Quick Links
|
International Journal of Advanced Innovative Technology in Engineering (IJAITE)Integrated Deep Temporal Learning and Reinforcement Optimization for Energy-Efficient Routing and Clustering in Wireless Sensor Networks Prof. Kalyani S. Tidke Abstract : These Wireless Sensor Networks are widely used in applications such as environmental monitoring, smart cities, and industrial automation, where energy efficiency remains a critical challenge due to limited battery resources of sensor nodes. This paper proposes a Hybrid Energy-Efficient Intelligent model that integrates deep learning, swarm optimization, and reinforcement learning to minimize energy consumption and enhance network lifetime. The model combines a Bidirectional Long Short-Term Memory model for energy prediction, Particle Swarm Optimization for optimal cluster head selection, and an Actor-Critic reinforcement learning model for adaptive routing. The proposed approach used temporal energy patterns to prevent premature node failures, balances energy distribution through optimized clustering, and dynamically selects routing paths to reduce communication overhead. Extensive simulation results demonstrate that the proposed HEEIF framework achieves up to 80% energy reduction, improves network lifetime by 20-25%, and maintain a high packet delivery ratio of 94-96% compared to conventional methods such as LEACH, LSTM-based, and RL-based approaches. Additionally, the framework improves throughput, reduces delay, and enhances load balancing. The results indicate that integrating prediction, optimization, and adaptive learning that allows the scalable and efficient solution for next-generation energy-aware WSNs. Keywords : Wireless Sensor Network, Energy Optimization, Particle Swarm Optimization, Actor-Critic reinforcement Full Text : Download PDF DOI : 10.65809/IJAITE/26/v11i02/001 Cite this paper : - References : [1] Tatiraju, K. K., Kanth, V. R., Babiyola, A., & Mishra, H. (2024). IoT-Based Smart Agricultural Monitoring Using WSN and Predictive Analytics with Artificial Intelligence (AI). International Journal of BIM & Engineering Science, 10(1), 26â34. https://doi.org/10.54216/ijbes.100104 [2] Sollapure, N. S., & G, P. (2024). An Efficient Machine Learning and Filter Based Implementation of Data Fusion in WSN. https://doi.org/10.5281/zenodo.10628271 [3] Hutaihit, M. A. (2025). Ai-enhanced physical layer for energy-efficient wireless sensor networks (wsns). International Journal of Apllied Mathematics, 38(6s), 1041â1062. https://doi.org/10.12732/ijam.v38i6s.451 [4] Chakravarthy, R. A., Sureshkumar, C., Arun, M., & Bhuvaneswari, M. (2025). AI-Driven Energy-Efficient Data Aggregation and Routing Protocol Modeling to Maximize Network Lifetime in Wireless Sensor Networks. NDT, 3(4), 22. https://doi.org/10.3390/ndt3040022 [5] Hakkem, B., Mahendrakan, K., Prabu, S., & Irshad, R. R. (2024). EnergyâEfficient Adaptive Clustering in Wireless Sensor Networks Using AIâDriven Optimization Algorithms. 01(01), 38â50. https://doi.org/10.69626/cai.2024.0038 [6] Yamsani, N., Almoussawi, Z. abed, Sindhu, L., Almusawi, M., & Aarthi, C. (2023). A Hybrid Cross Layer with Fuzzy Chaotic Whale Wolf Optimization-Based Efficient Routing for Wireless Sensor Networks. 1â5. https://doi.org/10.1109/aikiie60097.2023.10390199 [7] Pande K., Passi, A., Rao, M., Sholapurapu, P. K., Bhagyalakshmi, L., & Suman, S. K. (2025). Enhancing Energy Efficiency and Data Reliability in Wireless Sensor Networks Through Adaptive Multi-Hop Routing with Integrated Machine Learning. Journal of Machine and Computing, 2504â2512. https://doi.org/10.53759/7669/jmc202505192 [8] Mabunga, Z. P., & Dela Cruz, J. C. (2024). Deep Recurrent Neural Network for Time Series Modelling of Energy in Wireless Sensor Network Nodes. 126â131. https://doi.org/10.1109/icsgrc62081.2024.10690921 [9] Sollapure, N. S., & Sollapure, N. S. (2025). Enhancing energy efficiency and accuracy in IoT-based wireless sensor networks using machine learning. IAES International Journal of Artificial Intelligence, 14(5), 3869. https://doi.org/10.11591/ijai.v14.i5.pp3869-3878 [10] Bagwari, A., Logeshwaran, J., Usha, K., Kannadasan, R., Alsharif, M. H., Uthansakul, P., & Uthansakul, M. (2023). An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning. IEEE Access. https://doi.org/10.1109/access.2023.3311854 [11] Chauhan, N., & Bansal, S. (2025). Performance Evaluation of Machine Learning Techniques in Residential Energy Consumption Forecasting. 393â398. https://doi.org/10.1109/mrie66930.2025.11156370 [12] Sibi, S. A., & Annabel, L. S. P. (2024). Optimizing Energy Resources in WSNs: ARIMA Feature Selection Meets Adaptive Reinforcement Learning. Fiber and Integrated Optics, 43(2), 69â96. https://doi.org/10.1080/01468030.2024.2342269 [13] Balakumar, D., & Rangaraj, J. (2025). An energy efficient prediction based data collection approach using deep LSTM in wireless sensor network. Intelligent Decision Technologies. https://doi.org/10.1177/18724981251343221 [14] Surenther, I., Sridhar, K. P., & Roberts, M. K. (2023). Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach. Alexandria Engineering Journal. https://doi.org/10.1016/j.aej.2023.10.016 [15] Vijayakumar, K., Packirisamy, T., Vijayakumar, K., & Packirisamy, T. (2025). DeepCuckoo: A Synergistic Approach Using Deep Learning and Bioinspired Cuckoo Search for Optimized EnergyâEfficient Cluster Head Selection in 5G and Advanced Wireless Sensor Networks. International Journal of Communication Systems, 38(16). https://doi.org/10.1002/dac.70242 [16] Hakkem, B., Mahendrakan, K., Prabu, S., & Irshad, R. R. (2024). EnergyâEfficient Adaptive Clustering in Wireless Sensor Networks Using AIâDriven Optimization Algorithms. 01(01), 38â50. https://doi.org/10.69626/cai.2024.0038 [17] Sivasankar, C., Subramanian, E. K., Sarala, V., Moorthy, A. K., & Purushothaman, N. (2024). Optimizing Power Consumption in Wireless Sensor Networks for Prolonged Sustainability. 1â4. https://doi.org/10.1109/icait61638.2024.10690388 [18] Anshad, A. S., Babavali, S. F., Cephas, I., Ramanan, S. V., Priya, G. G. L., & Mishra, S. N. (2025). An Intelligent Hybrid Quantum-Deep Reinforcement Framework for Energy-Efficient Routing in Wireless Sensor Networks. 1â6. https://doi.org/10.1109/incet64471.2025.11140843 [19] Depuru, S., Reddy, P. J., Rafi, S., Amala, K., Akshaya, V., & Siri, K. (2025). Hybrid Temporal-Spatial Energy Prediction in Wireless Sensor Networks using Transformer and Graph Neural Network Models. 114â118. https://doi.org/10.1109/iccmc65190.2025.11140970 [20] Haripriya, R., Muthusamy, S., & Vinutha, C. B. (2025). A Hybrid Routing Algorithm using Artificial Neural Network and Swarm intelligence for Wireless Sensor Networks. Journal of Neonatal Surgery, 14(27S), 757â768. https://doi.org/10.63682/jns.v14i27s.6503 [21] Hachicha, M., Ben Halima, R., & Jemal, A. (2024). An Estimation Approach to Optimize Energy Consumption in Wireless Sensor Network: A Health-Care Application. https://doi.org/10.60692/kh2a8-awd03 |