Processed ASUNA-based data and Python code for adaptive beacon-controlled time synchronization in mobile underwater acoustic sensor networks
Description
This dataset contains processed measured-data-driven evaluation files and Python code used to generate the performance results for the manuscript “Adaptive Beacon-Controlled Time Synchronization for Mobile Underwater Acoustic Sensor Networks.” The evaluation uses the public ASUNA BerlinMultimodal-06-16 underwater acoustic network dataset as measured input for packet-success behavior, topology variation, node-location information, packet-loss estimation, and time-varying link conditions. The raw ASUNA dataset is not claimed as original work by the author. This deposited dataset contains only the processed CSV files, figure-generation data, and Python scripts used to evaluate the proposed ACB-TS method. The files include processed synchronization result data for RMS synchronization error, adaptive beacon control, energy consumption, control-packet overhead, convergence time, mobility variation, packet-loss variation, and relative reduction compared with fixed-beacon synchronization. The Python code can be used to reproduce the CSV files and figures reported in the manuscript.
Files
Steps to reproduce
1. Download the public ASUNA BerlinMultimodal-06-16 underwater acoustic network dataset from the ASUNA dataset page. 2. Use FullAdjMat to extract packet-success and packet-loss behavior. 3. Use FullLocMat to extract node location and topology variation information. 4. Run the supplied Python scripts in the uploaded code package. 5. The scripts generate the processed CSV files and figures used in the manuscript, including RMS synchronization error, adaptive beacon count, energy consumption, control-packet overhead, convergence time, packet-loss sensitivity, mobility sensitivity, and relative reduction results.