.png){kind=icon}
.png){kind=icon}
Return
UAV-Assisted Task Offloading in Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2024.3488210.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Task offloading can meet users' demands for the latency and energy consumption by offloading tasks from resource-constrained Internet of Things devices to relatively resource-rich edge servers. Traditional task offloading usually makes use of fixed base stations or servers as edge servers. This would lead to limited range of services and increased costs due to large-scale deployment of edge servers. Therefore, deploying unmanned aerial vehicles (UAVs) as mobile edge servers for task offloading in complex terrains (e.g., forest, desert, etc.) is a worthwhile research problem. To this end, this article proposes a UAV-assisted task offloading mechanism. The mechanism aims to minimize the weighted sum of latency and energy consumption through jointly optimizing resource allocation, offloading decision, and UAV trajectory. We first transform the nonconvex optimization problem into convex optimization subproblems to obtain the optimal resource allocation. Second, we use an improved particle swarm optimization algorithm to find the optimal offloading decision. Finally, we present the deep determination policy gradient algorithm to optimize the UAV trajectory which is a kind of deep reinforcement learning algorithm. Through simulation experiments, we show that the proposed mechanism can efficiently reduce the weighted sum of latency and energy consumption.
Keywords:
Autonomous aerial vehicles
Internet of Things
Resource management
Servers
Energy consumption
Costs
Monitoring
Trajectory optimization
Surveys
Prediction algorithms
Deep determination policy gradient (DDPG) algorithm
edge computing
resource allocation
task offloading
unmanned aerial vehicle (UAV) trajectory
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W