Modelling and optimization of sky surveillance visual sensor network

Detta är en avhandling från Sundsvall : Mid Sweden University

Sammanfattning: 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.

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