Air Force Institute of Technology Research Report 2009

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Parallel Model Counting with CUDA: Algorithm Engineering for Efficient Hardware Utilization

Propositional model counting (MC) and its extensions as well as applications in the area of probabilistic reasoning have received renewed attention in recent years. As a result, also the need for quickly solving counting-based problems with automated solvers is critical for certain areas. In this paper, we present experiments evaluating various techniques in order to improve the performance of parallel model counting on general purpose graphics processing units (GPGPUs). Thereby, we mainly consider engineering efficient algorithms for model counting on GPGPUs that utilize the treewidth of a propositional formula by means of dynamic programming. The combination of our techniques results in the solver GPUSAT3, which is based on the programming framework Cuda that -compared to other frameworks- shows superior extensibility and driver support. When combining all findings of this work, we show that GPUSAT3 not only solves more instances of the recent Model Counting Competition 2020 (MCC 2020) than existing GPGPU-based systems, but also solves those significantly faster. A portfolio with one of the best solvers of MCC 2020 and GPUSAT3 solves 19% more instances than the former alone in less than half of the runtime

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

An Autonomous Channel Selection Algorithm for WLANs

IEEE 802.11 wireless devices need to select a channel in order to transmit their packets. However, as a result of the contention-based nature of the IEEE 802.11 CSMA/CA MAC mechanism, the capacity experienced by a station is not fixed. When a station cannot win a sufficient number of transmission opportunities to satisfy its traffic load, it will become saturated. If the saturation condition persists, more and more packets are stored in the transmit queue and congestion occurs. Congestion leads to high packet delay and may ultimately result in catastrophic packet loss when the transmit queue’s capacity is exceeded. In this thesis, we propose an autonomous channel selection algorithm with neighbour forcing (NF) to minimize the incidence of congestion on all stations using the channels. All stations reassign the channels based on the local monitoring information. This station will change the channel once it finds a channel that has sufficient available bandwidth to satisfy its traffic load requirement or it will force its neighbour stations into saturation by reducing its PHY transmission rate if there exists at least one successful channel assignment according to a predicting module which checks all the possible channel assignments. The results from a simple C++ simulator show that the NF algorithm has a higher probability than the dynamic channel assignment without neighbour forcing (NONF) to successfully reassign the channel once stations have become congested. In an experimental testbed, the Madwifi open source wireless driver has been modified to incorporate the channel selection mechanism. The results demonstrate that the NF algorithm also has a better performance than the NONF algorithm in reducing the congestion time of the network where at least one station has become congested

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

Networking and application interface technology for wireless sensor network surveillance and monitoring

Distributed unattended ground sensor (UGS) networks are commonly deployed to support wide area battlefield surveillance and monitoring missions. The information they generate has proven to be valuable in providing a necessary tactical information advantage for command and control, intelligence and reconnaissance field planning. Until recently, however, there has been greater emphasis within the defence research community for UGS networks to fulfil their mission objectives successfully, with minimal user interaction. For a distributed UGS scenario, this implies a network centric capability, where deployed UGS networks can self-manage their behaviour in response to dynamic environmental changes. In this thesis, we consider both the application interface and networking technologies required to achieve a network centric capability, within a distributed UGS surveillance setting. Three main areas of work are addressed towards achieving this. The first area of work focuses on a capability to support autonomous UGS network management for distributed surveillance operations. The network management aspect is framed in terms of how distributed sensors can collaborate to achieve their common mission objectives and at the same time, conserve their limited network resources. A situation awareness methodology is used, in order to enable sensors which have similar understanding towards a common objective to be utilised, for collaboration and to allow sensor resources to be managed as a direct relationship according to, the dynamics of a monitored threat. The second area of work focuses on the use of geographic routing to support distributed surveillance operations. Here we envisage the joint operation of unmanned air vehicles and UGS networks, working together to verify airborne threat observations. Aerial observations made in this way are typically restricted to a specific identified geographic area. Information queries sent to inquire about these observations can also be routed and restricted to using this geographic information. In this section, we present our bio-inspired geographic routing strategy, with an integrated topology control function to facilitate this. The third area of work focuses on channel aware packet forwarding. Distributed UGS networks typically operate in wireless environments, which can be unreliable for packet forwarding purposes. In this section, we develop a capability for UGS nodes to decide which packet forwarding links are reliable, in order to reduce packet transmission failures and improve overall distributed networking performance