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Dynamic mobile charger scheduling with partial charging strategy for WSNs using deep-Q-networks

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Abstract

Wireless sensor networks are a group of spatially distributed nodes deployed to sense, gather, and transmit data to the sink for further analytics. Due to continuous operations, the battery-equipped sensor nodes (SNs) drain energy rapidly, and replacing them is a hectic task. Wireless energy transfer (WET) is evolved as a promising innovation to recharge the SNs battery wirelessly to address the challenges. A WET is embedded in a vehicle called a mobile charger (MC) and traveled in the network to recharge the SNs. However, scheduling the mobile charger over the network before a sensor node dies is challenging. In this work, we introduced a partial charging strategy to avoid the long waiting time for MC because full recharging of a single node takes a long time. The partial charging strategy preempts the current charging node and moves to the newly requested node to minimize the network’s dead nodes. However, it will increase the traveling distance. Hence, adequate charging time and MC traveling path are required. In this context, this paper proposes a deep reinforcement learning-based mobile charger scheduling strategy called dynamic partial mobile charger scheduling using deep-Q-networks (DPMCS). The proposed DPMCS learns from the environment and decides each sensor’s charging duration in an identified tour. Experimental results reveal that the proposed DPMCS outperforms well compared to the existing studies, enhance the lifetime and diminish the dead nodes count.

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References

  1. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422

    Article  Google Scholar 

  2. Arivudainambi D, Balaji S (2018) Optimal placement of wireless chargers in rechargeable sensor networks. IEEE Sens J 18(10):4212–4222

    Article  Google Scholar 

  3. Aslam N, Xia K, Hadi MU (2019) Optimal wireless charging inclusive of intellectual routing based on SARSA learning in renewable wireless sensor networks. IEEE Sens J 19(18):8340–8351

    Article  Google Scholar 

  4. Baroudi U (2017) Robot-assisted maintenance of wireless sensor networks using wireless energy transfer. IEEE Sens J 17(14):4661–4671

    Article  Google Scholar 

  5. Dai H, Liu Y, Chen G, Wu X, He T, Liu AX, Ma H (2017) Safe charging for wireless power transfer. IEEE/ACM Trans Netw 25(6):3531–3544

    Article  Google Scholar 

  6. Donta PK, Rao BSP, Amgoth T, Annavarapu CSR, Swain S (2020) Data collection and path determination strategies for mobile sink in 3D WSNs. IEEE Sens J 20(4):2224–2233

    Article  Google Scholar 

  7. Ejaz W, Naeem M, Basharat M, Anpalagan A, Kandeepan S (2016) Efficient wireless power transfer in software-defined wireless sensor networks. IEEE Sens J 16(20):7409–7420

    Article  Google Scholar 

  8. Feng Y, Liu N, Wang F, Qian Q, Fu X (2017) A framework of mobile energy replenishment for wireless sensor and actuator networks. In: GLOBECOM 2017-2017 IEEE global communications conference, IEEE, pp 1–6

  9. Han G, Guan H, Wu J, Chan S, Shu L, Zhang W (2018) An uneven cluster-based mobile charging algorithm for wireless rechargeable sensor networks. IEEE Syst J 13(4):3747–3758

    Article  Google Scholar 

  10. Li J, Mohapatra P (2007) Analytical modeling and mitigation techniques for the energy hole problem in sensor networks. Pervasive Mob Comput 3(3):233–254

    Article  Google Scholar 

  11. Liang W, Xu Z, Xu W, Shi J, Mao G, Das SK (2017) Approximation algorithms for charging reward maximization in rechargeable sensor networks via a mobile charger. IEEE/ACM Trans Netw 25(5):3161–3174

    Article  Google Scholar 

  12. Lin C, Zhou J, Guo C, Song H, Wu G, Obaidat MS (2017a) Tsca: a temporal-spatial real-time charging scheduling algorithm for on-demand architecture in wireless rechargeable sensor networks. IEEE Trans Mob Comput 17(1):211–224

    Article  Google Scholar 

  13. Lin C, Zhou Y, Song H, Yu CW, Wu G (2017b) Oppc: an optimal path planning charging scheme based on schedulability evaluation for wrsns. ACM Trans Embedded Comput Syst (TECS) 17(1):1–25

    Google Scholar 

  14. Lin C, Wei S, Deng J, Obaidat MS, Song H, Wang L, Wu G (2018) Gtccs: a game theoretical collaborative charging scheduling for on-demand charging architecture. IEEE Trans Veh Technol 67(12):12124–12136

    Article  Google Scholar 

  15. Liu T, Wu B, Wu H, Peng J (2017) Low-cost collaborative mobile charging for large-scale wireless sensor networks. IEEE Trans Mob Comput 16(8):2213–2227. https://doi.org/10.1109/TMC.2016.2616309

    Article  Google Scholar 

  16. Lu X, Wang P, Niyato D, Kim DI, Han Z (2016) Wireless charging technologies: fundamentals, standards, and network applications. IEEE Commun Surv Tutor 18(2):1413–1452

    Article  Google Scholar 

  17. Luong NC, Hoang DT, Gong S, Niyato D, Wang P, Liang YC, Kim DI (2019) Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun Surv Tutor 21(4):3133–3174

    Article  Google Scholar 

  18. Ma Y, Liang W, Xu W (2018) Charging utility maximization in wireless rechargeable sensor networks by charging multiple sensors simultaneously. IEEE/ACM Trans Netw 26(4):1591–1604

    Article  Google Scholar 

  19. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533

    Article  Google Scholar 

  20. Na W, Park J, Lee C, Park K, Kim J, Cho S (2017) Energy-efficient mobile charging for wireless power transfer in internet of things networks. IEEE Internet of Things J 5(1):79–92

    Google Scholar 

  21. Prasannababu D, Vaishnav S, Amgoth T (2019) Mobile charger scheduling using partial charging strategy for rechargeable wsns. In: 2019 international conference on computing, power and communication technologies (GUCON), IEEE, pp 845–852

  22. Praveen Kumar D, Tarachand A, Rao ACS (2019) Machine learning algorithms for wireless sensor networks: a survey. Inf Fusion 49:1–25

    Article  Google Scholar 

  23. Rao BSP, Singh D, Amgoth T (2019) Joint wireless charging and data collection using mobile element for rechargeable wsns. In: 2019 international conference on computing. power and communication technologies (GUCON), IEEE, pp 837–844

  24. Sangare F, Xiao Y, Niyato D, Han Z (2017) Mobile charging in wireless-powered sensor networks: optimal scheduling and experimental implementation. IEEE Trans Veh Technol 66(8):7400–7410

    Article  Google Scholar 

  25. Shi Y, Xie L, Hou YT, Sherali HD (2011) On renewable sensor networks with wireless energy transfer. In: 2011 Proceedings IEEE INFOCOM, IEEE, pp 1350–1358

  26. Shu Y, Shin KG, Chen J, Sun Y (2016) Joint energy replenishment and operation scheduling in wireless rechargeable sensor networks. IEEE Trans Ind Inform 13(1):125–134

    Article  Google Scholar 

  27. Wang C, Li J, Ye F, Yang Y (2014) Netwrap: an ndn based real-timewireless recharging framework for wireless sensor networks. IEEE Trans Mob Comput 13(6):1283–1297

    Article  Google Scholar 

  28. Wang Q, Kong F, Wang M, Wang H (2017) Optimized charging scheduling with single mobile charger for wireless rechargeable sensor networks. Symmetry 9(11):285

    Article  Google Scholar 

  29. Xie L, Shi Y, Hou YT, Lou W, Sherali HD, Zhou H, Midkiff SF (2015) A mobile platform for wireless charging and data collection in sensor networks. IEEE J Sel Areas Commun 33(8):1521–1533

    Google Scholar 

  30. Xu W, Liang W, Jia X, Xu Z, Li Z, Liu Y (2018) Maximizing sensor lifetime with the minimal service cost of a mobile charger in wireless sensor networks. IEEE Trans Mob Comput 17(11):2564–2577

    Article  Google Scholar 

  31. Zhu J, Feng Y, Liu M, Zhang Z, Ma C (2017) Node failure avoidance mobile charging in wireless rechargeable sensor networks. In: GLOBECOM 2017-2017 IEEE global communications conference, IEEE, pp 1–6

  32. Zhu X, Li J, Zhou M (2019) Target coverage-oriented deployment of rechargeable directional sensor networks with a mobile charger. IEEE Internet of Things J 6(3):5196–5208

    Article  Google Scholar 

Download references

Acknowledgements

We appreciate the time and efforts made by the editor during reviewing this manuscript. We pay our sincere thanks to the esteemed reviewers for their valuable comments and suggestions to improve the quality of this paper.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826 004, India

    Sanjai Prasada Rao Banoth, Praveen Kumar Donta & Tarachand Amgoth

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  1. Sanjai Prasada Rao Banoth
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  2. Praveen Kumar Donta
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  3. Tarachand Amgoth
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Correspondence to Tarachand Amgoth.

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Banoth, S.P.R., Donta, P.K. & Amgoth, T. Dynamic mobile charger scheduling with partial charging strategy for WSNs using deep-Q-networks. Neural Comput & Applic 33, 15267–15279 (2021). https://doi.org/10.1007/s00521-021-06146-9

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  • DOI: https://doi.org/10.1007/s00521-021-06146-9

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