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Primal and dual-based algorithms for sensing range adjustment in WSNs

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Abstract

Coverage and tracking of multiple targets, are viewed as important challenges in WSNs, mainly aimed for future ubiquitous and pervasive applications. Target coverage in WSNs with large numbers of sensor nodes and targets, and with a predefined placement of sensors, may be conducted through adjusting the sensing range and considering the energy consumption related to this operation. In this paper, we encounter the problem of multiple target coverage in WSNs by determining the sensing range of each sensor node to maximize the total utility of the network. We solve this Network Utility Maximization (NUM) problem via two approaches, primal and dual decompositions, which result in iterative distributed price-based algorithms. Convergence of sensing ranges to optimal values is proved by means of stability analysis and simulation experiments. Simulation results show convergence to optimal values in few iterations, with near optimal values for the total objective function and energy consumption of nodes. These results show scalability of our algorithm, in terms of the number of iterations needed for convergence when compared with the other two methods. Furthermore, the distributed algorithm based on dual decomposition is used to cover efficiently moving targets in consecutive time intervals.

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Authors and Affiliations

  1. Computer Engineering and Information Technology Department, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran

    Marjan Naderan, Mehdi Dehghan & Hossein Pedram

Authors
  1. Marjan Naderan
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  2. Mehdi Dehghan
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  3. Hossein Pedram
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Correspondence to Mehdi Dehghan.

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Naderan, M., Dehghan, M. & Pedram, H. Primal and dual-based algorithms for sensing range adjustment in WSNs. J Supercomput 64, 310–330 (2013). https://doi.org/10.1007/s11227-011-0702-5

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  • DOI: https://doi.org/10.1007/s11227-011-0702-5

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