Critical node identification of dynamic-load wireless sensor networks for cascading failure protection

Abstract

Wireless Sensor Networks (WSNs), as complex and dynamic systems, are highly susceptible to cascading failures. To enhance network resilience, this study addresses the identification of critical nodes that drive failure propagation. Unlike prior studies that often ignore the impact of varying network load, we highlight that node importance can change significantly under dynamic load conditions. To tackle this, we introduce a method for identifying critical nodes in dynamic-load WSNs. We first construct a cascading failure model that links network load with link capacity, analyzing how fluctuations in load affect failure propagation. Building on this model, we propose an EW-TOPSIS-based node evaluation method grounded in node deletion, where the influence of each node under different load conditions is considered as distinct evaluation criteria. To verify the proposed method, we conduct simulations of low-rate underwater WSNs in ns-3 under dynamic load conditions. Results show that, as an attack node selection strategy, our method achieves up to 30% and 25% greater degradation in failure severity and PDR, respectively, across varying network topology, densities and traffic conditions, compared to five baseline techniques. This work provides insights for designing effective mitigation strategies against cascading failures in resource-constrained networks.