This PDF file contains the front matter associated with SPIE Proceedings Volume 9849, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Modern software and network technologies are on the verge of enabling what has eluded the simulation and operational communities for more than two decades, truly integrating simulation functionality into operational Command and Control (C2) capabilities. This deep integration will benefit multiple stakeholder communities from experimentation and test to training by providing predictive and advanced analytics. There is a new opportunity to support operations with simulation once a deep integration is achieved. While it is true that doctrinal and acquisition issues remain to be addressed, nonetheless it is increasingly obvious that few technical barriers persist. How will this change the way in which common simulation and operational data is stored and accessed? As the Services move towards single networks, will there be technical and policy issues associated with sharing those operational networks with simulation data, even if the simulation data is operational in nature (e.g., associated with planning)? How will data models that have traditionally been simulation only be merged in with operational data models? How will the issues of trust be addressed?

The Night Vision Tactical Trainer - Shadow (NVTT-S) is a U.S. Army-developed training tool designed to improve critical Manned-Unmanned Teaming (MUMT) communication skills for payload operators in Unmanned Aerial Sensor (UAS) crews. The trainer is composed of several Government Off-The-Shelf (GOTS) simulation components and takes the trainee through a series of escalating engagements using tactically relevant, realistically complex, scenarios involving a variety of manned, unmanned, aerial, and ground-based assets. The trainee is the only human player in the game and he must collaborate, from his web-based mock operating station, with various non-human players via spoken natural language over simulated radio in order to execute the training missions successfully. Non-human players are modeled in two complementary layers - OneSAF provides basic background behaviors for entities while NVTT provides higher level models that control entity actions based on intent extracted from the trainee’s spoken natural dialog with game entities. Dialog structure is modeled based on Army standards for communication and verbal protocols. This paper presents an architecture that integrates the U.S. Army’s Night Vision Image Generator (NVIG), One Semi- Automated Forces (OneSAF), a flight dynamics model, as well as Commercial Off The Shelf (COTS) speech recognition and text to speech products to effect an environment with sufficient entity counts and fidelity to enable meaningful teaching and reinforcement of critical communication skills. It further demonstrates the model dynamics and synchronization mechanisms employed to execute purpose-built training scenarios, and to achieve ad-hoc collaboration on-the-fly between human and non-human players in the simulated environment.

In the fall of 2015, the Joint Staff J7 (JS J7) sponsored the Bold Quest (BQ) 15.2 event and conducted planning and coordination to combine this event into a joint event with the Army Warfighting Assessment (AWA) 16.1 sponsored by the U.S. Army. This multipurpose event combined a Joint/Coalition exercise (JS J7) with components of testing, training, and experimentation required by the Army. In support of Assistant Secretary of the Army for Acquisition, Logistics, and Technology (ASA(ALT)) System of Systems Engineering and Integration (SoSE&I), Always On-On Demand (AO-OD) used a system of systems (SoS) engineering approach to develop a live, virtual, constructive distributed environment (LVC-DE) to support risk mitigation utilizing this complex and challenging exercise environment for a system preparing to enter limited user test (LUT).

AO-OD executed a requirements-based SoS engineering process starting with user needs and objectives from Army Integrated Air and Missile Defense (AIAMD), Patriot units, Coalition Intelligence, Surveillance and Reconnaissance (CISR), Focused End State 4 (FES4) Mission Command (MC) Interoperability with Unified Action Partners (UAP), and Mission Partner Environment (MPE) Integration and Training, Tactics and Procedures (TTP) assessment. The SoS engineering process decomposed the common operational, analytical, and technical requirements, while utilizing the Institute of Electrical and Electronics Engineers (IEEE) Distributed Simulation Engineering and Execution Process (DSEEP) to provide structured accountability for the integration and execution of the AO-OD LVC-DE. As a result of this process implementation, AO-OD successfully planned for, prepared, and executed a distributed simulation support environment that responsively satisfied user needs and objectives, demonstrating the viability of an LVC-DE environment to support multiple user objectives and support risk mitigation activities for systems in the acquisition process.

In this paper, we develop methods for analyzing large amounts of data from a military ground combat simulation system. Through a series of processes, we focus the big data set on situations that correspond to important questions and show advantageous outcomes. The result is a decision support methodology that provides commanders with results that answer specific questions of interest, such as what the consequences for the Blue side are in various Red scenarios or what a particular Blue force can withstand. This approach is a step toward taking the traditional data farming methodology from its analytical view into a prescriptive operation planning context and a decision making mode.

The use of commodity mobile processors in wearable computing and field-deployed applications has risen as these processors have become increasingly powerful and inexpensive. Battery technology, however, has not advanced as quickly, and as the processing power of these systems has increased, so has their power consumption. In order to maximize endurance without compromising performance, fine-grained control of power consumption by these devices is highly desirable. Various methodologies exist to affect system-level bias with respect to the prioritization of performance or efficiency, but these are fragmented and global in effect, and so do not offer the breadth and granularity of control desired. This paper introduces a method of giving application programmers more control over system power consumption using a directive-based approach similar to existing APIs such as OpenMP. On supported platforms the compiler, application runtime, and Linux kernel will work together to translate the power-saving intent expressed in compiler directives into instructions to control the hardware, reducing power consumption when possible while still providing high performance when required.

Recent technological advances in web-based distributed computing and database technology have made possible a deeper and more transparent integration of some modeling and simulation applications. Despite these advances towards true integration of capabilities, disparate systems, architectures, and protocols will remain in the inventory for some time to come. These disparities present interoperability challenges for distributed modeling and simulation whether the application is training, experimentation, or analysis. Traditional approaches call for building gateways to bridge between disparate protocols and retaining interoperability specialists. Challenges in reconciling data models also persist. These challenges and their traditional mitigation approaches directly contribute to higher costs, schedule delays, and frustration for the end users. Osseus is a prototype software platform originally funded as a research project by the Defense Modeling & Simulation Coordination Office (DMSCO) to examine interoperability alternatives using modern, web-based technology and taking inspiration from the commercial sector. Osseus provides tools and services for nonexpert users to connect simulations, targeting the time and skillset needed to successfully connect disparate systems. The Osseus platform presents a web services interface to allow simulation applications to exchange data using modern techniques efficiently over Local or Wide Area Networks. Further, it provides Service Oriented Architecture capabilities such that finer granularity components such as individual models can contribute to simulation with minimal effort.

Graph analytics is a key component in identifying emerging trends and threats in many real-world applications. Largescale graph analytics frameworks provide a convenient and highly-scalable platform for developing algorithms to analyze large datasets. Although conceptually scalable, these techniques exhibit poor performance on modern computational hardware. Another model of graph computation has emerged that promises improved performance and scalability by using abstract linear algebra operations as the basis for graph analysis as laid out by the GraphBLAS standard. By using sparse linear algebra as the basis, existing highly efficient algorithms can be adapted to perform computations on the graph. This approach, however, is often less intuitive to graph analytics experts, who are accustomed to vertex-centric APIs such as Giraph, GraphX, and Tinkerpop. We are developing an implementation of the high-level operations supported by these APIs in terms of linear algebra operations. This implementation is be backed by many-core implementations of the fundamental GraphBLAS operations required, and offers the advantages of both the intuitive programming model of a vertex-centric API and the performance of a sparse linear algebra implementation. This technology can reduce the number of nodes required, as well as the run-time for a graph analysis problem, enabling customers to perform more complex analysis with less hardware at lower cost. All of this can be accomplished without the requirement for the customer to make any changes to their analytics code, thanks to the compatibility with existing graph APIs.

The low-tubal-rank tensors have been recently proposed to model real-world multidimensional data. In this paper, we study the low-tubal-rank tensor completion problem, i.e., to recover a third-order tensor by observing a subset of elements selected uniform at random. We propose a fast iterative algorithm, called Tubal-Alt-Min, that is inspired by similar approach for low rank matrix completion. The unknown low-tubal-rank tensor is parameterized as the product of two much smaller tensors with the low-tubal-rank property being automatically incorporated, and Tubal-Alt-Min alternates between estimating those two tensors using tensor least squares minimization. We note that the tensor least squares minimization is different from its counterpart and nontrivial, and this paper gives a routine to carry out this operation. Further, on both synthetic data and real-world video data, evaluation results show that compared with the tensor nuclear norm minimization, the proposed algorithm improves the recovery error by orders of magnitude with smaller running time for higher sampling rates.

The credibility of United States Army analytical experiments using distributed simulation depends on the quality of the simulation, the pedigree of the input data, and the appropriateness of the simulation system to the problem. The second of these factors is best met by using classified performance data from the Army Materiel Systems Analysis Activity (AMSAA) for essential battlefield behaviors, like sensors, weapon fire, and damage assessment.

Until recently, using classified data has been a time-consuming and expensive endeavor: it requires significant technical expertise to load, and it is difficult to verify that it works correctly. Fortunately, new capabilities, tools, and processes are available that greatly reduce these costs. This paper will discuss these developments, a new method to verify that all of the components are configured and operate properly, and the application to recent Army Capabilities Integration Center (ARCIC) experiments.

Recent developments have focused improving the process to load the data. OneSAF has redesigned their input data file formats and structures so that they correspond exactly with the Standard File Format (SFF) defined by AMSAA, ARCIC developed a library of supporting configurations that correlate directly to the AMSAA nomenclature, and the Entity Validation Tool was designed to quickly execute the essential models with a test-jig approach to identify problems with the loaded data.

The missing part of the process is provided by the new Expected Results Method. Instead of the usual subjective assessment of quality, e.g., "It looks about right to me", this new approach compares the performance of a combat model with authoritative expectations to quickly verify that the model, data, and simulation are all working correctly.

Integrated together, these developments now make it possible to use AMSAA classified performance data with minimal time and maximum assurance that the experiment's analytical results will be of the highest quality possible.

Modeling and simulation has been widely used as a training and educational tool for depicting different evacuation strategies and damage control decisions during evacuation. However, there are few simulation environments that can include human behavior with low to high levels of fidelity. It is well known that crowd stampede induced by panic leads to fatalities as people are crushed or trampled. Our proposed goal finding application can be used to model situations that are difficult to test in real-life due to safety considerations. It is able to include agent characteristics and behaviors. Findings of this model are very encouraging as agents are able to assume various roles to utilize fuzzy logic on the way to reaching their goals. Fuzzy logic is used to model stress, panic and the uncertainty of emotions. The fuzzy rules link these parts together while feeding into behavioral rules. The contributions of this paper lies in our approach of utilizing fuzzy logic to show learning and adaptive behavior of agents in a goal finding application. The proposed application will aid in running multiple evacuation drills for what-if scenarios by incorporating human behavioral characteristics that can scale from a room to building. Our results show that the inclusion of fuzzy attributes made the evacuation time of the agents closer to the real time drills.

The US Navy faces several limitations when planning operations in regard to forecasting environmental conditions. Currently, mission analysis and planning tools rely heavily on short-term (less than a week) forecasts or long-term statistical climate products. However, newly available data in the form of weather forecast ensembles provides dynamical and statistical extended-range predictions that can produce more accurate predictions if ensemble members can be combined correctly. Charles River Analytics is designing the Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS), which performs data fusion over extended-range multi-model ensembles, such as the North American Multi-Model Ensemble (NMME), to produce a unified forecast for several weeks to several seasons in the future. We evaluated thirty years of forecasts using machine learning to select predictions for an all-encompassing and superior forecast that can be used to inform the Navy’s decision planning process.

The Data Distribution Service (DDS) was started by the Object Management Group (OMG) in 2004. Currently, DDS is one of the contenders to support the Internet of Things (IoT) and the Industrial IOT (IIoT). DDS has also been used as a distributed simulation architecture. Given the anticipated proliferation of IoT and II devices, along with the explosive growth of sensor technology, can we expect this to have an impact on the broader community of distributed simulation? If it does, what is the impact and which distributed simulation domains will be most affected? DDS shares many of the same goals and characteristics of distributed simulation such as the need to support scale and an emphasis on Quality of Service (QoS) that can be tailored to meet the end user’s needs. In addition, DDS has some built-in features such as security that are not present in traditional distributed simulation protocols. If the IoT and II realize their potential application, we predict a large base of technology to be built around this distributed data paradigm, much of which could be directly beneficial to the distributed M&S community. In this paper we compare some of the perceived gaps and shortfalls of current distributed M&S technology to the emerging capabilities of DDS built around the IoT. Although some trial work has been conducted in this area, we propose a more focused examination of the potential of these new technologies and their applicability to current and future problems in distributed M&S.

The Internet of Things (IoT) and its data communications mechanisms such as the Data Distribution System (DDS) share properties in common with distributed modeling and simulation (M&S) and its protocols such as the High Level Architecture (HLA) and the Test and Training Enabling Architecture (TENA). This paper proposes a framework based on the sensor use case for how the two communities of practice (CoP) can benefit from one another and achieve greater capability in practical distributed computing.

A mobile ad hoc network is a collection of mobile nodes which communicate without a fixed backbone or centralized infrastructure. Due to the frequent mobility of nodes, routes connecting two distant nodes may change. Therefore, it is not possible to establish a priori fixed paths for message delivery through the network. Because of its importance, routing is the most studied problem in mobile ad hoc networks. In addition, if the Quality of Service (QoS) is demanded, one must guarantee the QoS not only over a single hop but over an entire wireless multi-hop path which may not be a trivial task. In turns, this requires the propagation of QoS information within the network. The key to the support of QoS reporting is QoS routing, which provides path QoS information at each source. To support QoS for real-time traffic one needs to know not only minimum delay on the path to the destination but also the bandwidth available on it. Therefore, throughput, end-to-end delay, and routing overhead are traditional performance metrics used to evaluate the performance of routing protocol. To obtain additional information about the link, most of quality-link metrics are based on calculation of the lost probabilities of links by broadcasting probe packets. In this paper, we address the problem of including multiple routing metrics in existing routing packets that are broadcasted through the network. We evaluate the efficiency of such approach with modified version of DSDV routing protocols in ns-3 simulator.