Traditional key agreement-based encryption mechanisms in public security communications struggle to address increasingly sophisticated network attacks and real-time threats due to infrequent key updates and exposure risks. To overcome these limitations, this study proposes a Chaotic Encryption-based Tunneling Method (CETM) at the network layer. CETM integrates a six-dimensional (6D) hyperchaotic system with the AES-256 algorithm, leveraging the system’s extreme sensitivity to initial conditions to generate high-strength key sequences, eliminating the need for traditional negotiation. A self-synchronizing key update mechanism is also introduced, allowing both parties to update keys automatically without network transmission, thereby eliminating the risk of key leakage. The proposed system demonstrates strong complexity and randomness, as validated by multiple chaotic metrics including the Lyapunov exponent, Shannon entropy, correlation coefficient, permutation entropy, and key space. The generated keys pass both the SP800-22 and FIPS 140-2 tests, meeting established security standards. Performance evaluations across local, cloud, and satellite environments show that CETM significantly outperforms conventional methods in terms of bandwidth efficiency, latency, jitter, and packet loss.

In heterogeneous wireless emergency networks, network fault diagnosis plays a critical role in ensuring reliable and secure communication. To improve network transmission quality, the complexity of network equipment—both in hardware and software design—has increased, which inevitably gives rise to equipment failures with complex root causes, significantly elevating the difficulty of fault diagnosis. Current fault diagnosis algorithms are inadequate for addressing the challenges in fault diagnosis of complex emergency access networks, primarily due to their high diagnostic costs and low accuracy. In this study, we first propose a diagnosis framework and a Deterministic Fault Propagation (DFP) model, and a Hierarchical Fault Diagnosis Framework. Second, we develop three algorithms to construct a Fault Cause Relationship Graph, which supports identifying the logical relationships among various fault causes associated with a specific fault. Third, we propose a Fault Diagnosis algorithm based on Relational Graph Inference (FDRGI). Finally, we conduct extensive experiments in real-world wireless access networks. The experimental results demonstrate that our algorithm satisfies the requirements for root cause diagnosis of access failures in emergency networks, and outperforms other comparative algorithms in terms of diagnostic cost and accuracy: it reduces the average diagnostic cost by 13.71%-69.88% and improves the average diagnostic accuracy by 39.06%-1.98-fold.

Multipath cooperation technology alleviates transmission pressure by leveraging path diversity. However, in next-generation service-oriented environments with computation-intensive services, the limited computing capability of transmission paths can degrade end-to-end service quality, even when bandwidth is sufficient. This issue becomes more pronounced in dynamic mobile scenarios, where fluctuating link status and computational resources introduce new challenges in path selection. To address these challenges, we propose a novel path selection approach that jointly considers both network and computation constraints for computation-intensive services. First, we construct a computation-integrated multipath transmission framework to support real-time monitoring of both link-level computing capabilities and network conditions. Second, we introduce a packet structure embedding device identifiers and computing capability, enabling adaptive scheduling. Finally, we develop a computing capability-constrained delay-minimizing packet scheduler (C2-DMPS) to balance bandwidth and computational load, ensuring low-latency transmission for emerging service demands. The results demonstrate the critical role of computational capacity in maintaining service performance, especially under volatile network conditions, highlighting potential risks to service continuity in next-generation environments.