DATA SETS : Analysis and Comparison of Two Selected Machine Learning Algorithms

The code and scripts implementing the clustering algorithms (Algorithm 1 and Algorithm 2) described in this study are publicly available and archived in the Zenodo repository using the DOI 10.5281/zenodo.19007010. The synthetic sensor node datasets used for the performance evaluation are generated within the provided MATLAB scripts using fixed random seeds to ensure full computational reproducibility. No external datasets were used in this study.Study conatain more than 30 Files Data Sets ## Project Structure ## TASK used MATLAB, some PYTHON ONLY FOR CHECKS, not included in the paper - README.md - algorithms:- * DEC_KNN.m # MATLAB implementation of Algorithm 1 * DEC_KM.m # MATLAB implementation of Algorithm 2 * DEC.m # MATLAB implementation of original DEC protocol * dec_knn.py # Python implementation of Algorithm 1 (DEC-KNN) // for check ONLY * dec_km.py # Python implementation of Algorithm 2 (DEC-KM) // for check ONLY * dec_protocol.py # Python implementation of original DEC protocol // for check ONLY - Data:- * DATAset MASTER For Test # 30 Files Data Sets * ALL.txt - # DATAset For Test * Data set DEC - KM.txt * Data set DEC - KNN.txt * DataForTest.txt * CA1.txt, CA2.txt, CA3.txt, CA4.txt, CA5.txt, CA6.txt # to check cluster heads * DB.mat # to use for Matlab * SinkDEC.mat * STAT.mat - Figures:- * DEC.fig * DEC-KM.fig * FINAL.fig * ORG.fig # for KNN test * untitled.fig # after KNN implementation - Additional Scripts:- * DEC.m * knn.m * VTest.m * VTest2.m - Other: * parameters.txt # Detailed parameter descriptions * run_experiments_matlab.m # Main MATLAB execution script * inputs.txt # Input data specifications * run_experiments_matlab.m # Main MATLAB execution script ## MATLAB 1. Open MATLAB (R2018b or later recommended). 2. Ensure the 'Statistics and Machine Learning Toolbox' is installed. 3. Run `run_experiments_matlab.m`+ All related m files. ## Citation If you use this code or dataset, please cite the original paper: *Juwaied, A., & Jackowska-Strumillo, L. (2026). Analysis and Comparison of Two Selected Machine Learning Algorithms for Enhancing Wireless Sensor Network Protocols.*

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Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-13
DOI
https://doi.org/10.5281/zenodo.19007011
Primary Topic
Energy Efficient Wireless Sensor Networks
Type
article
Field-Weighted Citation Impact
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article

DATA SETS : Analysis and Comparison of Two Selected Machine Learning Algorithms

Lidia M. Jackowska-Strumillo, Juwaied Abdulla
Zenodo (CERN European Organization for Nuclear Research)
Energy Efficient Wireless Sensor Networks
article

DATA SETS : Analysis and Comparison of Two Selected Machine Learning Algorithms

Lidia M. Jackowska-Strumillo, Juwaied Abdulla
article en

Abstract

The code and scripts implementing the clustering algorithms (Algorithm 1 and Algorithm 2) described in this study are publicly available and archived in the Zenodo repository using the DOI 10.5281/zenodo.19007010. The synthetic sensor node datasets used for the performance evaluation are generated within the provided MATLAB scripts using fixed random seeds to ensure full computational reproducibility. No external datasets were used in this study.Study conatain more than 30 Files Data Sets ## Project Structure ## TASK used MATLAB, some PYTHON ONLY FOR CHECKS, not included in the paper - README.md - algorithms:- * DEC_KNN.m # MATLAB implementation of Algorithm 1 * DEC_KM.m # MATLAB implementation of Algorithm 2 * DEC.m # MATLAB implementation of original DEC protocol * dec_knn.py # Python implementation of Algorithm 1 (DEC-KNN) // for check ONLY * dec_km.py # Python implementation of Algorithm 2 (DEC-KM) // for check ONLY * dec_protocol.py # Python implementation of original DEC protocol // for check ONLY - Data:- * DATAset MASTER For Test # 30 Files Data Sets * ALL.txt - # DATAset For Test * Data set DEC - KM.txt * Data set DEC - KNN.txt * DataForTest.txt * CA1.txt, CA2.txt, CA3.txt, CA4.txt, CA5.txt, CA6.txt # to check cluster heads * DB.mat # to use for Matlab * SinkDEC.mat * STAT.mat - Figures:- * DEC.fig * DEC-KM.fig * FINAL.fig * ORG.fig # for KNN test * untitled.fig # after KNN implementation - Additional Scripts:- * DEC.m * knn.m * VTest.m * VTest2.m - Other: * parameters.txt # Detailed parameter descriptions * run_experiments_matlab.m # Main MATLAB execution script * inputs.txt # Input data specifications * run_experiments_matlab.m # Main MATLAB execution script ## MATLAB 1. Open MATLAB (R2018b or later recommended). 2. Ensure the 'Statistics and Machine Learning Toolbox' is installed. 3. Run `run_experiments_matlab.m`+ All related m files. ## Citation If you use this code or dataset, please cite the original paper: *Juwaied, A., & Jackowska-Strumillo, L. (2026). Analysis and Comparison of Two Selected Machine Learning Algorithms for Enhancing Wireless Sensor Network Protocols.*

Zenodo (CERN European Organization for Nuclear Research)
Lodz University of Technology (PL)
Openalex Percentile: Top 26%
Energy Efficient Wireless Sensor Networks
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