Modelled Energy Cost Minimization Solution for Wireless Rechargeable Sensor Networks
Article Sidebar
Main Article Content
Abstract
In wireless rechargeable sensor networks (WRSNs), mobile chargers (MCs) are normally scheduled to deliver energy to the rechargeable sensor nodes (SNs). However, due to the energy consumption dynamicity of WRSNs, constructing optimal charging trajectories with minimized number of failed SNs due to energy deficiency ensuring a sustained WRSN operation at minimum MC’s movement cost is one aspect of the subject matter not yet thoroughly investigated. Thus, exploring this knowledge is the focus of this work. We applied shortest path algorithm, on-demand scheduling and multi-node charging methods to construct the energy cost-effective charging path for the MC, a model we coined as Shortest Hamiltonian Cycle Traveling Salesman Problem (SHC-TSP). Comparative analysis proves the optimality of our solution against the notable nearest job next with pre-emption (NJNP) model in terms of minimizing MC’s traveling energy cost with energy savings of 3.9156% and 2.1940% for the two scenarios respectively examined.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Andhare, M. S., Pal, T. L., Jayaram, V., Sreelekshmy, P. G., Tripath, V. & Krishnaraj, M. (2022). Design and implementation of wireless sensor networks for environmental monitoring, International Journal of Health Sciences, 6(4), 3158–3169. https://doi.org/10.53730/ijhs.v6nS4.9085.
Abdelraouf, O., et al. (2021). DZ50: energy efficient wireless sensor motes, platform for low data rate applications, 37(14), 189 – 194. https://doi.org/j.procs.2014.08.029.
Duobiene, S., Ratautas, K., Trusovas, R., Ragulis, P., Slekas, G., Simniskis, R. & Raciukaitis, G. (2022). Development of Wireless Sensor Network for Environment Monitoring and its Implementation Using SSAIL Technology, MDPI Sensors, 22(5343), 1-17. https://doi.org/10.3390/s22145343.
Ouyang, W., et al. (2021). Utility-aware charging scheduling for multiple mobile chargers in large-scale wireless rechargeable sensor networks, IEEE Trans. Sustain. Comput., 8(4), 679–690. https://doi.org/TSUSC.2020.3003014.
Sha, C., Song, D. & Malekian, R. (2021). A periodic and distributed energy supplement method based on maximum recharging benefit in sensor networks, IEEE Internet Things J., 8(4), 2649–2669. https://doi.org/10.1109/JIOT.2020.3020134.
Kaswan, A., Prasanta, K. J. & Sajad, K. D. (2022). A survey on mobile charging techniques in wireless rechargeable sensor networks, IEEE Communications survey & tutorials, 24(3), 1750-1779. https://doi.org/10.1109/COMST.2022.3189387.
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. https://doi.org/10.1109/JSYST.2020.2968968.
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.
Wang, R., Xu, X., Ran, X., Liu, Y. & Xue, L. (2021). Minimum nodes deployment for mixed energy replenishment in rechargeable WSNs, IEEE Sensors J., 21(14), 16282–16290.
Zhao, C., Zhang, H., Chen, F., Chen, S., Wu, C. & Wang, T. (2020). Spatiotemporal charging scheduling in wireless rechargeable sensor networks, Comput. Commun., 152, 155–170. https://doi.org/10.1016/j.comcom.2020.01.037.
Liu, T., Wu, B., Zhang, S., Peng, J. & Xu, W. (2020). An effective multi-node charging scheme for wireless rechargeable sensor networks, in Proc. IEEE INFOCOM Conf. Comput. Commun., 2026–2035. https://doi.org/10.1109/INFOCOM41043.2020.9155262.
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.
Tian, M., Jiao, W. & Chen, Y. (2021). A joint energy replenishment and data collection strategy in heterogeneous wireless rechargeable sensor networks, Sensors, 21(9), 2930. https://doi.org/10.3390/s21092930.
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. https://doi.org/10.3390/sym11081028.
Lyu, Z., Wei, Z., Lu, Y., Wang, X., Li, M., Xia, C. & Han, J. (2019). Multi-node charging planning algorithm with an energy-limited WCE in WRSNs, IEEE ACCESS, 7, 47154-47170. https://doi.org/10.1109/ACCESS.2019.2909778.
Tian, M., Jiao, W., Liu, J. & Ma, S. (2019). A charging algorithm for the wireless rechargeable sensor network with imperfect charging channel and finite energy storage, MDPI Sensors, 1-19. https://doi.org/10.3390/s19183887.
Fu, X., Cheng, Z. & Wang, J. (2021). Research on online scheduling and charging strategy of robots based on shortest path algorithm, Journal of computer and industrial engineering, ELSEVIER Ltd, 153. 1-9, https://doi.org/10.1016/j.cie.2021.107097.
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. https://doi.org/10.4218/etrij.2018-0126.
Kaswan, A., Tomar, A. & Jana, P. K. (2018). An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks, J. Netw. Comput. Appl., 114, 123–134. https://doi.org/10.1016/j.jnca.2018.02.017.
Fan, Z., Jie, Z. & Yujie, Q. (2018). A Survey on Wireless Power Transfer based charging Scheduling Schemes in Wireless Rechargeable Sensor Networks, IEEE 4th International Conference on Control Science and Systems Engineering,194-198. https://doi.10.1109/CCSSE.2018.8724809.
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. https://doi.org/10.1109/TGCN.2020.3039338.
Kumar, R. & Mukherjee, J. C. (2021). On-demand vehicle-assisted charging in wireless rechargeable sensor networks, Ad Hoc Netw., 112(102389). https://doi.org/10.1016/j.adhoc.2020.102389.
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 Netw., 110(102278). https://doi.org/10.1016/j.adhoc.2020.102278.
Jia, R., Wu, J., Wang, X., Lu, J., Lin, F., Zheng, Z. & Li, M. (2023). Energy Cost Minimization in Wireless Rechargeable Sensor Networks, IEEE/ACM Transactions on networking, 31(5), 2345-2360. https://doi.org/10.1109/TNET.2023.3248088.
Xu, W., Liang, W., Jia, X., Kan, H., Xu, Y. & Zhang, X. (2021). Minimizing the maximum charging delay of multiple mobile chargers under the multi-node energy charging scheme, IEEE Trans. Mobile Comput., 20(5), 1846–1861. https://doi.org/10.1109/TMC.2020.2973979.
Azeem, M., Jamil, M. K. & Shang, Y. (2023). Notes on the Localization of Generalized Hexagonal Cellular Networks, MDP Sensors, 11(844), 1-15. https://doi.org/103390/math11040844.