Visual sensor network lifetime maximization by prioritized scheduling of nodes

  • Authors:

  • M. Hooshmand;S. M. R. Soroushmehr;P. Khadivi;S. Samavi;S. Shirani

  • Affiliations:

  • Department of Electrical and Computer Engineering, Isfahan University of Technology, Iran;Department of Electrical and Computer Engineering, Isfahan University of Technology, Iran;Department of Electrical and Computer Engineering, Isfahan University of Technology, Iran;Department of Electrical and Computer Engineering, Isfahan University of Technology, Iran and Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada;Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada

  • Venue:
  • Journal of Network and Computer Applications
  • Year:
  • 2013

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Abstract

Visual surveillance of a designated air space can be achieved by a randomly distributed camera sensor network spread over a large area. The location and field of view of each battery operated sensor, after a calibration phase, will be known to a central processing node. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. As a sensor dies another sensor should be selected to compensate for the dead one and reestablish the complete coverage. This process should be continued until complete coverage is not achievable by the existing sensors. Thereafter, a graceful degradation of the coverage is desirable. The goal is to elongate the lifetime of the network while maintaining a maximum possible coverage of the designated air space. Since the selection of a subset of sensors for complete coverage of the target area is an NP-complete problem, we present a number of heuristics for this case. The proposed methods are categorized in two groups. In one category, the sensors are prioritized based on their visual and communicative properties and the selection is performed according to the prioritizing function. In the other group, we propose traditional evolutionary and swarm intelligence algorithms. The performance of the proposed methods is evaluated through extensive simulations.