Joint utility optimization for wireless sensor networks with energy harvesting and cooperation


In wireless sensor networks (WSNs), the limited battery capacity restricts the lifetime of the sensor nodes and thus degrades the system performance. The energy harvesting and cooperation techniques are promising solutions to prolonging the battery life, by collecting energy from ambient environment and exchanging energy among sensor nodes. This paper studies the joint utility maximization problem for WSNs in consideration of energy harvesting and cooperation. We first derive an upper bound on the Lyapunov drift for the network stability, and then formulate the optimization as a stochastic optimization problem. Furthermore, we propose an energy harvesting and energy transfer, data transmission, power control, routing and scheduling (EDPR) online algorithm by combining Lyapunov optimization technique with drift-plus-penalty method and perturbation technique. It contributes to optimal utility in a distributed manner along with a balanced trade-off between network utility and queue backlog, with no need for any statistical information about dynamic systems and no concern of curse of dimensionality under large queue backlog. Simulation results also show the practicality of the proposed algorithm in real implementation since data transmission has a linear relationship with battery life.

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  1. 1

    Almalkawi I T, Guerrero Zapata M, Al-Karaki J N, et al. Wireless multimedia sensor networks: current trends and future directions. Sensors, 2010, 10: 6662–6717

    Article  Google Scholar 

  2. 2

    Zhang Y S, He Q, Xiang Y, et al. Low-cost and confidentiality-preserving data acquisition for internet of multimedia things. IEEE Int Thing J, 2018, 5: 3442–3451

    Article  Google Scholar 

  3. 3

    Zhu B, Xie L H, Han D M, et al. A survey on recent progress in control of swarm systems. Sci China Inf Sci, 2017, 60: 070201

    MathSciNet  Article  Google Scholar 

  4. 4

    Zhang Y, Xiang Y, Zhang L Y, et al. Secure wireless communications based on compressive sensing: a survey. IEEE Commun Surv Tut, 2019, 21: 1093–1111

    Article  Google Scholar 

  5. 5

    Yousaf R, Ahmad R, Ahmed W, et al. A unified approach of energy and data cooperation in energy harvesting WSNs. Sci China Inf Sci, 2018, 61: 082303

    Article  Google Scholar 

  6. 6

    Alippi C, Galperti C. An adaptive system for optimal solar energy harvesting in wireless sensor network nodes. IEEE Trans Circ Syst I, 2008, 55: 1742–1750

    MathSciNet  Google Scholar 

  7. 7

    Yang H H, Lee J, Quek T Q S. Heterogeneous cellular network with energy harvesting-based D2D communication. IEEE Trans Wirel Commun, 2016, 15: 1406–1419

    Article  Google Scholar 

  8. 8

    Dhillon H, Li Y, Nuggehalli P, et al. Fundamentals of heterogeneous cellular networks with energy harvesting. IEEE Trans Wirel Commun, 2014, 13: 2782–2797

    Article  Google Scholar 

  9. 9

    Vaze R. Transmission capacity of wireless ad hoc networks with energy harvesting nodes. In: Proceedings of Global Conference on Signal and Information Processing (GlobalSIP), Austin, 2013. 353–358

  10. 10

    Michelusi N, Stamatiou K, Zorzi M. Transmission policies for energy harvesting sensors with time-correlated energy supply. IEEE Trans Commun, 2013, 61: 2988–3001

    Article  Google Scholar 

  11. 11

    Prabuchandran K J, Meena S K, Bhatnagar S. Q-learning based energy management policies for a single sensor node with finite buffer. IEEE Wirel Commun Lett, 2013, 2: 82–85

    Article  Google Scholar 

  12. 12

    Hsu R C, Liu C T, Wang H L. A reinforcement learning-based ToD provisioning dynamic power management for sustainable operation of energy harvesting wireless sensor node. IEEE Trans Emerg Top Comput, 2014, 2: 181–191

    Article  Google Scholar 

  13. 13

    Yu H, Neely M J. Learning aided optimization for energy harvesting devices with outdated state information. In: Proceedings of IEEE Conference on Computer Communications, 2018. 1853–1861

  14. 14

    Neely M J. Super-fast delay tradeoffs for utility optimal fair scheduling in wireless networks. IEEE J Sel Areas Commun, 2006, 24: 1489–1501

    Article  Google Scholar 

  15. 15

    Huang L, Moeller S, Neely M J, et al. LIFO-backpressure achieves near-optimal utility-delay tradeoff. IEEE/ACM Trans Netw, 2013, 21: 831–844

    Article  Google Scholar 

  16. 16

    Huang L. Optimal sleep-wake scheduling for energy harvesting smart mobile devices. IEEE Trans Mobile Comput, 2017, 16: 1394–1407

    Article  Google Scholar 

  17. 17

    Neely M J. Stochastic network optimization with application to communication and queueing systems. Synth Lect Commun Netw, 2010, 3: 1–211

    Article  Google Scholar 

  18. 18

    Eryilmaz A, Srikant R. Joint congestion control, routing, and MAC for stability and fairness in wireless networks. IEEE J Sel Areas Commun, 2006, 24: 1514–1524

    Article  Google Scholar 

  19. 19

    Liu J, Shroff N B, Xia C H, et al. Joint congestion control and routing optimization: an efficient second-order distributed approach. IEEE/ACM Trans Netw, 2016, 24: 1404–1420

    Article  Google Scholar 

  20. 20

    Le L B, Modiano E, Shroff N B. Optimal control of wireless networks with finite buffers. IEEE/ACM Trans Netw, 2012, 20: 1316–1329

    Article  Google Scholar 

  21. 21

    Xu W Q, Zhang Y S, Shi Q J, et al. Dynamic optimization for heterogeneous powered wireless multimedia sensor networks with correlated sources and network coding. 2014. ArXiv:1410.5697

  22. 22

    You C S, Huang K B, Chae H. Energy efficient mobile cloud computing powered by wireless energy transfer. IEEE J Sel Areas Commun, 2016, 34: 1757–1771

    Article  Google Scholar 

  23. 23

    Bi S Z, Ho C K, Zhang R. Wireless powered communication: opportunities and challenges. IEEE Commun Mag, 2015, 53: 117–125

    Article  Google Scholar 

  24. 24

    Huang L B, Neely M J. Utility optimal scheduling in energy-harvesting networks. IEEE/ACM Trans Netw, 2013, 21: 1117–1130

    Article  Google Scholar 

  25. 25

    Gutiérrez M A, Steen K. Stochastic finite element methods. In: Encyclopedia of Computational Mechanics. Hoboken: Wiley, 2018

    Google Scholar 

  26. 26

    Tapparello C, Simeone O, Rossi M. Dynamic compression-transmission for energy-harvesting multihop networks with correlated sources. IEEE/ACM Trans Netw, 2014, 22: 1729–1741

    Article  Google Scholar 

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This work was supported in part by National Science and Technology Major Project of China (Grant No. 2018ZX03001008-002), in part by Natural Science Foundation of Jiangsu Province (Grant No. BK20180011), and in part by National Natural Science Foundation of China (Grant Nos. 61571120, 61871122).

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Correspondence to Jiamin Li.

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Zhu, P., Xu, B., Li, J. et al. Joint utility optimization for wireless sensor networks with energy harvesting and cooperation. Sci. China Inf. Sci. 63, 122302 (2020).

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  • drift-plus-penalty
  • energy management
  • Lyapunov analysis
  • utility optimization
  • WSNs