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 routing paths, and poor responsiveness to changes in network dynamics. This article presents a new framework, OEEMLCR (Optimized Energy-Efficient Machine Learning-Based Clustering and Routing), which is a combination of Particle Swarm Optimization-based K-means clustering and Coati Optimization Algorithm-based routing and Q-learning adaptation. The suggested methodology fills in crucial gaps through the use of a dual-objective fitness function that both maximizes the compactness of space and the homogeneity of energy when forming clusters and introduces the learning technique of reinforcement to allow adaptive routing behavior under the influence of experience gained in the network. Experimental validation of heterogeneous network situations show that OEEMLCR can attain significant gains over existing protocols: network lifetime is increased 13.6% over baseline machine learning methods, cumulative packet delivery is increased 108%, energy efficiency is increased 340%. The framework operates in a stable way during 1000 rounds of the simulation and is 96.7 percent of the initial network power, far outperforming LEACH, DMHT, EDMHT, and EEMLCR in lifetime, throughput, percentage ratio of delivering packets, and the network resilience criterion.
Chinmay Charith Paladugu, R. N, Radhika G· 2026 International Conferenc...· 0 citations
The Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, healthcare, and smart farming. However, energy consumption remains critical since battery-powered sensor nodes directly affect network lifetime. The conventional clustering and multi-hop routing algorithms are prone to collapse when used in dynamic environments, resulting in poor energy consumption and frequent node failures. This paper proposes a novel reinforcement learning (RL) routing algorithm based on Q-learning to enhance energy savings in WSNs. The algorithm is dynamic in assigning routes based on node energy, communication distance, and packet size, and adapts in real-time to network changes. It ensures that data transfer is efficient and the load distribution throughout the network is even by updating routing decisions with Q-learning. These 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
For real-time applications, wireless sensor network technology driven by artificial intelligence (AI) is the way of the future. This technology makes it possible to collect data from almost any type of environment, analyze it instantly, and use its outcomes to improve operations and procedures. To optimize the clustering process in Wireless Sensor Networks (WSNs), an Adaptive Intelligent Clustering (AI-C) algorithm has been proposed previously where cluster heads are chosen probabilistically based on node distances and network density, though the scheme has shortcomings with respect to power usage and short network lifespan. 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. The cluster heads are selected intelligently and minimize energy depletion during data transfer. The proposed algorithm enhances the lifespan of sensor nodes while maintaining efficient network coverage. However, the scheme outperforms previous approaches of LEACH and AI-C in terms of extending network lifetime. Furthermore, the simulation result shows that the proposed approach achieved a better network performance, reduced energy consumption.
Babangida Zubairu, Sagir Ibrahim· WSEAS Transactions on Comput...· 0 citations
Wireless sensor networks (WSNs) are sophisticated monitoring systems that gather environmental data via wireless sensors. Numerous wireless sensors that have been placed to monitor and gather data on a particular environment make up these networks. Typically, a base station or central node wirelessly collects the data prior to analysis. As the battery in wireless sensors is both non-replaceable and non-rechargeable, it represents a key element. As a result, optimizing energy consumption in WSN has become a growing concern. One of the key challenges is consequently the creation of effective protocols for communication in WSNs. In this article, we provide a novel MSA (Mosquito Swarm Algorithm) technique for cluster formation and data routing. Simulations indicate that our proposed algorithm conserves the energy of the nodes and keeps them running for a greater number of survival rounds compared to LEACH (Low-Energy Adaptive Clustering Hierarchy) by a difference of 70.14%, PSO-R (Particle Swarm Optimization with routing) by a difference of 4.76%, and BA-R (Bat Algorithm with routing) by a difference of 2.52%. Our algorithm provides the highest throughput, surpassing LEACH by approximately 6.38%, PSO-R by almost 1%, and BA-R by 12.67%. Simulations indicate that our algorithm 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 Algorithms (EERHA) have made progress in extending network lifespan using machine learning, they still encounter limitations particularly in managing processing overhead, adapting to changing conditions, and scaling to larger deployments. Unlike existing approaches that merely combine machine learning modules, DEERL-WSN (Distributed Energy-Efficient Reinforcement Learning for Wireless Sensor Networks), introduces a unified hierarchical learning framework where distributed reinforcement learning, lightweight graph neural networks, transfer learning, and federated optimization operate cooperatively. The novelty lies in (i) adaptive reward-driven routing using meta-learned objective weights, (ii) topology-aware clustering through lightweight GNN embeddings with significantly reduced computational complexity, (iii) transfer learning-assisted cluster-head prediction to eliminate repetitive optimization overhead, and (iv) federated deep reinforcement learning enabling scalable learning without centralized processing bottlenecks. These integrated innovations collectively address energy efficiency, scalability, and computational constraints simultaneously, which remain largely unresolved in existing WSN routing frameworks. DEERL-WSN addresses several bottlenecks found in earlier protocols and the simulation results show that DEERL-WSN significantly outperforms EERHA and other state-of-the-art methods.
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.
