Managing traffic between the Earth-Moon and Earth-Mars is a complex process requiring significant investment in resources and expertise at NASA. INSPiRE improves the performance of space networks by enabling a dynamic reconfiguration process that works for any mixed topology over a heterogeneous and multi-vendor network. To achieve the desired functionality, INSPiRE incorporates a set of algorithms, machine learning processes, and policy inference to handle unpredictable, disruptive events. INSPiRE draws parallels from the current notion of the 3GPP (5G and beyond) Network Slicing approach, where the same physical network divides into several virtual networks, and for each of these virtual networks, there is a guaranteed Quality of Service for the missions that they serve.

With the number of sensors constantly increasing, there is a great need for automating the processing of sensor data in order to reduce cognitive load and response time for manned systems and enable greater autonomy in unmanned systems. It is anticipated that the unprecedented access to sensor data (both in volume and variety) will lead to reduced false alarm rates and increased probability of detection of threats and targets. Effectively, this capability will support situational awareness and facilitate mission success. However, current signal and image processing systems largely ignore the scene context which hinders their performance. In this paper, we describe a machine learning- and semantic reasoning-based system for target detection which incorporates the context. It combines the state-of-the-art image and signal processing capability with the leading-edge logic-based semantic reasoning technology. The main focus of this paper is on the value added by the semantic reasoning to machine learning

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.

This paper describes some of the challenges that need to be addressed in order to develop collaborative spectrum-sharing systems. The importance of these challenges stems from the assumption that rules for spectrum sharing can change after the deployment of radio networks and that the whole system must be able to adapt to them. To address such a requirement, we used a policy-based approach in which transmissions are controlled by a policy interpreter system, and the policies can be modified during system operation. Our primary goal was to develop a prototype of such a system. In this paper, we outline the implementation of policy interpretation, automatic generation of transmission opportunities in case a request for transmission is denied by the policy reasoner, and the generation of rendezvous channels for the synchronization of otherwise asynchronously running software-defined radios.

This paper describes a program-SPARQL Query Generator (SQG)-which takes as input an OWL ontology, a set of object descriptions in terms of this ontology and an OWL class as the context, and generates relatively large numbers of queries about various types of descriptions of objects expressed in RDF/OWL. The intent is to use SQG in evaluating data representation and retrieval systems from the perspective of OWL semantics coverage. While there are many benchmarks for assessing the efficiency of data retrieval systems, none of the existing solutions for SPARQL query generation focus on the coverage of the OWL semantics. Some are not scalable since manual work is needed for the generation process; some do not consider (or totally ignore) the OWL semantics in the ontology/instance data or rely on large numbers of real queries/datasets that are not readily available in our domain of interest. Our experimental results show that SQG performs reasonably well with generating large numbers of queries and guarantees a good coverage of OWL axioms included in the generated queries.

This paper describes a program for generating RDF descriptions of objects of a specific type based on a given OWL ontology. We needed this kind of capability to compare the use of OWL vs. XML-based descriptions within scenarios in which capabilities of radios are matched against application needs. Most of the existing solutions for RDF dataset generation are not generic, i.e., they are locked-in to specific class models or ontologies. Existing generic solutions do not support sufficiently (or totally ignore) the OWL semantics, or rely on large numbers of real-world data. Our experimental results show that RODG performs reasonably well with generating large numbers of object descriptions, which are consistent with and have a good space coverage (variety) over the input ontology.

In multi-vendor T&E systems, a single hardware vendor cannot anticipate the dependencies on the settings from hardware manufactured by other vendors, or the systemic constraints that are specific to a particular customer. The T&E community has recognized the fact that MDL and TMATS XML are not sufficient to address this problem alone, and that there is a need for a separate, constraints language. Constraints written in such a language can be validated by a third party validation engine, without relying on any particular vendor’s software. To this end, we developed the concept of TACL, a candidate for the standard constraint language, and demonstrated it with a reference implementation of a TACL engine integrated with the iNET System Manager. In this paper, we argue that this integration should be standardized in the form of a Validation Protocol in order to turn the existing system into a loosely-coupled, standards-based architecture.

Radio frequency spectrum management plays a critical role in various domains, including government, military, industrial and personal communications. Current methodology of spectrum management relies primarily on licensing, i.e., giving control over a specific part of the spectrum to a limited number of providers. This approach, however, may lead to an underutilization of the spectrum. To address this problem, various dynamic spectrum access and management approaches have been investigated and some are being actively tested in the field. In the work described in this paper we use the Model-Based Spectrum Management (MBSM) approach to policy-based dynamic spectrum management in which spectrum access policies are represented using Spectrum Consumption Models (SCMs). While in MBSM SCMs are expressed in an XML markup language called SCMML, we add a “logical” layer to MBSM by mapping SCMML to an ontology expressed in Web Ontology Language (OWL)—the formal language used in the Semantic Web. We show that it is possible to use such representations for automatically interpreting policies expressed in this layer by an ontology-based inference engine to derive decisions on the permissions of specific spectrum access requests. The main benefit of using the ontology-based representation is that it does not require addition of any new procedural code and thus new policies can be loaded and used by the system on the fly. The paper focuses on how to implement such a system. Towards this end, it presents two spectrum management related use cases and shows how these use cases are implemented using ontologies. The paper also discusses the advantages of the use of an ontology-based approach to dynamic spectrum management and its potential. Quantitative evaluation of the approach is part of our current work.

This paper provides an overview of research to apply human-machine Situation Awareness (SA) principles to Electronic Warfare (EW). The research leverages the Endsley SA model combined with Cognitive Radio Ontology extensions to provide automatic inference capabilities for EW. The result is an interoperable language for autonomous cognitive EW systems to create and share knowledge about the Electromagnetic Spectrum (EMS) and the platforms using it. In this paper, we explore several modeling and inference constructs using ontologies expressed in the Web Ontology Language (OWL). We show that these constructs are aligned with the elements of the Endsley model and when implemented in an autonomous system they can significantly enhance the SA of its users and lead to more informed courses of action.

A wireless network of software-defined radios can be considered as a distributed computing system, since the radios that are on the network possess the capabilities of performing various sensing and computation tasks requested by the applications running on other nodes. In order to achieve such an objective, radios need to perform various functions, often attributed to cognitive radios. E.g., devices may need to inform the network of their capabilities, applications may issue requests for services, and the network then can match the radio capabilities against the requests. Similarly, devices may request permission to transmit, which then need to be matched against policies and available resources (e.g., availability of spectrum). In all such scenarios, matching would have to be performed in order to derive decisions. One of the research questions is what languages are good for describing the requests, radio capabilities and policies? Since the solution to this problem needs to be flexible enough to address the scenarios in which radios with previously unknown capabilities and new applications can join the network dynamically, the language must be interpretable by the applications, radio devices and the network. In this paper we consider two options for expressing and solving the matching problem an XML based approach and aWebOntology Language approach. While our ultimate goal is to use quantitative metrics, such as processing time, bandwidth usage, precision, recall and F-measures to evaluate the two approaches, here we show howwe approach this problem and our qualitative assessments of the two approaches based on our initial results.

The amount of RDF data available on the Web has been increasingly growing over the past few years. Developing and fine-tuning SPARQL queries in order to sift through the data may be a very challenging task for human operators who need to quickly make sense of large graphs. In addition, often multiple queries need to be issued in order to gather and understand the context (relevant facts) for the explanation of the query. Thus, the challenge is not only to answer the query, but also to provide context, so that the analyst can easily comprehend what the data is actually conveying. This paper describes results of an investigation of the possibility to apply key aspects of Situation Theory, and its ontological realization in the Situation Theory Ontology, to simplify and abstract large RDF data sets, given a focus query from the analyst. In this approach, the query results are presented as concept maps. The approach was successfully implemented as a prototype, although this paper does not include a description of the tool.

In various scenarios, agents need to exchange information about situations - either the situations they are currently in or in reference to other circumstances. Assuming that agents are intelligent agents, i.e., they have their own inference capabilities, the question then is - which parts of the knowledge that an agent has need to be sent over (possibly narrow bandwidth) communication links in order to provide a complete description of the situation? Or in other words, which part of the agent's knowledge is relevant to a specific situation? The assumption behind this question is that only a small part of the agent's knowledge needs to be sent since the receiving agent can infer the rest of it locally. This paper discusses an example of reasoning about the relevancy of information. First, the notion of relevancy is defined. Then an implementation of relevance inference scenario is described.

The importance of visualization—discussed in the previous chapter—does not diminish the critical role that algorithmic analysis plays in achieving CSA. Algorithms reason about the voluminous observations and data about the network and infer important features of the situation that help analysts and decision-makers form their situational awareness. In order to perform this inference, and to make its output useful to other algorithms and human users, an algorithm needs to have its inputs and outputs represented in a consistent vocabulary of well-specified terms and their relations, i.e., it needs an ontology with a clear semantics and a standard. This topic is the focus of the present chapter. We already touched on the importance of semantics in the Cognition and Technology chapter. Now we discuss in detail how, in cyber operations, inference based on ontology can be used to determine the threat actor, the target and purpose in order to determine potential courses of action and future impact. Since a comprehensive ontology for cyber security does not exist, we show how such an ontology can be developed by taking advantage of existing cyber security related standards and markup languages.

This paper describes VIStology’s HADRian system for semantically integrating disparate information sources into a common operational picture (COP) for humanitarian assistance/disaster relief (HADR) operations. Here the system is applied to the task of determining where unexploded or additional bombs were being reported via Twitter in the hours immediately after the Boston Marathon bombing in April, 2013. We provide an evaluation of the results and discuss future directions. Keywords—social media, situational awareness, Boston Marathon bombing.

In this paper, we first describe a system we have implemented that takes expressions of policies, expressed in a fragment of English called SBVR SE (Semantics of Business Vocabulary and Rules Structured English), an OMG standard, and automatically translates them into an executable semantic web formalism (OWL 2 and semantic web rules). Specifically, we describe how these policies can be used to automatically enforce compliance with policies and to reconcile multiple policies specified by independent parties. The scenarios implemented concern information sharing via XMPP (“instant messaging”). We then outline how situations can be characterized as policy-compliant or policy-violating. In some cases, situations are policy compliant or violating because of events and actions that they contain. We show that our formalism supports this analysis.

Extensible Messaging and Presence Protocol (XMPP) is a popular open-standard protocol for instant messaging (IM) widely used in military and commercial applications. In military contexts, as in commercial settings, it is often necessary to regulate who may communicate with whom and how. The distributed nature of XMPP makes centralized information exchange policy enforcement impossible, however. We report on a technology we have developed, called PolVISor, in which we express information exchange policies in a natural language formalism (SBVR SE), automatically translate these policies into an executable rule language (BaseVISor rule language) and enforce and reconcile disparate policies among XMPP servers, each with its own policies, using semantic technologies.