Ergonomic Opportunistic Networks: A Human-Centric Architecture for Adaptive Resource Orchestration

Abstract

Opportunistic networks acquire external communication, computation, sensing, storage, and service resources as mission requirements evolve. Prior research has developed human-mobility models, context-aware routing, trust mechanisms, and adaptive resource-management approaches, but these contributions address narrower decision scopes and do not by themselves provide an ergonomics-guided process for governing heterogeneous Helpers throughout mission execution. This paper formalises Ergonomic Opportunistic Networks (ErOppNets) as a technology-independent extension of mission-driven opportunistic networks in which ergonomics serves as a design compass for selecting and adapting technologies, policies, resources, and participation mechanisms to support human factors and ergonomics goals alongside mission performance. ErOppNets integrate human-centric, context-aware, trust-aware, and resource-aware decisions across discovery, assessment, admission, role allocation, monitoring, reassessment, reconfiguration, replacement, and release. A multidimensional orchestration model formalises Helper selection, while the ErOppNet Adaptive Orchestration (EAO) procedure translates the model into operational decisions. The evaluation compares six policies across nominal, dynamic, adversarial, resource scarcity, failure, and scalability scenarios using 100 paired random seeds per primary comparative configuration, complemented by separate parameter-sensitivity analyses. Under nominal conditions, EAO and Static Multidimensional (SM) selection achieved 100 per cent mission completion; relative to SM, EAO reduced mean configuration churn by 60.0 per cent. Across dynamic conditions and external populations up to 2000 entities, EAO preserved mission feasibility while balancing utility, stability, trust requirements, and adversarial exposure. The results support ErOppNets as a human-centric, adaptive, and technology-independent model for intelligent opportunistic network management.