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Sensor Assignment to Missions: A Natural Language Knowledge-Based Approach

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Mission-Oriented Sensor Networks and Systems: Art and Science

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 163))

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Abstract

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 chapter, we describe a knowledge-driven approach to ISR asset assignment using ontologies, allocation algorithms, and a service-oriented architecture and we analyse the approach of using a representation based on Controlled English (CE) to improve the interface and human-in-the-loop aspects of the sensor assignment. An illustration of the use of the system from a mobile device is presented.

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Notes

  1. 1.

    https://www.gov.uk/government/publications/eight-great-technologies-infographics.

  2. 2.

    http://www.wired.com/opinion/2012/12/20-12-st_thompson/.

  3. 3.

    https://www.apple.com/ios/siri/.

  4. 4.

    http://www.google.com/now/.

  5. 5.

    http://www.w3.org/TR/owl-guide/.

  6. 6.

    An OWL description logic reasoner, http://clarkparsia.com/pellet/.

  7. 7.

    In some cases, a hierarchy of detectables allows some inference here; for example, any clause involving detect and a kind of detectable D is considered to cover all more specialised kinds of D also—so detect { car } covers specialised kinds of car (jeep, SUV, saloon, etc.).

  8. 8.

    The ontology contains the additional relationship interferes with to cover cases where types of sensor are incompatible. No valid bundle type can contain pairs of sensor types involved in an interferes with relationship.

  9. 9.

    http://www.fas.org/irp/imint/niirs_c/guide.htm.

  10. 10.

    Which in turn is similar to other efforts including SensorML [3], OntoSensor [28] and the W3C Semantic Sensor Network Incubator Group [15].

  11. 11.

    Here we show only the 4 elements of the tuple \(\langle IC, DS, FS, C, IT, NR \rangle \) from Sect. 2.1; FS and C are outside the scope of this chapter and omitted.

  12. 12.

    A bundle also contains specific asset instances, not shown here.

  13. 13.

    http://glass.google.com.

  14. 14.

    http://www.swi-prolog.org/.

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Acknowledgements

This research was sponsored by the US Army Research Laboratory and the UK Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the US Army Research Laboratory, the US Government, the UK Ministry of Defence or the UK Government. The US and UK Governments are authorised to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

   I am indebted to the many colleagues and students with whom I have collaborated on the SAM approach, including Amotz Bar-Noy, Konrad Borowiecki, Dave Braines, Mario Gomez, Chris Gwilliams, Tom La Porta, Matt Johnson, Geeth de Mel, Gavin Pearson, Tien Pham, Diego Pizzocaro and Hosam Rowaihy.

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  1. Crime and Security Research Institute, Cardiff University, Cardiff, UK

    Alun Preece

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  1. Alun Preece
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Correspondence to Alun Preece .

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  1. Wireless Sensor and Mobile Ad-hoc Network Applied Cryptography Engineering (WiSeMAN-ACE) Research Lab, Department of Electrical Engineering and Computer Science, Frank H. Dotterweich College of Engineering, Texas A&M University-Kingsville, Kingsville, TX, USA

    Habib M. Ammari

Appendix

Appendix

This code has been tested in SWI PrologFootnote 14 5.10.4 for MacOSX 10.7.

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Preece, A. (2019). Sensor Assignment to Missions: A Natural Language Knowledge-Based Approach. In: Ammari, H. (eds) Mission-Oriented Sensor Networks and Systems: Art and Science. Studies in Systems, Decision and Control, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-91146-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-91146-5_7

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