Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach

IEEE Access

卷:9

Maksim Jenihhin¹  Yannick Le Moullec²  Muhammad Mahtab Alam²  Dadmehr Rahbari² 

[1] Department of Computer Systems, Tallinn University of Technology, Tallinn, Estonia;

[2] Thomas Johann Seebeck Department of Electronics, School of Information Technologies, Tallinn University of Technology, Tallinn, Estonia;

关键词: Swarm of drones;    multi-access edge computing;    collaborative computing;    federated learning;   

DOI  :  10.1109/ACCESS.2021.3104117

来源: DOAJ

【 摘 要 】

Today, unmanned aerial vehicles (UAVs) or drones are increasingly used to enable and support multi-access edge computing (MEC). However, transferring data between nodes in such dynamic networks implies considerable latency and energy consumption, which are significant issues for practical real-time applications. In this paper, we consider an autonomous swarm of heterogeneous drones. This is a general architecture that can be used for applications that need in-field computation, e.g. real-time object detection in video streams. Collaborative computing in a swarm of drones has the potential to improve resource utilization in a real-time application i.e., each drone can execute computations locally or offload them to other drones. In such an approach, drones need to compete for using each other’s resources; therefore, efficient orchestration of the communication and offloading at the swarm level is essential. The main problem investigated in this work is computation offloading between drones in a swarm. To tackle this problem, we propose a novel federated learning (FL)-based fast and fair offloading strategy with a rating method. Our simulation results demonstrate the effectiveness of the proposed strategy over other existing methods and architectures with average improvements of −23% in energy consumption, −15% in latency, +18% in throughput, and +9% in fairness.

【 授权许可】

Unknown