Experimental Dataset: Hardware-Accelerated Security in Edge IoT This repository contains the complete telemetry and performance logs from a longitudinal experimental campaign evaluating the resilience of ESP32-based secure edge nodes communicating with a Raspberry Pi 5 Gateway. The data was collected to validate the performance of hardware-accelerated AES-GCM encryption under conditions of signal degradation (-88 dBm), network congestion (Bufferbloat), and power cycling. 📄 ASSOCIATED MANUSCRIPT This dataset supports the findings presented in the research article: "Cross-Layer Benchmarking of Hardware-Accelerated Security in Edge IoT Under Wi-Fi Signal Degradation and Network Congestion" 📂 DATASET STRUCTURE EdgeSec_Benchmark_Dataset/ │ ├── 01_Raw_Data/ (Original Server Logs - Raspberry Pi 5) │ ├── Day-1/ │ │ ├── Baseline_Server_Day-1.csv │ │ ├── Noise_Server_Day-1.csv │ │ ├── Distance_Server_Day-1.csv │ │ ├── Stress_Server_Day-1.csv │ ├── Day-2/ ... │ ├── Day-3/ ... │ └── Client_Serial_Logs/ (Forensic Boot Logs - ESP32) │ └── Stress_Client_DayX.txt │ ├── 02_Processed_Data/ (Cleaned & Merged) │ ├── Total_Baseline.csv │ ├── Total_Noise.csv │ ├── Total_Distance.csv │ └── Total_Stress.csv │ ├── 03_Analysis_Code/ (Reproducibility) │ ├── EdgeSec_Reproducibility_Analysis.ipynb │ └── extract_scientific_datapoints.py │ └── README.txt 📊 COLUMN DEFINITIONS seq: Sequence ID for calculating Packet Loss. latency_ms: End-to-End Latency (Server_Rx - Client_Tx). rssi: WiFi Signal Strength (dBm). crypto_time_us: Encryption overhead (microseconds). heap_free: Available memory (Bytes). throughput: Bandwidth usage (Bytes/sec). ⚠️ DATA NOTE (NTP Exclusions) While the Raw Data contains 30,953 packets, 322 packets (1.04%) were generated prior to client-side NTP synchronization. These were analytically excluded from the Processed Data to preserve precise end-to-end latency calculations, resulting in exactly 30,631 valid data points. 🛠 USAGE & REPRODUCIBILITY The included Jupyter Notebook (EdgeSec_Reproducibility_Analysis.ipynb) and Python extraction script contain the logic required to process these CSVs and reproduce all statistical figures and Kruskal-Wallis H-tests found in the associated research manuscript. Required Environment: Python 3.x (Tested on 3.14.0). To avoid Kernel execution errors (WinError 2), please ensure the following dependencies are installed in your virtual environment before running the notebook: pandas>=2.0.0 numpy>=1.24.0 scipy>=1.10.0 seaborn>=0.12.0 matplotlib>=3.7.0 jupyter>=1.0.0