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International Conference on Wireless Algorithms, Systems, and Applications

WASA 2015: Wireless Algorithms, Systems, and Applications pp 767–777Cite as

Energy-Balanced Backpressure Routing for Stochastic Energy Harvesting WSNs

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9204))

Abstract

In Energy Harvesting Wireless Sensor Networks (EHWSNs), energy imbalance among sensor nodes is detrimental to network performance and battery life. Particularly, nodes that are closer to a data sink or have less energy replenishment tend to exhaust their energy earlier, leading to some sub-regions of the environment being left unmonitored. Existing research efforts focus on energy management based on the assumption that the energy harvesting process is predictable. Unfortunately, such an assumption is not practicable in real-world energy harvesting systems. With the consideration of the unpredictability of the harvestable energy, in this paper we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make routing decision, which could help mitigate the energy imbalance problem. We develop an online policies, Energy-balanced Backpressure Routing Algorithm (EBRA) for lossless networks. EBRA is distributed, queuing stable and do not require explicit knowledge of the statistics of the energy harvesting. The simulation data shows that EBRA could achieve significantly higher performance in terms of energy balance than the existing scheme Original Backpressure Algorithm (OBRA).

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Notes

  1. 1.

    When node m does not have enough data to forward, idle-fill may be used. The actual endogenous arrivals to node n are none idle packets received by node n.

  2. 2.

    Nodes with more residual energy are usually with better energy replenishment or lower traffic loads.

  3. 3.

    To minimize the time average penalty while stabilizing the network, algorithms based on the Lyapunov optimization framework can be designed to greedily minimize a bound (i.e., RHS of (10)) on the drift-plus-penalty expression (i.e., LHS of (10)) on each slot t.

  4. 4.

    We denote the average queue length \(\overline{Q}=\frac{1}{N}\sum _{n=1}^NQ_n(t)\) with \(t=2\times 10^5\). If the queue is stable, the time average queue length is approximately equal to \(N\cdot \overline{Q}\). Otherwise, the time average queue length is approximately proportional to \(\overline{Q}\) according the setting of our experiments. We evaluate \(\overline{Q}\) instead of time average queue length here.

  5. 5.

    According to  [11, 17], a smaller average queue size is related to a better performance of packet delay.

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Acknowledgment

This work was supported in part by the following funds: Fundamental Research Funds for the Central Universities (xjj2015065) and China Post doctoral Science Foundation (2015M570836).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Zheng Liu, Xinyu Yang & Peng Zhao

  2. Department of Computer and Information Sciences, Towson University, Towson, MD, 21252, USA

    Wei Yu

Authors
  1. Zheng Liu
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  2. Xinyu Yang
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  3. Peng Zhao
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  4. Wei Yu
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Corresponding author

Correspondence to Peng Zhao .

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Editors and Affiliations

  1. Arizona State University, Phoenix, Arizona, USA

    Kuai Xu

  2. Shanghai Jiao Tong University, Shanghai, China

    Haojin Zhu

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© 2015 Springer International Publishing Switzerland

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Liu, Z., Yang, X., Zhao, P., Yu, W. (2015). Energy-Balanced Backpressure Routing for Stochastic Energy Harvesting WSNs. In: Xu, K., Zhu, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2015. Lecture Notes in Computer Science(), vol 9204. Springer, Cham. https://doi.org/10.1007/978-3-319-21837-3_75

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  • DOI: https://doi.org/10.1007/978-3-319-21837-3_75

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21836-6

  • Online ISBN: 978-3-319-21837-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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