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Cost Optimization of a Sky Surveillance Visual Sensor Network
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.ORCID-id: 0000-0002-6484-9260
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.ORCID-id: 0000-0003-1923-3843
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Institutionen för informationsteknologi och medier.ORCID-id: 0000-0002-3429-273X
Visa övriga samt affilieringar
2012 (Engelska)Ingår i: Proceedings of SPIE - The International Society for Optical Engineering, Belgium: SPIE - International Society for Optical Engineering, 2012, s. Art. no. 84370U-Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

A Visual Sensor Network (VSN) is a network of spatially distributed cameras. The primary difference between VSN and other type of sensor network is the nature and volume of information. A VSN generally consists of cameras, communication, storage and central computer, where image data from multiple cameras is processed and fused. In this paper, we use optimization techniques to reduce the cost as derived by a model of a VSN to track large birds, such as Golden Eagle, in the sky. The core idea is to divide a given monitoring range of altitudes into a number of sub-ranges of altitudes. The sub-ranges of altitudes are monitored by individual VSNs, VSN1 monitors lower range, VSN2 monitors next higher and so on, such that a minimum cost is used to monitor a given area. The VSNs may use similar or different types of cameras but different optical components, thus, forming a heterogeneous network.  We have calculated the cost required to cover a given area by considering an altitudes range as single element and also by dividing it into sub-ranges. To cover a given area with given altitudes range, with a single VSN requires 694 camera nodes in comparison to dividing this range into sub-ranges of altitudes, which requires only 96 nodes, which is 86% reduction in the cost.

Ort, förlag, år, upplaga, sidor
Belgium: SPIE - International Society for Optical Engineering, 2012. s. Art. no. 84370U-
Serie
Proceedings of SPIE, ISSN 0277-786X ; 8437
Nyckelord [en]
camera sensor networks, coverage, camera placement, surveillance, deployment optimization
Nationell ämneskategori
Inbäddad systemteknik
Identifikatorer
URN: urn:nbn:se:miun:diva-15669DOI: 10.1117/12.924344ISI: 000305693900024Scopus ID: 2-s2.0-84861946894Lokalt ID: STCISBN: 978-0-8194-9129-9 (tryckt)OAI: oai:DiVA.org:miun-15669DiVA, id: diva2:473331
Konferens
Real-Time Image and Video Processing 2012;Brussels;19 April 2012through19 April 2012;Code90041
Tillgänglig från: 2012-01-05 Skapad: 2012-01-05 Senast uppdaterad: 2016-10-19Bibliografiskt granskad
Ingår i avhandling
1. Modelling and optimization of sky surveillance visual sensor network
Öppna denna publikation i ny flik eller fönster >>Modelling and optimization of sky surveillance visual sensor network
2012 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Sundsvall: Mid Sweden University, 2012
Serie
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 86
Nationell ämneskategori
Inbäddad systemteknik
Identifikatorer
urn:nbn:se:miun:diva-17123 (URN)STC (Lokalt ID)978-91-87103-25-4 (ISBN)STC (Arkivnummer)STC (OAI)
Handledare
Tillgänglig från: 2012-10-02 Skapad: 2012-10-02 Senast uppdaterad: 2016-10-20Bibliografiskt granskad
2. Modelling, optimization and design of visual sensor networks for sky surveillance
Öppna denna publikation i ny flik eller fönster >>Modelling, optimization and design of visual sensor networks for sky surveillance
2013 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Ort, förlag, år, upplaga, sidor
Sundsvall: Mid Sweden University, 2013. s. 210
Serie
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 166
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
urn:nbn:se:miun:diva-21022 (URN)978-91-87557-11-8 (ISBN)
Handledare
Tillgänglig från: 2014-01-13 Skapad: 2014-01-13 Senast uppdaterad: 2014-04-24Bibliografiskt granskad

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