Testability of a swarm robot using a system of systems approach and discrete event simulation

A simulation framework using discrete event system specification (DEVS) and data encoded with Extensible Markup Language (XML) is presented to support agent-in-the-loop (AIL) simulations for large, complex, and distributed systems. A System of Systems (SoS) approach organizes the complex systems hierarchically. AIL simulations provide a necessary step in maintaining model continuity methods to achieve a greater degree of accuracy in systems analysis. The proposed SoS approach enables the simulation and analysis of these independent and cooperative systems by concentrating on the data transferred among systems to achieve interoperability instead of requiring the software modeling of global state spaces. The information exchanged is wrapped in XML to facilitate system integration and interoperability. A Groundscout is deployed as a real agent working cooperatively with virtual agents to form a robotic swarm in an example threat detection scenario. This scenario demonstrates the AIL framework\u27s ability to successfully test a swarm robot for individual performance and swarm behavior. Results of the testing process show an increase of robot team size increases the rate of successfully investigating a threat while critical violations of the algorithm remained low despite packet loss

Implementation, integration, and optimization of a fuzzy foreground segmentation system

Foreground segmentation is often an important preliminary step for various video processing systems. By improving the accuracy of the foreground segmentation process, the overall performance of a video processing system has the potential for improvement. This work introduces a Fuzzy Foreground Segmentation System (FFSS) that uses Mamdani-type Fuzzy Inference Systems (FIS) to control pixel-level accumulated statistics. The error of the FFSS is quantified by comparing its output with hand-segmented ground-truth images from a set of image sequences that specifically model canonical problems of foreground segmentation. Optimization of the FFSS parameters is achieved using a Real-Coded Genetic Algorithm (RCGA). Additionally, multiple central composite designed experiments used to analyze the performance of RCGA under selected schemes and their respective parameters. The RCGA schemes and parameters are chosen as to reduce variation and execution time for a set of known multi-dimensional test functions. The selected multi-dimensional test functions represent assorted function landscapes. To demonstrate accuracy of the FFSS and implicate the importance of the foreground segmentation process, the system is applied to real-time human detection from a single-camera security system. The Human Detection System (HDS) is composed of an IP Camera networked to multiple heterogeneous computers for distributed parallel processing. The implementation of the HDS, adheres to a System of Systems (SoS) architecture which standardizes data/communication, reduces overall complexity, and maintains a high level of interoperability

An Interactive Distributed Simulation Framework With Application To Wireless Networks And Intrusion Detection

In this dissertation, we describe the portable, open-source distributed simulation framework (WINDS) targeting simulations of wireless network infrastructures that we have developed. We present the simulation framework which uses modular architecture and apply the framework to studies of mobility pattern effects, routing and intrusion detection mechanisms in simulations of large-scale wireless ad hoc, infrastructure, and totally mobile networks. The distributed simulations within the framework execute seamlessly and transparently to the user on a symmetric multiprocessor cluster computer or a network of computers with no modifications to the code or user objects. A visual graphical interface precisely depicts simulation object states and interactions throughout the simulation execution, giving the user full control over the simulation in real time. The network configuration is detected by the framework, and communication latency is taken into consideration when dynamically adjusting the simulation clock, allowing the simulation to run on a heterogeneous computing system. The simulation framework is easily extensible to multi-cluster systems and computing grids. An entire simulation system can be constructed in a short time, utilizing user-created and supplied simulation components, including mobile nodes, base stations, routing algorithms, traffic patterns and other objects. These objects are automatically compiled and loaded by the simulation system, and are available for dynamic simulation injection at runtime. Using our distributed simulation framework, we have studied modern intrusion detection systems (IDS) and assessed applicability of existing intrusion detection techniques to wireless networks. We have developed a mobile agent-based IDS targeting mobile wireless networks, and introduced load-balancing optimizations aimed at limited-resource systems to improve intrusion detection performance. Packet-based monitoring agents of our IDS employ a CASE-based reasoner engine that performs fast lookups of network packets in the existing SNORT-based intrusion rule-set. Experiments were performed using the intrusion data from MIT Lincoln Laboratories studies, and executed on a cluster computer utilizing our distributed simulation system

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

A Service Oriented Architecture Approach for Global Positioning System Quality of Service Monitoring

This research focuses on the development of a Service Oriented Architecture (SOA) for monitoring the Global Positioning System (GPS) Standard Positioning Service (SPS) in near real time utilizing a Mobile Crowd Sensing (MCS) technique. A unique approach to developing the MCS SOA was developed that utilized both the Depart- ment of Defense Architecture Framework (DoDAF) and the SOA Modeling Language (SoaML) guidance. The combination of these two frameworks resulted in generation of all the architecture products required to evaluate the SOA through the use of Model Based System Engineering (MBSE) techniques. Ultimately this research provides a feasibility analysis for utilization of mobile distributed sensors to provide situational awareness of the GPS Quality of Service (QoS). First this research provides justification for development of a new monitoring architecture and defines the scope of the SOA. Then an exploration of current SOA, MBSE, and Geospatial System Information (GIS) research was conducted. Next a Discrete Event Simulation (DES) of the MCS participant interactions was developed and simulated within AGI\u27s Systems Toolkit. The architecture performance analysis was executed using a GIS software package known as ArcMap. Finally, this research concludes with a suitability analysis of the proposed architecture for detecting sources of GPS interference within an Area of Interest (AoI)

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Distributed Virtual Environment for Radar Testing

This project entailed the design and prototype implementation of a distributed radar simulator for MIT Lincoln Laboratory. This simulator is designed to test radar control software developed at the Laboratory by mimicking radar hardware and simulating radar returns from targets in a virtual environment. The team worked closely with Lincoln Laboratory staff to ensure that the simulator design would be extensible to support different types of radar systems and scalable to thousands of targets. Finally, a distributed simulator was implemented in order to validate the project design

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques

This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event. Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers. Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system