Stochastic Coded Offloading Scheme for Unmanned Aerial Vehicle-Assisted Edge Computing

by   Wei Chong Ng, et al.

Unmanned aerial vehicles (UAVs) have gained wide research interests due to their technological advancement and high mobility. The UAVs are equipped with increasingly advanced capabilities to run computationally intensive applications enabled by machine learning techniques. However, because of both energy and computation constraints, the UAVs face issues hovering in the sky while performing computation due to weather uncertainty. To overcome the computation constraints, the UAVs can partially or fully offload their computation tasks to the edge servers. In ordinary computation offloading operations, the UAVs can retrieve the result from the returned output. Nevertheless, if the UAVs are unable to retrieve the entire result from the edge servers, i.e., straggling edge servers, this operation will fail. In this paper, we propose a coded distributed computing approach for computation offloading to mitigate straggling edge servers. The UAVs can retrieve the returned result when the number of returned copies is greater than or equal to the recovery threshold. There is a shortfall if the returned copies are less than the recovery threshold. To minimize the cost of the network, energy consumption by the UAVs, and prevent over and under subscription of the resources, we devise a two-phase Stochastic Coded Offloading Scheme (SCOS). In the first phase, the appropriate UAVs are allocated to the charging stations amid weather uncertainty. In the second phase, we use the z-stage Stochastic Integer Programming (SIP) to optimize the number of computation subtasks offloaded and computed locally, while taking into account the computation shortfall and demand uncertainty. By using a real dataset, the simulation results show that our proposed scheme is fully dynamic, and minimizes the cost of the network and UAV energy consumption amid stochastic uncertainties.


Optimal Stochastic Coded Computation Offloading in Unmanned Aerial Vehicles Network

Today, modern unmanned aerial vehicles (UAVs) are equipped with increasi...

FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing

Integrating unmanned aerial vehicles (UAVs) into vehicular networks have...

Optimizing Multi-UAV Deployment in 3D Space to Minimize Task Completion Time in UAV-Enabled Mobile Edge Computing Systems

In Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) sys...

UAVs over mmWave/THz Cellular MEC Networks: A Comparative Study for Energy Efficiency

Cellular networks equipped with mobile edge computing (MEC) servers can ...

Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels

With the continuous increment of maritime applications, the development ...

Dynamic Coded Distributed Convolution for UAV-based Networked Airborne Computing

A single unmanned aerial vehicle (UAV) has limited computing resources a...

Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

The fifth and sixth generations of wireless communication networks are e...

Please sign up or login with your details

Forgot password? Click here to reset