Optimizing Energy Resources in WSNs: ARIMA Feature Selection Meets Adaptive Reinforcement Learning

References

• F. Mazunga and A. Nechibvute, “Ultra-low power techniques in energy harvesting wireless sensor networks: recent advances and issues,” Sci. Afr, vol. 11, pp. e00720, 2021. DOI: 10.1016/j.sciaf.2021.e00720.
   Google Scholar
• R. Chéour et al. “Towards hybrid energy-efficient power management in wireless sensor networks.” Sensors, vol. 22, no. 1, pp. 301, 2021. DOI: 10.3390/s22010301.
   PubMed Web of Science ®Google Scholar
• D. Van Huynh et al. “Real-time optimized path planning and energy consumption for data collection in unmanned ariel vehicles-aided intelligent wireless sensing.” IEEE Trans. Ind. Informat., vol. 18, no. 4, pp. 2753–2761, 2021. DOI: 10.1109/TII.2021.3114358.
   Web of Science ®Google Scholar
• D. Zhai, C. Wang, R. Zhang, H. Cao, and F. R. Yu, “Energy-saving deployment optimization and resource management for UAV-assisted wireless sensor networks with NOMA.” IEEE Trans. Veh. Technol., vol. 71, no. 6, pp. 6609–6623, 2022. DOI: 10.1109/TVT.2022.3159681.
   Web of Science ®Google Scholar
• F. A. Almalki et al. “Green IoT for eco-friendly and sustainable smart cities: future directions and opportunities.” Mobile Netw. Appl., vol. 28, no. 1, pp. 178–202, 2023. DOI: 10.1007/s11036-021-01790-w.
   Web of Science ®Google Scholar
• J. N. Chen et al. “An efficient aggregate signature scheme for healthcare wireless sensor networks.” J. Netw. Intell, vol. 6, pp. 1–15, 2021.
   Google Scholar
• O. Singh, V. Rishiwal, R. Chaudhry, and M. Yadav, “Multi-objective optimization in WSN: opportunities and challenges,” Wirel. Pers. Commun., vol. 121, no. 1, pp.127–152, 2021. DOI: 10.1007/s11277-021-08627-5.
   Web of Science ®Google Scholar
• D. J. Bahadur and L. Lakshmanan, “A novel method for optimizing energy consumption in wireless sensor network using genetic algorithm,” Microprocess. Microsyst, vol. 96, pp. 104749, 2023. DOI: 10.1016/j.micpro.2022.104749.
   Web of Science ®Google Scholar
• A. Choudhary, S. Kumar, M. Sharma, and K. P. Sharma, “A framework for data prediction and forecasting in WSN with Auto ARIMA,” Wirel. Pers. Commun., vol. 125, no. 1, pp.1–15, 2022. DOI: 10.1007/s11277-022-09538-9.
   Google Scholar
• C. Jiang et al. “Attention-shared multi-agent actor–critic-based deep reinforcement learning approach for mobile charging dynamic scheduling in wireless rechargeable sensor networks.” Entropy, vol. 24, no. 7, pp. 965, 2022. DOI: 10.3390/e24070965.
   PubMed Web of Science ®Google Scholar
• D. Devassy, J. Immanuel Johnraja, and G. J. L. Paulraj, “NBA: Novel bio-inspired algorithm for energy optimization in WSN for IoT applications.” J. Supercomput, vol. 78, no. 14, pp. 16118–16135, 2022. DOI: 10.1007/s11227-022-04505-4.
   Web of Science ®Google Scholar
• V. Narayan, A. K. Daniel, and P. Chaturvedi, “E-FEERP: Enhanced Fuzzy based Energy Efficient Routing Protocol for wireless sensor network.” Wirel. Pers. Commun., pp. 1–28, 2023.
   Google Scholar
• H. Y. Huang, K. T. Kim, and H. Y. Youn, “Determining node duty cycle using Q-learning and linear regression for WSN,” Front. Comput. Sci., vol. 15, no. 1, pp.1–7, 2021. DOI: 10.1007/s11704-020-9153-6.
   Web of Science ®Google Scholar
• M. Mittal, R. P. De Prado, Y. Kawai, S. Nakajima, and J. E. Muñoz-Expósito, “Machine learning techniques for energy efficiency and anomaly detection in hybrid wireless sensor networks.” Energies, vol. 14, no. 11, pp. 3125, 2021. DOI: 10.3390/en14113125.
   Web of Science ®Google Scholar
• G. Santhosh and K. V. Prasad, “Energy optimization routing for hierarchical cluster based WSN using artificial bee colony,” Meas.: Sens, vol. 29, pp. 100848, 2023. DOI: 10.1016/j.measen.2023.100848.
   Google Scholar
• G. Sudha and C. Tharini, “Trust-based clustering and best route selection strategy for energy efficient wireless sensor networks.” Automatika, vol. 64, no. 3, pp. 634–641, 2023. DOI: 10.1080/00051144.2023.2208462.
   Web of Science ®Google Scholar
• A. Lipare, D. R. Edla, and S. R. Parne, “Fuzzy rule-based system for energy efficiency in wireless sensor networks.” J. Supercomput, vol. 77, no. 9, pp. 9947–9970, 2021. DOI: 10.1007/s11227-021-03668-w.
   Web of Science ®Google Scholar
• V. M. Kuthadi, R. Selvaraj, S. Baskar, P. M. Shakeel, and A. Ranjan, “Optimized energy management model on data distributing framework of wireless sensor network in IoT system.” Wirel. Pers. Commun., vol. 127, no. 2, pp. 1377–1403, 2022. DOI: 10.1007/s11277-021-08583-0.
   Web of Science ®Google Scholar
• K. R. Madhavi et al. “Energy efficient target tracking in wireless sensor network using PF-SVM (particle filter-support vector machine) technique,” Meas.: Sens., vol. 26, pp. 1006, 2023. DOI: 10.1016/j.measen.2023.100667.
   Google Scholar
• O. J. Aroba, N. Naicker, T. Adeliyi, and C. Marques, “An innovative hyperheuristic, Gaussian clustering scheme for energy-efficient optimization in wireless sensor networks,” J. Sens, vol. 2021, pp. 1–12, 2021. DOI: 10.1155/2021/6666742.
   Google Scholar
• S. Madden, Intel Lab Data. Berkeley, CA, USA: Intel Research Lab, 2006.
   Google Scholar