Optimization of self-directed target coverage in wireless multimedia sensor network.

Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing m...

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Publicado en:Scientific World Journal pp. 416218 - 416219
Autores principales: Yang, Yang, Wang, Yufei, Pi, Dechang, Wang, Ruchuan
Formato: research Journal Article
Publicado: Wiley-Blackwell 2014
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2014
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2014/416218
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        atl: Optimization of self-directed target coverage in wireless multimedia sensor network.
      aug:
        au:
          Yang, Yang
          Wang, Yufei
          Pi, Dechang
          Wang, Ruchuan
        affil: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China ; Department of Information Technology, Nanjing Radio and TV University, Nanjing City Vocational College, Nanjing, Jiangsu 210002, China.
      sug:
        subj:
          Algorithms
          Computer Communication Networks
          Models, Theoretical
          Multimedia
          Telemetry Methods
          Wireless Communications
      ab: Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers the target is defined by the deflection angle between target and the sensor's current orientation and the distance between target and the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor single-target, multisensor single-target, and single-sensor multitargets problems distinguishingly. Selecting the orientation that sensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to multisensor multitargets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors which covers all the targets in networks. Simulation results show the algorithm's performance and the effect of number of targets on the resulting subset.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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