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A Jackson network model and threshold policy for joint optimization of energy and delay in multi-hop wireless networks

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  • Xia, Li
  • Shihada, Basem

Abstract

This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.

Suggested Citation

  • Xia, Li & Shihada, Basem, 2015. "A Jackson network model and threshold policy for joint optimization of energy and delay in multi-hop wireless networks," European Journal of Operational Research, Elsevier, vol. 242(3), pages 778-787.
  • Handle: RePEc:eee:ejores:v:242:y:2015:i:3:p:778-787
    DOI: 10.1016/j.ejor.2014.10.063
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    References listed on IDEAS

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    1. Xia, Li & Cao, Xi-Ren, 2012. "Performance optimization of queueing systems with perturbation realization," European Journal of Operational Research, Elsevier, vol. 218(2), pages 293-304.
    2. Donald L. Iglehart, 1963. "Optimality of (s, S) Policies in the Infinite Horizon Dynamic Inventory Problem," Management Science, INFORMS, vol. 9(2), pages 259-267, January.
    3. Li, Yanjie & Cao, Fang, 2013. "A basic formula for performance gradient estimation of semi-Markov decision processes," European Journal of Operational Research, Elsevier, vol. 224(2), pages 333-339.
    4. Montemanni, Roberto & Leggieri, Valeria & Triki, Chefi, 2008. "Mixed integer formulations for the probabilistic minimum energy broadcast problem in wireless networks," European Journal of Operational Research, Elsevier, vol. 190(2), pages 578-585, October.
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    Cited by:

    1. Brooks, James D. & Kar, Koushik & Mendonça, David J., 2016. "Allocation of flows in closed bipartite queueing networks," European Journal of Operational Research, Elsevier, vol. 255(2), pages 333-344.
    2. Jing-Yu Ma & Quan-Lin Li, 2022. "Optimal dynamic mining policy of blockchain selfish mining through sensitivity-based optimization," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3663-3700, December.
    3. Keskin, Muhammed Emre, 2017. "A column generation heuristic for optimal wireless sensor network design with mobile sinks," European Journal of Operational Research, Elsevier, vol. 260(1), pages 291-304.

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