An Ontology-Centric Approach to Sensor-Mission Assignment

Gómez, Mario; Preece, Alun; Johnson, Matthew; Mel, Geeth de; Vasconcelos, Wamberto; Gibson, Christopher C.; Bar-Noy, Amotz; Borowiecki, Konrad; Porta, Thomas La; Pizzocaro, Diego; Rowaihy, Hosam; Pearson, Gavin; Pham, Tien

doi:10.1007/978-3-540-87696-0_30

Cited by 51 publications

(46 citation statements)

References 6 publications

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“…The core sensor, platform and task ontologies are based on pre-existing models, chosen for their relatively stable and accepted nature 6 . The ontologies are open to new asset, task, and capability types -in fact, the NIIRS framework was added as an extension to the original ontologies.…”

Section: Figure 1: Missions and Means Framework Isr Ontologymentioning

confidence: 99%

“…As part of a solution to this problem, several works have proposed the use of some form of knowledge base or mapping that relates sensor capabilities to task requirements, to aid either automatic or semi-automatic identification of suitable assets for tasks 3,4,5 . In our previous work 6 we developed an approach to automatic assettask assignment founded on the Military Missions and Means Framework (MMF) 7 . We created ontologies of task and asset types, and an automatic procedure for matching one to the other, through the capabilities required on one and provided by the other.…”

Section: Introductionmentioning

confidence: 99%

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Agilely Assigning Sensing Assets to Mission Tasks in a Coalition Context

et al. 2013

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A key problem in managing intelligence, surveillance and reconnaissance (ISR) operations in a coalition context is assigning available sensing assets -of which there are increasingly many -to mission tasks. High demands for information and relative scarcity of available assets implies that assignments must be made taking into account all possible ways of achieving an ISR task by different kinds of sensing. Moreover, the dynamic nature of most ISR situations means that asset assignment must be done in a highly agile manner. The problem is exacerbated in a coalition context because it is harder for users to have an overview of all suitable assets across multiple coalition partners. In this paper, we describe a knowledge-driven approach to ISR asset assignment using ontologies, allocation algorithms, and a service-oriented architecture. An illustration of the use of the system from a mobile device is presented.

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Section: Figure 1: Missions and Means Framework Isr Ontologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agilely Assigning Sensing Assets to Mission Tasks in a Coalition Context

et al. 2013

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“…Our original knowledge base and task-asset matching procedure 6,7 was based on an ontology derived from the Military Missions and Means Framework (MMF). 8 To derive our ontology, we formalized concepts and relationships from the MMF documentation, the main ones being as shown in Figure 1.…”

Section: Knowledge-based Matching Of Isr Assets To Tasksmentioning

confidence: 99%

“…In our previous work, 6,7 we developed an approach founded on the Military Missions and Means Framework (MMF). 8 We created ontologies of task and asset types, and an automatic procedure for matching one to the other, through the capabilities required on one and provided by the other.…”

Section: Introductionmentioning

confidence: 99%

Tasking and sharing sensing assets using controlled natural language

et al. 2012

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We introduce an approach to representing intelligence, surveillance, and reconnaissance (ISR) tasks at a relatively high level in controlled natural language. We demonstrate that this facilitates both human interpretation and machine processing of tasks. More specifically, it allows the automatic assignment of sensing assets to tasks, and the informed sharing of tasks between collaborating users in a coalition environment. To enable automatic matching of sensor types to tasks, we created a machine-processable knowledge representation based on the Military Missions and Means Framework (MMF), and implemented a semantic reasoner to match task types to sensor types. We combined this mechanism with a sensor-task assignment procedure based on a well-known distributed protocol for resource allocation. In this paper, we re-formulate the MMF ontology in Controlled English (CE), a type of controlled natural language designed to be readable by a native English speaker whilst representing information in a structured, unambiguous form to facilitate machine processing. We show how CE can be used to describe both ISR tasks (for example, detection, localization, or identification of particular kinds of object) and sensing assets (for example, acoustic, visual, or seismic sensors, mounted on motes or unmanned vehicles). We show how these representations enable an automatic sensor-task assignment process. Where a group of users are cooperating in a coalition, we show how CE task summaries give users in the field a high-level picture of ISR coverage of an area of interest. This allows them to make efficient use of sensing resources by sharing tasks.

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“…We can further argue that they can just be considered as a black box, especially that those components are not directly accessible by applications. However, we chose to allow users to describe the internal components of devices (also done in [9,10]) by their name and type only.…”

Section: Device Ontologymentioning

confidence: 99%

Ontologies for the internet of things

Hachem

Teixeira

Issarny

2011

Proceedings of the 8th Middleware Doctoral Symposium

126

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Challenges the Internet of Things (IoT) is facing are directly inherited from today's Internet. However, they are amplified by the anticipated large scale deployments of devices and services, information flow and involved users in the IoT. Challenges are many and we focus on addressing those related to scalability, heterogeneity of IoT components, and the highly dynamic and unknown nature of the network topology. In this paper, we give an overview of a service-oriented middleware solution that addresses those challenges using semantic technologies to provide interoperability and flexibility. We especially focus on modeling a set of ontologies that describe devices and their functionalities and thoroughly model the domain of physics. The physics domain is indeed at the core of the IoT, as it allows the approximation and estimation of functionalities usually provided by things. Those functionalities will be deployed as services on appropriate devices through our middleware.

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An Ontology-Centric Approach to Sensor-Mission Assignment

Cited by 51 publications

References 6 publications

Agilely Assigning Sensing Assets to Mission Tasks in a Coalition Context

Agilely Assigning Sensing Assets to Mission Tasks in a Coalition Context

Tasking and sharing sensing assets using controlled natural language

Ontologies for the internet of things

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