Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Distributed Simulation: State-of-the-Art and Potential for Operational Research

In Operational Research conventional simulation practices typically focus on the conceptualization, development and use of a single model simulated on a single computer by a single analyst. Since the late 1970s the field of Distributed Simulation has led research into how to speed up simulation and how to compose large-scale simulations consisting of many reusable models running using distributed computers. There have been significant advances in the theories and technologies underpinning Distributed Simulation and there have been major successes in defence, computer systems design and smart urban environments. However, from an Operational Research perspective, Distributed Simulation has had little impact on mainstream research and practice. To argue the potential benefits of Distributed Simulation for Operational Research, this article gives an overview of Distributed Simulation approaches and technologies as well as discussing the state-of-the-art of Distributed Simulation applications. It will investigate the potential advantages of Distributed Simulation for Operational Research and present a possible sustainable future, based on experiences from e-Science, that will help Operational Research meet future challenges such as those emerging from Big Data Analytics, Cyber-physical systems, Industry 4.0, Digital Twins and Smart environments

Enabling Accurate Cross-Layer PHY/MAC/NET Simulation Studies of Vehicular Communication Networks

Vehicle-to-vehicle and vehicle-to-roadside communications is required for numerous applications that aim at improving traffic safety and efficiency. In this setting, however, gauging system performance through field trials can be very expensive especially when the number of studied vehicles is high. Therefore, many existing studies have been conducted using either network or physical layer simulators; both approaches are problematic. Network simulators typically abstract physical layer details (coding, modulation, radio channels, receiver algorithms, etc.) while physical layer ones do not consider overall network characteristics (topology, network traffic types, and so on). In particular, network simulators view a transmitted frame as an indivisible unit, which leads to several limitations. First, the impact of the vehicular radio channel is typically not reflected in its appropriate context. Further, interference due to frame collisions is not modeled accurately ( if at all) and, finally, the benefits of advanced signal processing techniques, such as interference cancellation, are difficult to assess. To overcome these shortcomings we have integrated a detailed physical layer simulator into the popular NS-3 network simulator. This approach aims to bridge the gap between the physical and network layer perspectives, allow for more accurate channel and physical layer models, and enable studies on cross-layer optimization. In this paper, we exemplify our approach by integrating an IEEE 802.11a and p physical layer simulator with NS-3. Further, we validate the augmented NS-3 simulator against an actual IEEE 802.11 wireless testbed and illustrate the additional value of this integration

A grid computing framework for commercial simulation packages

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.An increased need for collaborative research among different organizations, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users non-trivial access to geographically dispersed computing resources (processors, storage, applications, data, instruments, etc.) that are administered in multiple computer domains. The term grid computing or grids is popularly used to refer to such distributed systems. A broader definition of grid computing includes the use of computing resources within an organization for running organization-specific applications. This research is in the context of using grid computing within an enterprise to maximize the use of available hardware and software resources for processing enterprise applications. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by simulation practitioners using Windows-based commercially available simulation packages to model simulations in industry. These packages are commonly referred to as Commercial Off-The-Shelf (COTS) Simulation Packages (CSPs). The study identifies several higher level grid services that could be potentially used to support the practise of simulation in industry. It proposes a grid computing framework to investigate these services in the context of CSP-based simulations. This framework is called the CSP-Grid Computing (CSP-GC) Framework. Each identified higher level grid service in this framework is referred to as a CSP-specific service. A total of six case studies are presented to experimentally evaluate how grid computing technologies can be used together with unmodified simulation packages to support some of the CSP-specific services. The contribution of this thesis is the CSP-GC framework that identifies how simulation practise in industry may benefit from the use of grid technology. A further contribution is the recognition of specific grid computing software (grid middleware) that can possibly be used together with existing CSPs to provide grid support. With its focus on end-users and end-user tools, it is intended that this research will encourage wider adoption of grid computing in the workplace and that simulation users will derive benefit from using this technology

An Architectural Framework for Performance Analysis: Supporting the Design, Configuration, and Control of DIS /HLA Simulations

Technology advances are providing greater capabilities for most distributed computing environments. However, the advances in capabilities are paralleled by progressively increasing amounts of system complexity. In many instances, this complexity can lead to a lack of understanding regarding bottlenecks in run-time performance of distributed applications. This is especially true in the domain of distributed simulations where a myriad of enabling technologies are used as building blocks to provide large-scale, geographically disperse, dynamic virtual worlds. Persons responsible for the design, configuration, and control of distributed simulations need to understand the impact of decisions made regarding the allocation and use of the logical and physical resources that comprise a distributed simulation environment and how they effect run-time performance. Distributed Interactive Simulation (DIS) and High Level Architecture (HLA) simulation applications historically provide some of the most demanding distributed computing environments in terms of performance, and as such have a justified need for performance information sufficient to support decision-makers trying to improve system behavior. This research addresses two fundamental questions: (1) Is there an analysis framework suitable for characterizing DIS and HLA simulation performance? and (2) what kind of mechanism can be used to adequately monitor, measure, and collect performance data to support different performance analysis objectives for DIS and HLA simulations? This thesis presents a unified, architectural framework for DIS and HLA simulations, provides details on a performance monitoring system, and shows its effectiveness through a series of use cases that include practical applications of the framework to support real-world U.S. Department of Defense (DoD) programs. The thesis also discusses the robustness of the constructed framework and its applicability to performance analysis of more general distributed computing applications

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing