"RC-FDIA_defense"

"This supplementary data set is associated with the research on reflective collaborative false data injection attack (RC-FDIA) defense in wireless sensor networks (WSNs), which proposes two scenario-adaptive lightweight machine learning schemes (GRF-ML for GPS-equipped WSNs and NRF-ML for GPS-free WSNs). The data set includes all original and processed research data, experimental code, and auxiliary explanation documents to fully reproduce the performance verification results of the GRF-ML and NRF-ML schemes in the study. Specifically, the supplementary files consist of four parts: \u2460 NS-3 simulation data (CSV\/Excel format) covering key performance indicators (filtering rate, compromise tolerance, energy consumption, forwarding delay) under different numbers of compromised nodes (0-200) and reflective node ratios (0-30%); \u2461 MICA2 hardware test raw data (TXT\/CSV format) including per-hop verification delay, single-node energy consumption, and actual filtering rate measured in indoor WSNs deployment; \u2462 Core implementation code (Python\/C++ format) of GRF-ML and NRF-ML, involving the five-layer en-route verification algorithm, dynamic feature selection based on information-theoretic methods, and lightweight logistic regression model; \u2463 Detailed auxiliary explanation documents (PDF format) containing simulation parameter configuration (consistent with mainstream WSNs deployment standards), hardware experimental environment (MICA2 node and CC2530 coordinator parameters), and detailed interpretation of all data fields. All data in the set are versioned as V1, with strict consistency between experimental parameters and the research paper, and can be directly used for result reproduction, performance comparison and algorithm optimization of WSNs security defense schemes. This data set provides a reliable experimental basis for the research fields of WSNs false data injection attack defense, lightweight machine learning application in resource-constrained networks, and en-route data verification, and can also serve as reference data for the performance evaluation of related WSNs security schemes."