Zhou, Bingnan (University of California San Diego), Xie, Junfei (San Diego State University), Wang, Baoqian (Texas A&M University-Corpus Christi)

Dynamic Coded Distributed Convolution for UAV-Based Networked Airborne Computing

Scheduled for presentation during the Regular Session "UAS Applications II" (ThB5), Thursday, June 23, 2022, 16:50−17:10, Elafiti

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

A single unmanned aerial vehicle (UAV) has limited computing resources and battery capacity, making it difficult to handle computationally intensive tasks such as the convolution operations in many deep learning applications. UAV-based networked airborne computing (NAC) is a promising technique to address this challenge. It allows UAVs within a range to share resources among each other via UAV-to-UAV communication links and carry out computation-intensive tasks in a collaborative manner. This paper investigates the vector convolution problem over the NAC architecture. A novel dynamic coded convolution strategy with privacy awareness is developed to address the unique features of UAV-based NAC, including node heterogeneity, frequently changing network typologies, time-varying communication and computation resources. Simulation results show its high efficiency and resilience to uncertain stragglers.