Evaluating Combinatorial Anti-Spoofing Strategies for IoT GNSS Receivers Under Dynamic Meaconing Attacks

Dynamic meaconing attacks, where authentic GNSS signals are replayed with adaptive delays, threaten the integrity of IoT devices that rely on precise positioning and timing. While several lightweight, receiver-centric anti-spoofing methods exist, each suffers from limitations such as drift, noise sensitivity, or hardware demands. This work presents a purely analytical evaluation of combinatorial defense strategies using a 2-of-3 voting rule across four lightweight techniques: IMU-aided Kalman filter innovations, Doppler residual checks, C/No analysis, and clock-bias jump detection. Using conservative, worst-case ROC operating points from prior studies, we show that combining detectors significantly improves spoofing detection while keeping false alarms manageable. The IMU-Doppler-clock-bias triplet consistently emerges as the most robust configuration under dynamic meaconing. A computational and power analysis confirms that all methods are feasible for constrained IoT GNSS receivers, with per-update costs remaining within real-time budgets. This study provides a hardware-independent theoretical baseline. Future work will validate these findings on physical GNSS receivers through hardware-in-the-loop testing and dynamic replay experiments.