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Model, placement optimization and verification of a sky surveillance visual sensor network
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0003-1923-3843
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-6484-9260
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-3429-273X
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2013 (English)In: International Journal of Space-Based and Situated Computing (IJSSC), ISSN 2044-4893, E-ISSN 2044-4907, Vol. 3, no 3, p. 125-135Article in journal (Refereed) Published
Abstract [en]

A visual sensor network (VSN) is a distributed system of a large number of camera nodes, which generates two dimensional data. This paper presents a model of a VSN to track large birds, such as golden eagle, in the sky. The model optimises the placement of camera nodes in VSN. A camera node is modelled as a function of lens focal length and camera sensor. The VSN provides full coverage between two altitude limits. The model can be used to minimise the number of sensor nodes for any given camera sensor, by exploring the focal lengths that fulfils both the full coverage and minimum object size requirement. For the case of large bird surveillance, 100% coverage is achieved for relevant altitudes using 20 camera nodes per km² for the investigated camera sensors. A real VSN is designed and measurements of VSN parameters are performed. The results obtained verify the VSN model.

Place, publisher, year, edition, pages
2013. Vol. 3, no 3, p. 125-135
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:miun:diva-17118DOI: 10.1504/IJSSC.2013.056380OAI: oai:DiVA.org:miun-17118DiVA, id: diva2:558123
Available from: 2012-10-02 Created: 2012-10-02 Last updated: 2017-05-04Bibliographically approved
In thesis
1. Modelling and optimization of sky surveillance visual sensor network
Open this publication in new window or tab >>Modelling and optimization of sky surveillance visual sensor network
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A Visual Sensor Network (VSN) is a distributed system of a largenumber of camera sensor nodes. The main components of a camera sensornode are image sensor, embedded processor, wireless transceiver and energysupply. The major difference between a VSN and an ordinary sensor networkis that a VSN generates two dimensional data in the form of an image, whichcan be exploited in many useful applications. Some of the potentialapplication examples of VSNs include environment monitoring, surveillance,structural monitoring, traffic monitoring, and industrial automation.However, the VSNs also raise new challenges. They generate large amount ofdata which require higher processing powers, large bandwidth requirementsand more energy resources but the main constraint is that the VSN nodes arelimited in these resources.This research focuses on the development of a VSN model to track thelarge birds such as Golden Eagle in the sky. The model explores a number ofcamera sensors along with optics such as lens of suitable focal length whichensures a minimum required resolution of a bird, flying at the highestaltitude. The combination of a camera sensor and a lens formulate amonitoring node. The camera node model is used to optimize the placementof the nodes for full coverage of a given area above a required lower altitude.The model also presents the solution to minimize the cost (number of sensornodes) to fully cover a given area between the two required extremes, higherand lower altitudes, in terms of camera sensor, lens focal length, camera nodeplacement and actual number of nodes for sky surveillance.The area covered by a VSN can be increased by increasing the highermonitoring altitude and/or decreasing the lower monitoring altitude.However, it also increases the cost of the VSN. The desirable objective is toincrease the covered area but decrease the cost. This objective is achieved byusing optimization techniques to design a heterogeneous VSN. The core ideais to divide a given monitoring range of altitudes into a number of sub-rangesof altitudes. The sub-ranges of monitoring altitudes are covered by individualsub VSNs, the VSN1 covers the lower sub-range of altitudes, the VSN2 coversthe next higher sub-range of altitudes and so on, such that a minimum cost isused to monitor a given area.To verify the concepts, developed to design the VSN model, and theoptimization techniques to decrease the VSN cost, the measurements areperformed with actual cameras and optics. The laptop machines are used withthe camera nodes as data storage and analysis platforms. The area coverage ismeasured at the desired lower altitude limits of homogeneous as well asheterogeneous VSNs and verified for 100% coverage. Similarly, the minimumresolution is measured at the desired higher altitude limits of homogeneous aswell as heterogeneous VSNs to ensure that the models are able to track thebird at these highest altitudes.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2012
Series
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 86
National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-17123 (URN)STC (Local ID)978-91-87103-25-4 (ISBN)STC (Archive number)STC (OAI)
Supervisors
Available from: 2012-10-02 Created: 2012-10-02 Last updated: 2016-10-20Bibliographically approved
2. Modelling, optimization and design of visual sensor networks for sky surveillance
Open this publication in new window or tab >>Modelling, optimization and design of visual sensor networks for sky surveillance
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2013. p. 210
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 166
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-21022 (URN)978-91-87557-11-8 (ISBN)
Supervisors
Available from: 2014-01-13 Created: 2014-01-13 Last updated: 2014-04-24Bibliographically approved

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Ahmad, NaeemImran, MuhammadKhursheed, KhursheedLawal, NajeemO'Nils, Mattias

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