Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 958-965· 0 citations· 22 references
Abstract
With the evolution from Fifth Generation towards Beyond 5G and 6G (Sixth Generation), achieving energy efficiency has become an important objective in the design of wireless communication systems. The rise in the network density and complexity call for low-energy approaches to facilitate sustainability and longer lifetimes for networks, particularly for Wireless Sensor Networks (WSN). Present work is initiated for energy optimization based on Ant Colony Optimization, that is motivated by the natural behavior of ants. To cope with dynamic and resource-aware settings, Machine Learning (ML) mechanisms are embedded for smart decision-making and optimization. Tree based algorithms like Decision Trees, and Machine learning algorithms like K-Nearest Neighbor have been successfully implemented for wireless applications. ACO was selected for its low computational complexity and distributed and adaptive routing mechanism and has been tested previously in various WSN experiments for increased data delivery and energy efficiency. The findings presented are based on these experiences aligning with the changing demands of 5G and B5G.
WSNs continue to struggle with the issue of energy usage, network lifetime, and dependable data transfer, especially in heterogeneous networks where nodes have diverse computational and energy resources. The classical clustering algorithms are usually characterized by uneven distribution of cluster-heads, inefficient r...
Chinmay Charith Paladugu, R. N, Radhika G· 2026 International Conferenc...· 0 citations
Simulation outcomes indicate that the suggested algorithm can save up to 25% of energy per round relative to traditional protocols and increase network life to 50 times that of the LEACH protocol.
A. Mahmood, T. Khaleel· Kufa journal of Engineering· 0 citations
The research proposed an Enhanced Adaptive Intelligent Clustering (EAI-C) based on the concept of machine learning techniques and dynamically modifies clustering parameters of node density, energy levels, and communication overhead of the current state of the network in cluster head (CH) formation to achieve a better n...
Babangida Zubairu, Sagir Ibrahim· WSEAS Transactions on Comput...· 0 citations
Simulations indicate that the proposed MSA (Mosquito Swarm Algorithm) technique is highly effective at extending network longevity and increasing throughput, making it the preferred option to lower energy consumption in WSNs.
Wireless Sensor Networks (WSNs) play a crucial role in the expanding landscape of the Internet of Things (IoT), yet they continue to face persistent challenges related to energy consumption, computational efficiency, and scalability. Although protocols like the Energy-Efficient Routing Protocol through Hybrid Algorithm...
Maheshkumar Patil, B. J, K. R et al.· 2026 7th International Confe...· 0 citations
An innovative EDSP-QCH (Energy-efficient Dynamic Sub-Station Placement and reward-based Q-Learning reinforcement model for Cluster Head selection) strategy is proposed, which indicates significant improvements in energy efficiency and network robustness.
