A Geographical Information-Based Reflective Collaborative False Data Filtering Scheme in Wireless Sensor Networks

Wireless sensor networks (WSNs) are highly vulnerable to reflective collaborative false data injection attacks (RC-FDIA), where adversaries manipulate legitimate nodes to forward false reports, leading to amplified energy consumption and hidden attack sources. Existing schemes (e.g., geographical-based false data filtering scheme (GFFS)) lack targeted detection for reflective nodes, failing to counter this threat. This paper proposes geographical information based reflective collaborative false data filtering (GRFFS), a lightweight filtering scheme that integrates a four-layer verification mechanism: format, geographical location legitimacy, reflective behavior (targeting reflective nodes), and message authentication code (MAC)/key consistency verification. Theoretical analysis and multi-scenario experiments show GRFFS outperforms state-of-the-art schemes (RC-FDIA filtering rate >50%) with 37% higher compromise tolerance, 29% lower energy consumption, and 1.25 KB optimized storage, well-suited for resource-constrained WSNs under attacks.