Trust-Driven Adaptive Sampling for Grid-Based IoT Path Validation

We present trust-driven adaptive sampling (TDAS), a lightweight and efficient path validation scheme designed for resource-constrained Internet of Things (IoT). The key innovation of TDAS is its trust-driven adaptive sampling mechanism, which strategically validates packet flows based on dynamic node trust and a random factor, moving beyond conventional per-packet or fixed-probability approaches. This allows TDAS to focus validation overhead where it is most needed—on untrusted or unstable paths. The scheme employs grid-based network partitioning for scalable management and uses lightweight cryptographic operations (truncated hashes and MACs) for proof generation and verification. A non-cryptographic hiding strategy effectively conceals sampling states from adversaries. Security analysis confirms TDAS’s resilience against path deviation attacks. Evaluation results demonstrate that TDAS significantly outperforms state-of-the-art benchmarks (Hummingbird and EPIC). In simulations of a 100-node IoT network, TDAS increases system throughput by up to 15% and reduces average path validation time by approximately 20%. These performance gains are also validated on a physical IoT testbed using DAYU800 & DAYU200 development kits running OpenHarmony OS.