UAV-enabled Computing Power Networks: Task Completion Probability Analysis

2025-12-17

AI Summary

This paper addresses the challenge of quantifying task completion capability under end-to-end latency constraints in unmanned aerial vehicle–enabled computing power networks (UAV-CPNs) operating in spatiotemporally dynamic environments. To this end, it introduces “task completion probability” as the core performance metric and establishes a unified analytical framework integrating stochastic processes and stochastic geometry—marking the first joint modeling of communication-computation coupling, UAV mobility, and task offloading. A closed-form expression for the task completion probability is derived analytically. Numerical results demonstrate that wide-area deployment of computing nodes, coupled with coordinated optimization of communication and computation resources, significantly enhances network-wide task completion capability, effectively mitigating both the edge computing island effect and communication bottlenecks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper presents an innovative framework that synergistically enhances computing performance through ubiquitous computing power distribution and dynamic computing node accessibility control via adaptive unmanned aerial vehicle (UAV) positioning, establishing UAV-enabled Computing Power Networks (UAV-CPNs). In UAV-CPNs, UAVs function as dynamic aerial relays, outsourcing tasks generated in the request zone to an expanded service zone, consisting of a diverse range of computing devices, from vehicles with onboard computational capabilities and edge servers to dedicated computing nodes. This approach has the potential to alleviate communication bottlenecks in traditional computing power networks and overcome the "island effect" observed in multi-access edge computing. However, how to quantify the network performance under the complex spatio-temporal dynamics of both communication and computing power is a significant challenge, which introduces intricacies beyond those found in conventional networks. To address this, in this paper, we introduce task completion probability as the primary performance metric for evaluating the ability of UAV-CPNs to complete ground users' tasks within specified end-to-end latency requirements. Utilizing theories from stochastic processes and stochastic geometry, we derive analytical expressions that facilitate the assessment of this metric. Our numerical results emphasize that striking a delicate balance between communication and computational capabilities is essential for enhancing the performance of UAV-CPNs. Moreover, our findings show significant performance gains from the widespread distribution of computing nodes.

Problem

• Analyzes task completion probability in UAV-enabled computing power networks
• Quantifies network performance under spatio-temporal dynamics of communication and computing
• Derives analytical expressions to evaluate end-to-end latency requirements

Innovation

• UAVs as dynamic aerial relays for task outsourcing
• Task completion probability as key performance metric
• Stochastic geometry models for network analysis