Skip to main content
SpringerLink
Log in

ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks

  • Chapter
  • First Online:
Self-Learning Optimal Control of Nonlinear Systems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 103))

Abstract

This chapter presents a novel sensor scheduling scheme based on adaptive dynamic programming (ADP), which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks (WSNs) with solar energy harvesting. Neural network (NN) is used to model the solar energy harvesting. Kalman filter (KF) estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The present method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Alippi, C., Galperti, C.: An adaptive system for optimal solar energy harvesting in wireless sensor network nodes. IEEE Trans. Circuits Syst. I 55(6), 1742–1750 (2008)

    Article  MathSciNet  Google Scholar 

  2. Fadare, D.A.: Modelling of solar energy potential in Nigeria using an artificial neural network model. Appl. Energy 86, 1410–1422 (2009)

    Article  Google Scholar 

  3. Hengster-Movric, K., You, K., Lewis, F.L., Xie, L.: Synchronization of discrete-time multi-agent systems on graphs using Riccati design. Automatica 49(2), 414–423 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Huang, Y., Liu, D.: Neural-network-based optimal tracking control scheme for a class of unknown discrete-time nonlinear systems using iterative ADP algorithm. Neurocomputing 125, 46–56 (2014)

    Article  Google Scholar 

  5. Koutsopoulos, I., Stańczak, S.: The impact of transmit rate control on energy-efficient estimation in wireless sensor network. IEEE Trans. Wirel. Commun. 11(9), 3261–3271 (2012)

    Article  Google Scholar 

  6. Li, B., Li, H., Wang, W., Yin, Q., Liu, H.: Performance analysis and optimization for energy-efficient cooperative transmission in random wireless sensor network. IEEE Trans. Wirel. Commun. 12(9), 4647–4657 (2013)

    Article  Google Scholar 

  7. Li, Y., Chen, C.S., Song, Y., Wang, Z., Sun, Y.: Enhancing real-time delivery in wireless sensor networks with two-hop information. IEEE Trans. Ind. Inf. 5(2), 113–122 (2009)

    Article  Google Scholar 

  8. Liang, J., Molina, D.D., Venayagamoorthy, G.K., Harley, R.G.: Two-level dynamic stochastic optimal power flow control for power systems with intermittent renewable generation. IEEE Trans. Pow. Syst. 28(3), 2670–2678 (2013)

    Article  Google Scholar 

  9. Maheswararajah, S., Halgamuge, S.K., Premaratne, M.: Sensor scheduling for target tracking by suboptimal algorithms. IEEE Trans. Veh. Technol. 58(3), 1467–1479 (2009)

    Article  Google Scholar 

  10. Mo, Y.L., Ambrosino, R., Sinopoli, B.: Sensor selection strategies for state estimation in energy constrained wireless sensor networks. Automatica 47, 1330–1338 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  11. Molina, D., Venayagamoorthy, G.K., Liang, J., Harley, R.G.: Intelligent local area signals based damping of power system oscillations using virtual generators and approximate dynamic programming. IEEE Trans. Smart Grid 4(1), 498–508 (2013)

    Article  Google Scholar 

  12. Ni, Z., He, H., Wen, J.: Adaptive learning in tracking control based on the dual critic network design. IEEE Trans. Neural Netw. Learn. Syst. 24(6), 913–928 (2013)

    Article  Google Scholar 

  13. Ni, Z., He, H., Wen, J., Xu, X.: Goal representation heuristic dynamic programming on maze navigation. IEEE Trans. Neural Netw. Learn. Syst. 24(12), 2038–2050 (2013)

    Article  Google Scholar 

  14. Prokhorov, D.V., Wunsch, D.C.: Adaptive critic designs. IEEE Trans. Neural Netw. 8(5), 997–1007 (1997)

    Article  Google Scholar 

  15. Rout, R.R., Ghosh, S.K.: Enhancement of lifetime using duty cycle and network coding in wireless sensor networks. IEEE Trans. Wirel. Commun. 12(2), 656–667 (2013)

    Article  Google Scholar 

  16. Shi, L., Jia, Q.S., Mo, Y.L., Sinopoli, B.: Sensor scheduling over a packet-delaying network. Automatica 47, 1089–1092 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  17. Song, R., Wei, Q., Xiao, W.: ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks. Neural Comput. Appl. 27, 1543–1551 (2016)

    Article  Google Scholar 

  18. Song, R., Xiao, W., Zhang, H., Sun, C.: Adaptive dynamic programming for a class of complex-valued nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 25(9), 1733–1739 (2014)

    Article  Google Scholar 

  19. Song, R., Zhang, H.: The finite horizon optimal control for a class of time-delay affine nonlinear system. Neural Comput. Appl. 22(2), 229–235 (2013)

    Article  MathSciNet  Google Scholar 

  20. Squartini, S., Lu, J., Wei, Q.: The neural paradigm for complex systems: new algorithms and applications. Neural Comput. Appl. 22(2), 203–204 (2013)

    Article  Google Scholar 

  21. Vamvoudakis, K.G., Lewis, F.L.: Multi-player non-zero-sum games: online adaptive learning solution of coupled Hamilton-Jacobi equations. Automatica 47(8), 1556–1569 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  22. Wei, D., Jin, Y., Vural, S., Moessner, K., Tafazolli, R.: An energy-efficient clustering solution for wireless sensor network. IEEE Trans. Wirel. Commun. 10(11), 3973–3983 (2011)

    Article  Google Scholar 

  23. Wei, Q., Liu, D.: Adaptive dynamic programming for optimal tracking control of unknown nonlinear systems with application to coal gasification. IEEE Trans. Autom. Sci. Eng. 11(4), 1020–1036 (2014)

    Article  Google Scholar 

  24. Wu, Y., Liu, W.: Routing protocol based on genetic algorithm for energy harvesting-wireless sensor networks. IET Wirel. Sens. Syst. 3(2), 112–118 (2013)

    Article  Google Scholar 

  25. Xiao, W., Song, R.: Adaptive dynamic programming for sensor scheduling in energy-constrained wireless sensor networks. In: Proceedings of the 15th International Conference on Information Fusion, pp. 991–996 (2012)

    Google Scholar 

  26. Xiao, W., Song, R.: Self-learning sensor scheduling for target tracking in wireless sensor networks based on adaptive dynamic programming. In: Proceedings of 10th World Congress on Intelligent Control and Automation, pp. 1056–1061 (2012)

    Google Scholar 

  27. Xu, H., Jagannathan, S.: Stochastic optimal controller design for uncertain nonlinear networked control system via neuro dynamic programming. IEEE Trans. Neural Netw. Learn. Syst. 24(3), 471–484 (2013)

    Article  Google Scholar 

  28. Xu, X., Lian, C., Zuo, L., He, H.: Kernel-based approximate dynamic programming for real-time online learning control: an experimental study. IEEE Trans. Control Syst. Technol. 22(1), 146–156 (2014)

    Article  Google Scholar 

  29. Zhang, H., Wei, Q., Liu, D.: An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games. Automatica 47(1), 207–214 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang, H., Wei, Q., Luo, Y.: A novel infinite-time optimal tracking control scheme for a class of discrete-time nonlinear systems via the greedy HDP iteration algorithm. IEEE Trans. Syst. Man Cybern. Part B Cybern. 38(4), 937–942 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Qinglai Wei & Benkai Li

  2. University of Science and Technology Beijing, Beijing, China

    Ruizhuo Song

  3. Guangxi University, Guangxi, China

    Xiaofeng Lin

Authors
  1. Qinglai Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruizhuo Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benkai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaofeng Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinglai Wei .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Science Press, Beijing and Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Wei, Q., Song, R., Li, B., Lin, X. (2018). ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks. In: Self-Learning Optimal Control of Nonlinear Systems. Studies in Systems, Decision and Control, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-10-4080-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4080-1_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4079-5

  • Online ISBN: 978-981-10-4080-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation