Performance Analysis of Live-Virtual-Constructive and Distributed Virtual Simulations: Defining Requirements in Terms of Temporal Consistency

This research extends the knowledge of live-virtual-constructive (LVC) and distributed virtual simulations (DVS) through a detailed analysis and characterization of their underlying computing architecture. LVCs are characterized as a set of asynchronous simulation applications each serving as both producers and consumers of shared state data. In terms of data aging characteristics, LVCs are found to be first-order linear systems. System performance is quantified via two opposing factors; the consistency of the distributed state space, and the response time or interaction quality of the autonomous simulation applications. A framework is developed that defines temporal data consistency requirements such that the objectives of the simulation are satisfied. Additionally, to develop simulations that reliably execute in real-time and accurately model hierarchical systems, two real-time design patterns are developed: a tailored version of the model-view-controller architecture pattern along with a companion Component pattern. Together they provide a basis for hierarchical simulation models, graphical displays, and network I/O in a real-time environment. For both LVCs and DVSs the relationship between consistency and interactivity is established by mapping threads created by a simulation application to factors that control both interactivity and shared state consistency throughout a distributed environment

Modeling Quantum Optical Components, Pulses and Fiber Channels Using OMNeT++

Quantum Key Distribution (QKD) is an innovative technology which exploits the laws of quantum mechanics to generate and distribute unconditionally secure cryptographic keys. While QKD offers the promise of unconditionally secure key distribution, real world systems are built from non-ideal components which necessitates the need to model and understand the impact these non-idealities have on system performance and security. OMNeT++ has been used as a basis to develop a simulation framework to support this endeavor. This framework, referred to as "qkdX" extends OMNeT++'s module and message abstractions to efficiently model optical components, optical pulses, operating protocols and processes. This paper presents the design of this framework including how OMNeT++'s abstractions have been utilized to model quantum optical components, optical pulses, fiber and free space channels. Furthermore, from our toolbox of created components, we present various notional and real QKD systems, which have been studied and analyzed.Comment: Published in: A. F\"orster, C. Minkenberg, G. R. Herrera, M. Kirsche (Eds.), Proc. of the 2nd OMNeT++ Community Summit, IBM Research - Zurich, Switzerland, September 3-4, 201

ADS-B Classification using Multivariate Long Short-term Memory–fully Convolutional Networks and Data Reduction Techniques

Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of previous efforts with only 25% of the data). The findings show that feature selection and data quality lead to higher classification accuracy than data quantity. The second experiment accepted all ADS-B feature combinations and determined that airspeed, barometric pressure, and vertical speed had the most impact on aircraft engine type prediction

Aerodynamic Centers of Arbitrary Airfoils Below Stall

The aerodynamic center of an airfoil is commonly estimated to lie at the quarter-chord. This traditional estimate is based on thin airfoil theory, which neglects aerodynamic and geometric nonlinearities. Even below stall, these nonlinearities can have a significant effect on the location of the aerodynamic center. Here, a method is presented for accurately predicting the aerodynamic center of any airfoil from known lift, drag, and pitching-moment data as a function of angle of attack. The method accounts for aerodynamic and geometric nonlinearities, and it does not include small-angle, small-camber, and thin-airfoil approximations. It is shown that the aerodynamic center of an airfoil with arbitrary amounts of thickness and camber in an inviscid flow is a single, deterministic point, independent of angle of attack, and lies at the quarter-chord only in the limit as the airfoil thickness and camber approach zero. Furthermore, it is shown that, once viscous effects are included, the aerodynamic center is not a single point but is a function of angle of attack. Differences between this general solution and that predicted by the thin airfoil theory can be on the order of 3%, which is significant when predicting flutter speeds. Additionally, the results have implications for predicting the neutral point of a complete aircraft

Traffic Collision Avoidance System: False Injection Viability

Safety is a simple concept but an abstract task, specifically with aircraft. One critical safety system, the Traffic Collision Avoidance System II (TCAS), protects against mid-air collisions by predicting the course of other aircraft, determining the possibility of collision, and issuing a resolution advisory for avoidance. Previous research to identify vulnerabilities associated with TCAS’s communication processes discovered that a false injection attack presents the most comprehensive risk to veritable trust in TCAS, allowing for a mid-air collision. This research explores the viability of successfully executing a false injection attack against a target aircraft, triggering a resolution advisory. Monetary constraints precluded access to a physical TCAS unit; instead, this research creates a novel program, TCAS-False Injection Environment (TCAS-FIE), that incorporates real-world distributed computing systems to simulate a ground-based attacker scenario which explores how a false injection attack could target an operational aircraft. TCAS-FIEs’ simulation models are defined by parameters to execute tests that mimic real-world TCAS units during Mode S message processing. TCAS-FIE simulations execute tests over applicable ranges (5–30 miles), altitudes (25–45K ft), and bearings standard for real-world TCAS tracking. The comprehensive tests compare altitude, measure range closure rate, and measure signal strength from another aircraft to determine the delta in bearings over time. In the attack scenario, the ground-based adversary falsely injects a spoofed aircraft with characteristics matching a Boeing 737-800 aircraft, targeting an operational Boeing 737-800 aircraft. TCAS-FIE completes 555,000 simulations using the various ranges, altitudes, and bearings. The simulated success rate to trigger a resolution advisory is 32.63%, representing 181,099 successful resolution advisory triggers out of 555,000 total simulations. The results from additional analysis determine the required ranges, altitudes, and bearing parameters to trigger future resolution advisories, yielding a predictive threat map for aircraft false injection attacks. The resulting map provides situational awareness to pilots in the event of a real-world TCAS anomaly

Evaluation Criteria for Selecting NoSQL Databases in a Single Box Environment

In recent years, NoSQL database systems have become increasingly popular, especially for big data, commercial applications. These systems were designed to overcome the scaling and flexibility limitations plaguing traditional relational database management systems (RDBMSs). Given NoSQL database systems have been typically implemented in large-scale distributed environments serving large numbers of simultaneous users across potentially thousands of geographically separated devices, little consideration has been given to evaluating their value within single-box environments. It is postulated some of the inherent traits of each NoSQL database type may be useful, perhaps even preferable, regardless of scale. Thus, this paper proposes criteria conceived to evaluate the usefulness of NoSQL systems in small-scale single-box environments. Specifically, key value, document, column family, and graph database are discussed with respect to the ability of each to provide CRUD transactions in a single-box environment

Symmetric Dimethylarginine Is Not Associated with Cumulative Inflammatory Load or Classical Cardiovascular Risk Factors in Rheumatoid Arthritis: A 6-Year Follow-Up Study

Symmetric dimethylarginine (SDMA) indirectly inhibits nitric oxide (NO) synthesis and predicts cardiovascular and all-cause mortality in high-risk patients. The aim of our study was to investigate the associations of cumulative inflammatory burden (assessed by serial measurements of inflammatory markers) and classical cardiovascular (CV) disease risk factors with SDMA in RA patients. 201 RA patients (155 females, median age 67 (59�73)) were assessed at baseline (2006). Classical CV disease risk factors were recorded and systemic inflammation was determined by themeasurement of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). At follow-up (2012) SDMA levels were measured by enzyme-linked immunosorbent assay.Mean SDMA levels in RA population were 0.40 (0.40�0.53) ����mol/L. No significant association between SDMA and cumulative inflammatory load was established in the analysis. SDMA levels were not found to be significantly related to CV disease risk factors.We explored the potential relationship between SDMA and cumulative inflammatory burden in patients with RA and obtained negative results. SDMA did not relate to CV disease risk factors in our population and its clinical significance as a surrogate marker of endothelial dysfunction in patients with RA remains to be determined

Quantum Key Distribution: Boon or Bust

Quantum Key Distribution (QKD) is an emerging cybersecurity technology which provides the means for two geographically separated parties to grow “unconditionally secure” symmetric cryptographic keying material. Unlike traditional key distribution techniques, the security of QKD rests on the laws of quantum mechanics and not computational complexity. This unique aspect of QKD is due to the fact that any unauthorized eavesdropping on the key distribution channel necessarily introduces detectable errors (Gisin, Ribordy, Tittel, & Zbinden, 2002). This attribute makes QKD desirable for high-security environments such as banking, government, and military applications. However, QKD is a nascent technology where implementation non-idealities can negatively impact system performance and security (Mailloux, Grimaila, Hodson, Baumgartner, & McLaughlin, 2015). While the QKD community is making progress towards the viability of QKD solutions, it is clear that more work is required to quantify the impact of such non-idealities in real-world QKD systems (Scarani & Kurtsiefer, 2009)

Distribution of DDS-cerberus Authenticated Facial Recognition Streams

Successful missions in the field often rely upon communication technologies for tactics and coordination. One middleware used in securing these communication channels is Data Distribution Service (DDS) which employs a publish-subscribe model. However, researchers have found several security vulnerabilities in DDS implementations. DDS-Cerberus (DDS-C) is a security layer implemented into DDS to mitigate impersonation attacks using Kerberos authentication and ticketing. Even with the addition of DDS-C, the real-time message sending of DDS also needs to be upheld. This paper extends our previous work to analyze DDS-C’s impact on performance in a use case implementation. The use case covers an artificial intelligence (AI) scenario that connects edge sensors across a commercial network. Specifically, it characterizes how DDS-C performs between unmanned aerial vehicles (UAV), the cloud, and video streams for facial recognition. The experiments send a set number of video frames over the network using DDS to be processed by AI and displayed on a screen. An evaluation of network traffic using DDS-C revealed that it was not statistically significant compared to DDS for the majority of the configuration runs. The results demonstrate that DDS-C provides security benefits without significantly hindering the overall performance

Quantifying DDS-cerberus Network Control Overhead

Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of subscribers and publisher nodes from three to eighteen. By categorizing the network traffic from these nodes into either data message, security, or discovery+ with Quality of Service (QoS) best effort and reliable, the mean security traffic from DDS-C has minimal impact to Data Distribution Service (DDS) operations compared to other network traffic. The results reveal that applying DDS-C to a representative distributed network robotics operating system network does not impact performance