Abstract
Wireless rechargeable sensor networks (WRSNs) are broadly utilized in numerous areas. However, the limited battery capacity of sensor nodes (SNs) is considered as a critical issue. To extend the battery life of SNs, mobile chargers (MCs) equipped with wireless power transfer (WPT) technology have been proposed as a key solution for charging SNs. Using directional antennas to focus energy within a specific area, as opposed to an omnidirectional antenna, increases the energy efficiency of an MC. In this paper, we focus on the travel path charging scheduling problem with a directional MC in on-demand WRSNs. Our goals are to develop a mechanism to reduce the changing delay time and boost the energy efficiency of MC. In this case, the MC receives the charging requests of SNs and responds to them by selecting appropriate stopping points (SPs) and the charging orientation angles in each SP. We propose a mobile directional charging scheduling (MDCS) solution based on a deep reinforcement learning technique. The simulation results demonstrate the superior performance of our method to existing studies in terms of the energy consumption of the MC, the number of dead SNs, and charging delay time.
Similar content being viewed by others
References
Rajasekaran, M., Yassine, A., Hossain, M. S., Alhamid, M. F., & Guizani, M. (2019). Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes. Future Generation Computer Systems, 98, 565–576.
Sumi, F., Dutta, L., & Sarker, F. (2018). Future with Wireless Power Transfer Technology. J Electr Electron Syst, 7(279), 2332–2796.
Tashtarian, F., Sohraby, K., & Varasteh, A. (2017). Multihop data gathering in wireless sensor networks with a mobile sink. International Journal of Communication Systems, 30(12), e3264.
Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2015). Wireless charging technologies: Fundamental standards and network applications. IEEE Communications Surveys & Tutorials, 18(2), 1413–1452.
Engmann, F., Katsriku, F. A., Abdulai, J.-D., Adu-Manu, K. S., & Banaseka, F. K. (2018). Prolonging the lifetime of wireless sensor networks: a review of current techniques. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2018/8035065
Kurs, A., Karalis, A., Moffatt, R., Joannopoulos, J. D., Fisher, P., & Soljačić, M. (2007). Wireless power transfer via strongly coupled magnetic resonances. Science, 317(5834), 83–86.
Peng, Y., Li, Z., Zhang, W., & Qiao, D. (2010). Prolonging sensor network lifetime through wireless charging. In 2010 31st IEEE real-time systems Symposium, (pp. 129–139).
Xie, L., Shi, Y., Hou, Y. T., & Lou, A. (2013). Wireless power transfer and applications to sensor networks. IEEE Wireless Communications, 20(4), 140–145.
Xie, L., Shi, Y., Hou, Y. T., & Sherali, H. D. (2012). Making sensor networks immortal: An energy-renewal approach with wireless power transfer. IEEE/ACM Transactions on Networking, 20(6), 1748–1761.
Kaswan, A., Tomar, A., & Jana, P. K. (2018). An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks. Journal of Network and Computer Applications, 114, 123–134.
Ding, Z., Zhong, C., Ng, D. W. K., Peng, M., Suraweera, H. A., Schober, R., & Poor, H. V. (2015). Application of smart antenna technologies in simultaneous wireless information and power transfer. IEEE Communications Magazine, 53(4), 86–93.
Xu, X., Chen, L., & Cheng, Z. (2019). Optimizing charging efficiency and maintaining sensor network perpetually in mobile directional charging. Sensors, 19(12), 2657.
Lin, C., Zhou, Y., Ma, F., Deng, J., Wang, L., and Wu, G. ( 2019). Minimizing charging delay for directional charging in wireless rechargeable sensor networks. In IEEE INFOCOM Conference on computer communications, (pp. 1819–1827).
Wang, X., Dai, H., Huang, H., Liu, Y., Chen, G., and Dou, W. (2019). Robust scheduling for wireless charger networks. In IEEE INFOCOM Conference on computer communications.(pp. 2323–2331).
Ding, X., Wang, Y., Sun, G., Luo, C., Li, D., Chen, W., & Hu, Q. (2020). Optimal charger placement for wireless power transfer. Computer Networks, 170, 107123.
Yu, N., Dai, H., Liu, A. X., and Tian, B. (2018). Placement of connected wireless chargers. In IEEE INFOCOM 2018-IEEE Conference on computer communications. (pp. 387–395).
Lin, C., Yang, Z., Dai, H., Cui, L., Wang, L., & Wu, G. (2021). Minimizing charging delay for directional charging. IEEE/ACM Transactions on Networking., 29(6), 2478–2493.
Nowrozian, N., & Tashtarian, F. (2021). A mobile charger based on wireless power transfer technologies: a survey of concepts, techniques, challenges, and applications on rechargeable wireless sensor networks. Journal of AI and Data Mining, 9(3), 383–402.
Kaswan, A., Jana, P. K., & Das, S. K. (2022). A survey on mobile charging techniques in wireless rechargeable sensor networks. IEEE Communications Surveys & Tutorials, 24(3), 1750–1779.
He, L., Zhuang, Y., Pan, J., and Xu, J. (2010). Evaluating on-demand data collection with mobile elements in wireless sensor networks. In 2010 IEEE 72nd Vehicular Technology Conference-Fall, (pp. 1–5).
He, L., Kong, L., Gu, Y., Pan, J., & Zhu, T. (2014). Evaluating the on-demand mobile charging in wireless sensor networks. IEEE Transactions on Mobile Computing, 14(9), 1861–1875.
Ye, X., & Liang, W. (2017). Charging utility maximization in wireless rechargeable sensor networks. Wireless Networks, 23(7), 2069–2081.
Ma, Y., Liang, W., & Xu, W. (2018). Charging utility maximization in wireless rechargeable sensor networks by charging multiple sensors simultaneously. IEEE/ACM Transactions on Networking, 26(4), 1591–1604.
Huang, H., Lin, S., Chen, L., Gao, J., Mamat, A., & Wu, J. (2015). Dynamic mobile charger scheduling in heterogeneous wireless sensor networks. In 2015 IEEE 12th International Conference on mobile ad hoc and sensor systems, (pp. 379–387).
Lyu, Z., Wei, Z., Pan, J., Chen, H., Xia, C., Han, J., & Shi, L. (2019). Periodic charging planning for a mobile WCE in wireless rechargeable sensor networks based on hybrid PSO and GA algorithm. Applied Soft Computing, 75, 388–403.
Tomar, A., & Jana, P. K. (2019). Mobile charging of wireless sensor networks for internet of things: a multi-attribute decision making approach. In International Conference on Distributed Computing and Internet Technology, (pp. 309–324). Cham: Springer.
Cormen, T. H. (2001). Introduction to algorithms. The MIT Press.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making (pp. 58–191). Springer.
Cao, X., Xu, W., Liu, X., Peng, J., & Liu, T. (2021). A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks. Ad Hoc Networks, 110, 102278.
Wei, Z., Liu, F., Lyu, Z., Ding, X., Shi, L., & Xia, C. (2018). Reinforcement learning for a novel mobile charging strategy in wireless rechargeable sensor networks. In International Conference on Wireless Algorithms, Systems, and Applications (pp. 485–496). Cham: Springer
Wei, Z., Li, M., Wei, Z., Cheng, L., Lyu, Z., & Liu, F. (2020). A novel on-demand charging strategy based on swarm reinforcement learning in WRSNs. IEEE Access, 8, 84258–84271.
Le Nguyen, P., Nguyen, T. H., & Nguyen, K. (2020). Qlearning-based, optimized on-demand charging algorithm in WRSN. In 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). (pp. 1–8)
Nguyen, P. L., La, V. Q., Nguyen, A. D., Nguyen, T. H., & Nguyen, K. (2021). An on-demand charging for connected target coverage in WRSNs using fuzzy logic and Q-Learning. Sensors, 21(16), 5520.
Banoth, S. P. R., Donta, P. K., & Amgoth, T. (2021). Dynamic mobile charger scheduling with partial charging strategy for WSNs using deep-Qnetworks. Neural Computing and Applications, 33(22), 15267–15279.
Lee, C., Na, W., Jang, G., Lee, C., & Cho, S. (2020). Energy-efficient and delay-minimizing charging method with a multiple directional mobile charger. IEEE Internet of Things Journal, 8(10), 8291–8303.
Riccardo Bonetto, V.L., (2020) Chapter 8–Machine learning. In Computing in Communication Networks.
Zeng, X. (2019). Reinforcement learning based approach for the navigation of a pipe-inspection robot at sharp pipe corners. Master’s thesis, University of Twente.
He, S., Chen, J., Jiang, F., Yau, D. K., Xing, G., & Sun, Y. (2012). Energy provisioning in wireless rechargeable sensor networks. IEEE Transactions on Mobile Computing, 12(10), 1931–1942.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Nowrozian, N., Tashtarian, F. & Forghani, Y. On optimizing the charging trajectory of mobile chargers in wireless sensor networks: a deep reinforcement learning approach. Wireless Netw 30, 421–436 (2024). https://doi.org/10.1007/s11276-023-03384-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-023-03384-8