EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks

EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks

Blog Article

Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions.However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifespan.To address these limitations and enhance the energy efficiency of WSNs, it is often necessary to divide sensors into clusters and establish routing to conserve energy.Machine learning algorithms can potentially automate these processes, minimizing energy consumption and extending network lifetime.

This research investigates the application of machine learning algorithms, specifically Q-learning and K-means clustering, to propose the Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR) method for WSNs.This method facilitates cluster formation and routing majicontrast red path selection.The proposed method is compared with the well-established LEACH algorithm and two multi-hop variants, DMHT LEACH and EDMHT LEACH to validate its effectiveness.Our experimental results demonstrate the effectiveness of EEMLCR compared to cubs foam finger LEACH and its multi-hop variants (DMHT LEACH and EDMHT LEACH).

After 600 rounds in networks comprising 400 nodes, EEMLCR showed significant improvements in key performance metrics.These include increased alive nodes, reduced average energy consumption, higher remaining energy levels, and improved packet reception.Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency.By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes.

The primary objective of this research is to contribute to the development of energy-efficient WSNs by leveraging machine learning algorithms for data routing and the cluster-based organization of sensor nodes.