messaging identifiers (e.g., email addresses, IM handles, cell phone numbers) to an access control list. There is no need to store passwords or other account information. Users simply prove ownership of their authorized identifier to obtain wireless access.

A modern power system is composed of many individual entities collaborating with each other to operate the entire system in a secure and economic manner. These entities may have different owners and operators with their own operating rules and policies, and it complicates the decision-making process in the system. In this work, a system of systems (SoS) engineering framework is presented for optimally operating the modern power systems. The proposed SoS framework defines each entity as an independent system with its own regulations, and the communication and process of information exchange between the systems are discussed. Since the independent systems are working in an interconnected system, the operating condition of one may impact the operating condition of others. According to the independent systems’ characteristics and connection between them, an optimization problem is formulated for each independent system. In order to solve the optimization problem of each system and to optimally operate the entire SoS-based power system, a decentralized decision-making algorithm is developed. Using this algorithm, only a limited amount of information is exchanged among different systems, and the operators of independent systems do not need to exchange all the information, which may be commercially sensitive, with each other. In addition, applying chance-constrained stochastic programming, the impact of uncertain variables, such as renewable generation and load demands, is modeled in the proposed SoS-based decision-making algorithm. The proposed SoS-based decision-making algorithm is applied to find the optimal and secure operating point of an active distribution grid (ADG). This SoS framework models the distribution company (DISCO) and microgrids (MGs) as independent systems having the right to work based on their own operating rules and policies, and it coordinates the DISCO and MGs operating condition. The proposed decision-making algorithm is also performed to solve the security-constrained unit commitment incorporating distributed generations (DGs) located in ADGs. The independent system operator (ISO) and DISCO are modeled as self-governing systems, and competition and collaboration between them are explained according to the SoS framework.

Many successful control schemes for land-based or air-based groups, or platoons, of autonomous vehicles cannot be implemented in underwater applications because of their dependence upon high-bandwidth communication. In current strategies for controlling groups of autonomous underwater vehicles (AUVs), platoon size remains limited by communication bandwidth requirements. So, there is great need for advances in low-bandwidth control techniques for arbitrarily large platoons of AUVs. This thesis presents a new approach to multiple vehicle control. The concepts described herein enable an arbitrarily large platoon to be controlled while utilizing minimal inter-vehicle communication. Specifically, this thesis examines a sufficient condition on platoon commands in order for a low-bandwidth decentralized controller to exist. Knowing from this sufficient condition the necessary general form of platoon commands, a number of higher-order statistics were tested. This thesis describes and analyzes their utility as platoon commands. In addition to these theoretical developments, this thesis presents the practical design needs for the Virginia Tech miniature autonomous underwater vehicle as well as their resolution. / Master of Science

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms.

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms.

<p dir="ltr">Decentralized learning is the key to training deep neural networks (DNNs) over large distributed datasets generated at different devices and locations, without the need for a central server. They enable next-generation applications that require DNNs to interact and learn from their environment continuously. The practical implementation of decentralized algorithms brings about its unique set of challenges. In particular, these algorithms should be (a) compatible with time-varying graph structures, (b) compute and communication efficient, and (c) resilient to heterogeneous data distributions. The objective of this thesis is to enable efficient decentralized learning in deep neural networks addressing the abovementioned challenges. Towards this, firstly a communication-efficient decentralized algorithm (Sparse-Push) that supports directed and time-varying graphs with error-compensated communication compression is proposed. Second, a low-precision decentralized training that aims to reduce memory requirements and computational complexity is proposed. Here, we design ”Range-EvoNorm” as the normalization activation layer which is better suited for low-precision decentralized training. Finally, addressing the problem of data heterogeneity, three impactful advancements namely Neighborhood Gradient Mean (NGM), Global Update Tracking (GUT), and Cross-feature Contrastive Loss (CCL) are proposed. NGM utilizes extra communication rounds to obtain cross-agent gradient information whereas GUT tracks global update information with no communication overhead, improving the performance on heterogeneous data. CCL explores an orthogonal direction of using a data-free knowledge distillation approach to handle heterogeneous data in decentralized setups. All the algorithms are evaluated on computer vision tasks using standard image-classification datasets. We conclude this dissertation by presenting a summary of the proposed decentralized methods and their trade-offs for heterogeneous data distributions. Overall, the methods proposed in this thesis address the critical limitations of training deep neural networks in a decentralized setup and advance the state-of-the-art in this domain.</p>

When the Internet was created it did not include any protocol for identifying the person behind the computer. Instead, the act of identification has primarily been established by trusting a third party. But, the rise of Distributed Ledger Technology has made it possible to authenticate a digital identity and build trust without the need of a third party. The Swedish Maritime Administration are currently validating a new maritime digital infrastructure for the maritime transportation industry. The goal is to reduce the number of accidents, fuel consumption and voyage costs. Involved actors has their identity stored in a central registry that relies on the trust of a third party. This thesis investigates how a conversion from the centralized identity registry to a decentralized identity registry affects the usability and the risk for compromised data integrity. This is done by implementing a Proof of Concept of a decentralized identity registry that replaces the current centralized registry, and comparing them. The decentralized Proof of Concept’s risk for compromised data integrity is 95.1% less compared with the centralized registry, but this comes with a loss of 53% in efficiency.