Topology-Stress-Based Wormhole Attack Defense for Power Wireless Sensor Networks with UWB Physical-Layer Awareness

Power wireless sensor networks (PWSNs) provide essential field-level sensing and communication support for smart grids, where topology authenticity directly affects communication reliability and network operation. However, wormhole attacks can forge false adjacency relationships through low-latency tunnels, thereby disrupting topology consistency and misleading routing decisions. In practical power environments, metallic obstruction, multipath reflection, and non-line-of-sight (NLOS) propagation may further cause normal-ranging anomalies to resemble attack-induced topology distortion, making reliable wormhole attack detection challenging. To address this issue, this paper proposes a topology-stress-based wormhole attack defense method with ultra-wideband (UWB) physical-layer awareness. The first-path power ratio and root-mean-square delay spread extracted from UWB channel impulse responses are used to evaluate link-ranging reliability and construct adaptive stiffness coefficients. Local backbone links are modeled as virtual springs, and a topology stress indicator is derived from the residual deformation after potential-energy minimization to quantify the geometric inconsistency caused by forged adjacency relationships. Furthermore, a Beta-based temporal evidence fusion mechanism is introduced to support graded node access decisions and improve decision stability. Simulation and hardware validation results demonstrate that the proposed method effectively suppresses NLOS-induced false alarms while maintaining high sensitivity to wormhole attacks. Compared with representative baseline methods, it achieves more stable detection performance under increasing ranging errors and different attack intensities. Hardware experiments further show that topology stress can clearly distinguish normal links, NLOS-affected links, and forged wormhole links, confirming its effectiveness for topology-authenticity verification in power wireless sensor networks.