ArticleScientific reports2026
Analysis and comparison of two selected machine learning algorithms for enhancing wireless sensor network protocols.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- An intelligent reinforcement learning enhanced improved LEACH protocol for prolonging wireless sensor network lifetime.Scientific reports · 2026Article
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2 authors.
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Abstract
Wireless Sensor Networks (WSNs) consist of low-power, battery-powered sensor nodes that collaborate to monitor the environment and transmit data to a base station. However, their efficiency is mainly constrained by energy usage and limited network lifespan. Among cluster‑based routing protocols, the Deterministic Energy‑Efficient Clustering (DEC) protocol is a recognised hierarchical scheme for heterogeneous WSNs; however, DEC still faces several challenges, particularly with energy consumption. This paper presents a unified comparative study of two machine learning-enhanced modifications to the DEC protocol for WSNs. Unlike our previous protocol proposals, this work integrates DEC-KNN (K-Nearest Neighbours) and DEC-KM (K-Means) within a single simulation framework, systematically compares their energy, distance, and stability performance, and provides practical deployment guidelines based on application-specific trade-offs. The simulations are conducted through MATLAB R2024b simulations on a 50-node heterogeneous WSN covering a 100 m × 100 m area. Both protocols show a substantial benefit over the original protocol baseline. DEC-KM leads to a small reduction in both total and average energy consumption and provides a good balance between clustering performance and energy consumption, making it an attractive option for energy-limited WSN applications in Internet of Things (IoT) and smart environments.
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