TY - JOUR
T1 - Cascading failure modeling and survivability analysis of weak-communication underwater unmanned swarm networks
AU - Yuan, Yifan
AU - Shen, Xiaohong
AU - Sun, Lin
AU - He, Ke
AU - Yan, Yongsheng
AU - Wang, Haiyan
N1 - Publisher Copyright:
© 2025 China Ordnance Society
PY - 2026/2
Y1 - 2026/2
N2 - Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks (UUSNs), significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring. Existing failure models primarily focus on power grids and traffic systems, and don't address the unique challenges of weak-communication UUSNs. In UUSNs, cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels, passive node drift, adversarial attacks, and network heterogeneity. To address these challenges, a directed weighted graph model of UUSNs is first developed, in which node positions are updated according to ocean-current–driven drift and link weights reflect the probability of successful acoustic transmission. Building on this UUSNs graph model, a cascading failure model is proposed that integrates a normal–failure–recovery state-cycle mechanism, multiple attack strategies, and routing-based load redistribution. Finally, under a five-level connectivity UUSNs scheme, simulations are conducted to analyze how dynamic topology, network load, node recovery delay, and attack modes jointly affect network survivability. The main findings are: (1) moderate node drift can improve survivability by activating weak links; (2) based-energy routing (BER) outperform based-depth routing (BDR) in harsh conditions; (3) node self-recovery time is critical to network survivability; (4) traditional degree-based critical node metrics are inadequate for weak-communication UUSNs. These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.
AB - Cascading failures pose a serious threat to the survivability of underwater unmanned swarm networks (UUSNs), significantly limiting their service ability in collaborative missions such as military reconnaissance and environmental monitoring. Existing failure models primarily focus on power grids and traffic systems, and don't address the unique challenges of weak-communication UUSNs. In UUSNs, cascading failure present a complex and dynamic process driven by the coupling of unstable acoustic channels, passive node drift, adversarial attacks, and network heterogeneity. To address these challenges, a directed weighted graph model of UUSNs is first developed, in which node positions are updated according to ocean-current–driven drift and link weights reflect the probability of successful acoustic transmission. Building on this UUSNs graph model, a cascading failure model is proposed that integrates a normal–failure–recovery state-cycle mechanism, multiple attack strategies, and routing-based load redistribution. Finally, under a five-level connectivity UUSNs scheme, simulations are conducted to analyze how dynamic topology, network load, node recovery delay, and attack modes jointly affect network survivability. The main findings are: (1) moderate node drift can improve survivability by activating weak links; (2) based-energy routing (BER) outperform based-depth routing (BDR) in harsh conditions; (3) node self-recovery time is critical to network survivability; (4) traditional degree-based critical node metrics are inadequate for weak-communication UUSNs. These results provide a theoretical foundation for designing robust survivability mechanisms in weak-communication UUSNs.
KW - Cascading failure
KW - Link success probability
KW - Node self-recovery
KW - Survivability analysis
KW - Underwater unmanned swarm networks (UUSNs)
KW - Weak communication
UR - https://www.scopus.com/pages/publications/105024729666
U2 - 10.1016/j.dt.2025.09.022
DO - 10.1016/j.dt.2025.09.022
M3 - 文章
AN - SCOPUS:105024729666
SN - 2096-3459
VL - 56
SP - 66
EP - 82
JO - Defence Technology
JF - Defence Technology
ER -