Asymptotically Optimal Transmission Policies for Large-Scale Low-Power Wireless Sensor Networks

Paschalidis, I Ch; Lai, W; Starobinski, D

doi:10.1109/TNET.2006.890108

Title: Asymptotically Optimal Transmission Policies for Large-Scale Low-Power Wireless Sensor Networks

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Abstract

We consider wireless sensor networks with multiple gateways and multiple classes of traffic carrying data generated by different sensory inputs. The objective is to devise joint routing, power control and transmission scheduling policies in order to gather data in the most efficient manner while respecting the needs of different sensing tasks (fairness). We formulate the problem as maximizing the utility of transmissions subject to explicit fairness constraints and propose an efficient decomposition algorithm drawing upon large-scale decomposition ideas in mathematical programming. We show that our algorithm terminates in a finite number of iterations and produces a policy that is asymptotically optimal at low transmission power levels. Furthermore, we establish that the utility maximization problem we consider can, in principle, be solved in polynomial time. Numerical results show that our policy is near-optimal, even at high power levels, and far superior to the best known heuristics at low power levels. We also demonstrate how to adapt our algorithm to accommodate energy constraints and node failures. The approach we introduce can efficiently determine near-optimal transmission policies for dramatically larger problem instances than an alternative enumeration approach.

Authors:: Paschalidis, I Ch; Lai, W; Starobinski, D

Publication Date:: Thu Feb 01 00:00:00 EST 2007

Research Org.:: Boston University

Sponsoring Org.:: USDOE - National Nuclear Security Administration (NNSA)

OSTI Identifier:: 902709

Report Number(s):: DOE/NA/27490-J1
Journal ID: 1063-6692

DOE Contract Number:: FG52-06NA27490

Resource Type:: Journal Article

Journal Name:: IEEE/ACM Transactions on Networking

Additional Journal Information:: Journal Volume: 15; Journal Issue: 1

Country of Publication:: United States

Language:: English

Subject:: 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; Mathematical programming, optimization, routing, transmission scheduling, wireless sensor networks

Citation Formats


                    Paschalidis, I Ch, Lai, W, and Starobinski, D. Asymptotically Optimal Transmission Policies for Large-Scale Low-Power Wireless Sensor Networks.  United States: N. p., 2007. 
        Web.  doi:10.1109/TNET.2006.890108.

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                    Paschalidis, I Ch, Lai, W, & Starobinski, D. Asymptotically Optimal Transmission Policies for Large-Scale Low-Power Wireless Sensor Networks.  United States.  https://doi.org/10.1109/TNET.2006.890108

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                    Paschalidis, I Ch, Lai, W, and Starobinski, D. 2007.  
        "Asymptotically Optimal Transmission Policies for Large-Scale Low-Power Wireless Sensor Networks".  United States.  https://doi.org/10.1109/TNET.2006.890108.

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@article{osti_902709,

  title        = {Asymptotically Optimal Transmission Policies for Large-Scale Low-Power Wireless Sensor Networks},

  author       = {Paschalidis, I Ch and Lai, W and Starobinski, D},

  abstractNote = {We consider wireless sensor networks with multiple gateways and multiple classes of traffic carrying data generated by different sensory inputs. The objective is to devise joint routing, power control and transmission scheduling policies in order to gather data in the most efficient manner while respecting the needs of different sensing tasks (fairness). We formulate the problem as maximizing the utility of transmissions subject to explicit fairness constraints and propose an efficient decomposition algorithm drawing upon large-scale decomposition ideas in mathematical programming. We show that our algorithm terminates in a finite number of iterations and produces a policy that is asymptotically optimal at low transmission power levels. Furthermore, we establish that the utility maximization problem we consider can, in principle, be solved in polynomial time. Numerical results show that our policy is near-optimal, even at high power levels, and far superior to the best known heuristics at low power levels. We also demonstrate how to adapt our algorithm to accommodate energy constraints and node failures. The approach we introduce can efficiently determine near-optimal transmission policies for dramatically larger problem instances than an alternative enumeration approach.},

  doi          = {10.1109/TNET.2006.890108},

  url          = {https://www.osti.gov/biblio/902709},
  journal      = {IEEE/ACM Transactions on Networking},
number       = 1,

  volume       = 15,

  place        = {United States},

  year         = {Thu Feb 01 00:00:00 EST 2007},

  month        = {Thu Feb 01 00:00:00 EST 2007}

}

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