Design of an Adaptive Edge-Computing Architecture for Offline Digital Payment Authentication

Introduction: This study presents the design and development of an adaptive edge-computing architecture that enables offline digital payment authentication through localized trust computation, hierarchical caching, and embedded security modules. The system integrates a multi-layer authentication protocol that operates within edge gateways, combining a context-aware trust engine with lightweight cryptographic handshake sequences based on elliptic curve signatures and symmetric key synchronization.
Methodology: The study employed a hybrid hardware–software model, where Raspberry Pi 5 nodes served as edge authenticators connected to mobile point-of-sale (mPOS) terminals through a local mesh network. A reinforcement-based synchronization algorithm dynamically determines when cached credentials should be revalidated with the cloud once connectivity is resumed. 
Result: Experimental evaluation across three simulated rural networks demonstrated a 43% reduction in transaction latency, a 62% improvement in offline authentication success rate, and 99.1% data consistency upon resynchronization. Power efficiency analysis also revealed a 27% lower energy footprint compared with existing mobile payment modules relying on persistent online verification.
Conclusion: The findings establish that integrating adaptive edge intelligence into payment authentication pipelines can significantly enhance transaction continuity and energy performance in bandwidth-constrained or disaster-prone regions. The proposed architecture thus offers a sustainable pathway for inclusive financial digitalization, particularly in underbanked and infrastructure-limited areas.