An Overview of On-Demand Wireless Charging as a Promising Energy Replenishment Solution for Future Wireless Rechargeable Sensor Networks | ||||
| Journal of Computing and Communication | ||||
| Article 1, Volume 3, Issue 2 - Serial Number 1, July 2024, Page 1-9 PDF (755.26 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2024.380111 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Musa Ahmed  1; Dominic S. Nyitamen2
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| 1Department of Electrical and Electronics Engineering, College of Engineering, Kaduna Polytechnic, Kaduna, Kaduna State, Nigeria | ||||
| 2Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Nigerian Defence Academy, Kaduna, Kaduna State, Nigeria | ||||
| Abstract | ||||
| The rapid advancement in the emerging technology of wireless power transfer (WPT) has enabled energy-constrained wireless sensor networks (WSNs) to operate perpetually through the mobile charging robot scheduled to recharge the sensors' batteries. In contrast to previous schemes where the mobile charger (MC) is scheduled to periodically visit and recharge every sensor node (SN) in the network irrespective of their energy status, the current trend is using a more efficient recharging scheme called on-demand. In the on-demand recharging scheme, the MC is scheduled to visit and recharge only a few SNs that have forwarded a recharging request after their battery energies lessen below a preset threshold. However, due to the energy consumption dynamicity of WSNs, designing an on-demand wireless recharging scheme is still a challenging research problem. This article explores some of the recent design issues of on-demand wireless recharging scheme and corresponding performance evaluation metrics. Although recently, researchers have proposed many efficient on-demand recharging schemes, there are still some limitations, such as scalability, high MCs' energy consumption, and prolonged SNs' recharging delay, which, if not adequately addressed through research, may limit the network's performance efficiency as well as lifetime. | ||||
| Keywords | ||||
| Energy replenishment Mobile charger On; demand wireless recharging scheme Sensor node Wireless power transfer Wireless sensor network | ||||
| References | ||||
|
[1] Kaswan, A., Prasanta, K. J., & Sajad, K. D. (2021). A survey on mobile charging techniques in wireless rechargeable sensor networks. IEEE Communications survey & tutorials, 24(3), 1750-1779.
[2] Liu, X., Obaidat, M. S., Lin, C., Wang, T., & Liu, A. (2021). Movement-based solutions to energy limitation in wireless sensor networks: State of the art and future trends. IEEE Netw., 35(2), 188–193.
[3] Boukerche, A., Wu, Q., & Sun, P. (2022). A novel two-mode QoS-aware mobile charger scheduling method for achieving sustainable wireless sensor networks. IEEE Trans. Sustain. Comput., 7(1), 14–26.
[4] Mukase, S., Xia, K., Umar, A., & Owoola, E. O. (2022). On‐Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks. MDPI Sensors, 22(384), 1-17. https://doi.org/10.3390/s22010384
[5] Tomar, A., Muduli, L., & Jana, P. K. (2021). A fuzzy logic-based on-demand charging algorithm for wireless rechargeable sensor networks with multiple chargers. IEEE Trans. Mobile Comput., 20(9), 2715–2727.
[6] Zhou, P., Wang, C., & Yang, Y. (2021). Design of self-sustainable wireless sensor networks with energy harvesting and wireless charging. ACM Trans. Sensor Netw., 17(4), 1–38.
[7] Wang, Y., Wang, F., Liu, Y., & Zhao, C. (2021). Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm. EURASIP journal on wireless communication and networking, https://doi.org/10.1186/s13638-021-01951-1
[8] Sandhu, M. M., Khalifa, S., Jurdak, R., & Portmann, M. (2021). Task scheduling for energy harvesting-based IoT: A survey and critical analysis. IEEE Internet of Things J., 8(18), 13825–13848.
[9] Sun, Y., Lin, C., Dai, H., Lin, Q., Wang, L., & Wu, G. (2022). Trading off charging and sensing for stochastic events monitoring in WRSNs. IEEE/ACM Trans. Netw., 30(2), 557–571.
[10] Gharaei, N., Al-Otaibi, Y. D., Butt, S. A., Malebary, S. J., Rahim, S., & Sahar, G. (2021). Energy-efficient tour optimization of wireless mobile chargers for rechargeable sensor networks. IEEE Syst. J., 15(1), 27–36.
[11] Tsoumanis, G., Oikonomou, K., Aïssa, S., & Stavrakakis, I. (2021). Energy and distance optimization in rechargeable wireless sensor networks. IEEE Trans. Green Commun. Netw., 5 (1), 378–391.
[12] He, L., Kong, L., Gu, Y., Pan, J., & Zhu, T. (2015). Evaluating the on-demand mobile charging in wireless sensor networks. IEEE Trans. Mobile Comput., 14(9), 1861–1875.
[13] Liu, Y. et al. (2022). Joint scheduling and trajectory optimization of charging UAV in wireless rechargeable sensor networks. IEEE Internet of Things J., 9(14), 11796–11813.
[14] Chen, L., Lin, S., Huang, H., & Yang, W. (2022). Charging path optimization in mobile networks, IEEE/ACM Trans. Netw., early access 26, doi: 10.1109/TNET.2022.3167781.
[15] Rohit, K., & Joy, C. M. (2020). Charge Scheduling in wireless rechargeable sensor network using mobile charging vehicle. In: 12th international conference on communication and networking (COMSNETS), IEEE Explore, 373-382.
[16] Kumar, R., & Mukherjee, J. C. (2021). On-demand vehicle-assisted charging in wireless rechargeable sensor networks. Ad Hoc Netw., 112(102389).
[17] Wang, Y., Dong, Y., Li, S., Huang, R., & Shang, Y. (2019). A new on-demand recharging strategy based on cycle-limitation in a WRSN. Symmetry, 11(8), 1028.
[18] Tomar, A., Muduli, L., & Jana, P. K. (2021). A fuzzy logic-based on-demand charging algorithm for wireless rechargeable sensor networks with multiple chargers. IEEE Trans. Mobile Comput., 20(9), 2715–2727.
[19] Lin, C., Wang, Z., Deng, J., Wang, L., Ren, J., & Wu, G. (2018, April). MTS: Temporal and spatial collaborative charging for wireless rechargeable sensor networks with multiple vehicles. In: Proceedings of 37th IEEE Conference on Computer Communication (INFOCOM), 99–107.
[20] Tsoumanis, G., Oikonomou, K., Aïssa, S., & Stavrakakis, I. (2021). Energy, distance optimization in rechargeable wireless sensor networks. IEEE Trans. Green Commun. Netw.,5(1), 378–391.
[21] Liu, T., Wu, B., Xu, W., Cao, X., Peng, J., & Wu, H. (2021). RLC: A reinforcement learning-based charging algorithm for mobile devices. ACM Trans. Sensor Netw., 17(4), 1–23.
[22] Kaswan, A., Tomar, A., & Jana, P. K. (2018). A GSA-based scheduling scheme for mobile charger in on-demand wireless rechargeable sensor network. Journal of network and computer applications (JNCA), 10(18), 1-26, doi: 10.1016/j.jnca.2018.02.017.
[23] Gharaei, N., Al-Otaibi, Y. D., Butt, S. A., Malebary, S. J., Rahim, S., & Sahar, G. (2021). Energy-efficient tour optimization of wireless mobile chargers for rechargeable sensor networks. IEEE Syst. J., 15(1), 27–36.
[24] Dong, Y., Wang, Y., Li, S., Cui, M. & Wu, H. (2019). Demand-based charging strategy for wireless rechargeable sensor networks. ETRI Journal, 1(1), 326–336.
[25] Xing, Y., Young, R., Nguyen, G., Lefebvre, M., Zhao, T., Pan, H., & Dong, L. (2022). Optimal path planning for wireless power transfer robot using area division deep reinforcement learning. Wireless power transfer Cambridge university press, 1-10, doi: 10.1155/2022/9921885.
[26] Ma, C., An, S., Wang, W., Lin, D., Li, M., & Sun, L. (2020). Wireless sensor network charging strategy based on modified ant colony algorithm. International journal of materials, mechanics and manufacturing, 8(3), 155-161.
[27] Han, G., Liao, Z., Martínez-García, M., Zhang, Y., & Peng, Y. (2021). Dynamic collaborative charging algorithm for mobile and static nodes in Industrial Internet of Things. IEEE Internet of Things J., 8(24), 17747–17761.
[28] Huang, J., Zhou, Y., Ning, Z., & Gharavi, H. (2019). Wireless power transfer and energy harvesting: Current status and future prospects. IEEE Wireless Commun., 26(4), 163–169.
[29] Bui, N., Nguyen, P., Nguyen, V. A., & Do, P. T. (2022). A deep reinforcement learning based adaptive charging policy for WRSNs. 1-9, doi: 2208.07824v1.
[30] Hazra, A., Adhikari, M., Amgoth, T., & Srirama, S. N. (2021). A comprehensive survey on interoperability for IoT: Taxonomy, standards, and future directions. ACM Comput. Surveys, 55(1), 1–35. | ||||
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