Abstract
Target tracking is a typical application of wireless sensor networks (WSNs), in which improving the tracking accuracy with the limited network resources is remaining as a challenging problem. Hence target tracking often relies on sensor scheduling approaches to optimize the resource utilization. With the development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to help weaken the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. Hence, the development of energy harvesting technologies provides a new challenge of infinite-horizon sensor scheduling with the finite energy harvesting capability for high performance target tracking. This paper proposes an adaptive multi-step sensor scheduling approach based on the mixed iterative adaptive dynamic programming (MIADP) to minimize the global performance composed of tracking performance and energy consumption. MIADP consists of two iterations: P-iteration to update the iterative value function and V-iteration to obtain the iterative control law sequence. The simulation results demonstrate that the proposed scheme has advantages in the global trade-off between tracking performance and energy consumption compared with adaptive dynamic programming (ADP) based single-step sensor scheduling.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Odat, E., Shamma, J.S., Claudel, C.: Vehicle classification and speed estimation using combined passive infrared/ultrasonic sensors. IEEE Trans. Intell. Transp. Syst. 19(5), 1593–1606 (2018)
Xiao, W.D., Wu, J.K., Xie, L.H., Dong, L.: Sensor scheduling for target tracking in networks of active sensors. Acta Automatica Sinica 32(6), 922–928 (2006)
Xiao, W.D., Zhang, S., Lin, J.Y., Tham, C.K.: Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks. J. Control Theor. Appl. 8(1), 86–92 (2010)
Huber, M.F.: Optimal pruning for multi-step sensor scheduling. IEEE Trans. Autom. Control 57(5), 1338–1343 (2012)
Wei, Q.L., Liu, D.R., Lin, H.Q.: Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems. IEEE Trans. Cybern. 46(3), 840–853 (2016)
Xiao, W.D., Liu, F., Zhang, J.J.: Adaptive dynamic programming for multi-point scheduling in energy harvesting wireless sensor networks. In: 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp. 1498–1502. IEEE, Beijing (2015)
Liu, F., Jiang, C.P., Chen, S., Xiao, W.D.: Multi-sensor scheduling for target tracking based on constrained ADP in energy harvesting WSN. In: 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1579–1584. IEEE, Wuhan (2018)
Chen, H.B., Zeng, Q., Zhao, F.: Efficient sleep scheduling algorithm for target tracking in double-storage energy harvesting sensor networks. Int. J. Distrib. Sens. Netw. 2016(2), 1–8 (2016)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (Grants No. 61673055 and No. 61773056).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, F., Chen, S., Jiang, C., Xiao, W. (2019). Mixed Iterative Adaptive Dynamic Programming Based Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_44
Download citation
DOI: https://doi.org/10.1007/978-981-13-7986-4_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7985-7
Online ISBN: 978-981-13-7986-4
eBook Packages: Computer ScienceComputer Science (R0)