An Adaptive Deep Learning Framework for Energy-Efficient Clustering in Wireless Sensor Networks
Keywords:
Wireless Sensor Networks, Energy Efficiency, Network Lifetime, Cluster Head Selection, Artificial Neural Network, ClusteringAbstract
Wireless sensor networks (WSNs) are used for the indoor monitoring system, biological applications in healthcare, and other day to day applications. However, the biggest problem is that they currently have very limited life indeed since the only apparent stopping force for them is the energy stored in the battery. The traditional clustering protocols, such as LEACH, concentrate on a probabilistic forward feedback approach for determining who the Cluster Head (CH) is to be. This has resulted to more of the power bias in the network, makes it a hundred less fault prone and thus lasts even fewer years. This research study recommends the application of an Energy-Efficient Clustering Algorithm (EECA) that employs the use of the Artificial Neural Network (ANN)-based procedure in the cluster head selection process since it relates with many system parameters. The result is the efficient placement of the nodes according to each CH, then their communication in every round is through TDMA and multi-hop. We demonstrate in a detailed fashion how our EECA models provide strong enhancements in quality of network operations. Specifically, it was observed that comparing with the performance of the LEACH protocol, the last extended the network liveliness to a larger optimization time of about 3200 rounds rather than LEACH’s 1600 rounds in operation. In addition to this, more than 50% of the energy was sustained as compared to the same LEACH cycle not failing any Packet Delivery Ratio (PDR) of 0.96. This helps to solve the problem by putting more emphasis on the increased rates and throughputs at the comfortable latency level for the best communication. In this sense, clustering decisions integrated with deep learning offer a proven and scalable means of improving the energy performance and operational activity of WSNs.
References
Akram, Muhammad, et al. "EEMLCR: Energy-efficient machine learning-based clustering and routing for wireless sensor networks." IEEE Access (2025).
Kang, Jaewoong, et al. "Machine learning-based energy-saving framework for environmental states-adaptive wireless sensor network." IEEE Access 8 (2020): 69359-69367.
Juwaied, Abdulla, and Lidia Jackowska-Strumillo. "DL-HEED: A deep learning approach to energy-efficient clustering in heterogeneous wireless sensor networks." Applied Sciences 15.16 (2025): 8996.
Wang, Wei, and Mengjun Zhang. "Self-adaptive gathering for energy-efficient data stream in heterogeneous wireless sensor networks based on deep learning." IEEE Wireless Communications 27.5 (2020): 74-79.
Wibowo, Ferry Wahyu, Eko Sediyono, and Hindriyanto Dwi Purnomo. "An adaptive framework using machine learning in wireless sensor network." 2020 IEEE International Conference on Communication, Networks and Satellite (Comnetsat). IEEE, 2020.
Gherbi, Chirihane, Zibouda Aliouat, and Mohamed Benmohammed. "An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks." Energy 114 (2016): 647-662.
Abu-Baker, Amjad, Ahmad Alshamali, and Yanal Shawaheen. "Energy-efficient cluster-based wireless sensor networks using adaptive modulation: Performance analysis." IEEE Access 9 (2021): 141766-141777.
Zhang, Yang, Ling Yang, and Yan Tan. "Energy-efficient adaptive routing in heterogeneous wireless sensor networks via hybrid PSO and dynamic clustering." Journal of Cloud Computing 14.1 (2025): 46.
Jalili, Ahmad, and Fatemeh Babakordi. "A New Energy-Efficient Clustering in Wireless Sensor Networks Using an Adaptive Fuzzy Neural Network Approach." Control and Optimization in Applied Mathematics 10.2 (2025): 75-102.
Ding, Qianao, et al. "An overview of machine learning-based energy-efficient routing algorithms in wireless sensor networks." Electronics 10.13 (2021): 1539.
Guo, Hong, Jian Zhang, and Hossein Omidizadeh. "Adaptive and scalable energy aware clustering for mobile wireless sensor networks using a density driven approach." Scientific Reports 15.1 (2025): 40198.
Sakthimohan, M., J. Deny, and G. Elizabeth Rani. "Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks." Journal of Intelligent & Fuzzy Systems 46.4 (2024): 8587-8603.
Khashan, Osama A., et al. "Smart energy-efficient encryption for wireless multimedia sensor networks using deep learning." IEEE Open Journal of the Communications Society 5 (2024): 5745-5763.
Soundari, A. Gnana, and V. L. Jyothi. "Energy efficient machine learning technique for smart data collection in wireless sensor networks." Circuits, Systems, and Signal Processing 39.2 (2020): 1089-1122.
Hu, Liyazhou, et al. "Security enhancement for deep reinforcement learning-based strategy in energy-efficient wireless sensor networks." Sensors 24.6 (2024): 1993.
Yun, Wan-Kyu, and Sang-Jo Yoo. "Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks." IEEE Access 9 (2021): 10737-10750.
Kathiresan, Seethalakshmi, et al. "DQL-iLEACH: A Deep Reinforcement Learning-Driven Clustering Protocol for Energy-Efficient Wireless Sensor Networks." IAENG International Journal of Computer Science 53.3 (2026).
Mittal, Mohit, et al. "Machine learning techniques for energy efficiency and anomaly detection in hybrid wireless sensor networks." Energies 14.11 (2021): 3125.
Saadati, Morteza, et al. "Energy efficient clustering for dense wireless sensor network by applying graph neural networks with coverage metrics." Ad Hoc Networks 156 (2024): 103432.
Manchanda, Rachit, et al. "Energy-efficient wireless sensor network for urban groundwater level monitoring using machine learning and sink mobility." Scientific Reports (2026).
Meenakshi, N., et al. "Efficient communication in wireless sensor networks using optimized energy efficient engroove leach clustering protocol." Tsinghua Science and Technology 29.4 (2024): 985-1001.
Abadi, Ali Forghani Elah, et al. "RLBEEP: Reinforcement-learning-based energy efficient control and routing protocol for wireless sensor networks." IEEe Access 10 (2022): 44123-44135.
Adu-Manu, Kofi Sarpong, Emmanuel Amoako, and Felicia Engmann. "Advancements in Machine Learning‐Enhanced Green Wireless Sensor Networks: A Comprehensive Survey on Energy Efficiency, Network Performance, and Future Directions." Journal of Sensors 2025.1 (2025): 5242517.
Sahoo, Laxminarayan, et al. "Improvement of wireless sensor network lifetime via intelligent clustering under uncertainty." IEEE Access 12 (2024): 25018-25033.
Sharma, Tripti, Archana Balyan, and Ajay Kumar Singh. "Machine learning-based energy optimization and anomaly detection for heterogeneous wireless sensor network." SN computer science 5.6 (2024): 751.
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