Decentralized unmanned aerial vehicle (UAV) swarms require low-latency peer communication while remaining resilient to spoofing, replay, command injection, key compromise, and malicious membership changes. This study develops a zero-trust communication framework that separates the real-time swarm data plane from a permissioned Byzantine-fault-tolerant trust ledger. The method is grounded in an existing GPS-denied UAV software baseline implementing canonical packet hashing, HMAC-SHA256 authentication, trust epochs, timestamp and sequence freshness checks, onboard security-state transitions, command-policy gating, and firmware trust records. The proposed extension introduces per-node identities, authenticated session establishment, AEAD-protected peer traffic, and event-sparse ledger anchoring for trust-changing evidence. Formal models are derived for message acceptance, trust dynamics, Byzantine tolerance, consensus traffic, storage growth, processing overhead, and energy cost. Under a representative analytical case of 100 swarm messages/s, a 1% anchoring ratio reduces ledger event rate and modeled consensus-control traffic by 100 times compared with per-packet anchoring. Repository benchmark measurements are reported separately from blockchain projections. The analysis supports using blockchain as a decentralized trust anchor rather than as a transport for flight-critical telemetry.
The integration of real-time capacity optimization suites within legacy civil aviation computing ecosystems is highly bottlenecked by the severe structural heterogeneity of distribution data infrastructures. Telemetry and transactional feeds remain highly siloed across disparate legacy Global Distribution System (GDS) alphabetic fields, Low-Cost Carrier (LCC) direct APIs, and non-public multi-alliance loyalty program ledger inventories. This paper presents a sovereign computational architecture engineered to achieve distributed eventual consistency across these fragmented environments. The system introduces an automated Heterogeneous Data Fusion (H-Pipeline) layer that aggregates high-frequency multi-source distribution data streams into a unified, encrypted semantic vector space through specialized vector dimension coalescing protocols operating under strict TLS 1.3 mutual authentication frameworks. To intelligently parse and navigate these multi-source streams, the architecture deploys an asynchronous, cloud-native Large Language Model (LLM) orchestration middleware running entirely within serverless stateless edge containers (AWS Wavelength/Cloudflare Workers meshes). The cloud-native LLM layer is established as an asynchronous predictive semantic router, dynamically identifying macroeconomic anomalies, unexpected capacity imbalances, and transient route volatility without introducing synchronized write-back overhead or data persistence bottlenecks to critical On-Line Transaction Processing (OLTP) reservation threads. Simulation-based performance evaluation utilizing industry-standard benchmark datasets confirms single-digit millisecond failover recovery bounds, a strict 12 ms cross-border fiber pathway propagation convergence limit, and total mitigation of cross-region distributed semantic drift, establishing a robust computational foundation for next-generation asynchronous AI airline operations.