Swarm Network-as-a-Service (SNaaS)

2026-05-13

AI Summary

This work proposes a service-oriented unmanned aerial vehicle (UAV) swarm networking framework to address the stringent service-level agreement (SLA) requirements in emerging on-demand connectivity scenarios. For the first time, the service-oriented paradigm is introduced into UAV networks, modeling interactions among UAVs and between UAVs and ground devices as composable atomic and composite services. The framework integrates service providers, consumers, and a registry within a software-defined networking (SDN) architecture and employs three dynamic composition strategies. An adaptive service selection and reconfiguration mechanism is designed by combining a queueing-theory-inspired heuristic algorithm with real-time SLA monitoring. Experimental results demonstrate that, compared to fixed composition approaches, the proposed framework significantly reduces latency and SLA violations under increasing network load and swarm size, while enabling smoother dynamic adaptation.

Abstract

Emerging on-demand connectivity scenarios increasingly require networking solutions with stringent service-level guarantees. We propose Swarm Network-as-a-Service (SNaaS), a service-oriented framework that leverages fleets of drones to provide on-demand connectivity at scale. SNaaS explicitly models drone-to-device and drone-to-drone interactions as composable services, enabling consumers to request connectivity through Service-Level Agreements (SLAs). We formalize atomic and composite SNaaS services, present an SDN-inspired architecture that integrates the service-oriented triad of provider, consumer, and registry. We introduce a composition framework that orchestrates drones into end-to-end services. Within this framework, we define and analyze three composition strategies, i.e., direct, clustered, and parallel, and propose a queuing-theory-based heuristic for selecting the most suitable strategy under varying load conditions. A dedicated enforcement module continuously monitors queue stability and SLA latency, adaptively reconfiguring the swarm when violations occur. Experiments using real air-to-ground measurements show that the framework consistently outperforms fixed compositions, achieving lower latency, fewer SLA violations, and smoother adaptation as load and swarm size increase.

Problem

• on-demand connectivity
• service-level guarantees
• drone networks
• SLA
• networking

Innovation

• Swarm Network-as-a-Service
• service composition
• drone networking
• SLA-aware orchestration
• queueing-theory-based heuristic