Online Task Allocation and Flying Control in Fog-Aided Internet of Drones

Jingjing Yao, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Fog-aided Internet of Drones (IoD) networks employ fog nodes to provide computing resources for the delay-sensitive tasks offloaded from drones. In IoD networks, drones are launched to complete a journey in which several locations of interest are visited. At each location, a drone collects the ground information, generates computing tasks and offloads them to the fog nodes for processing. In our work, we consider both the task allocation (which distributes tasks to different fog nodes) and the flying control (which adjusts the drone's flying speed) to minimize the drone's journey completion time constrained by the drone's battery capacity and task completion deadlines. We formulate this joint optimization problem as a mixed integer non-linear programming (MINLP) problem. In consideration of the practical scenario that the future task information is difficult to obtain, we design an online algorithm to provide strategies for task allocation and flying control when the drone visits each location without knowing the future. The performances of our proposed online algorithm are demonstrated via extensive simulations.

Original languageEnglish (US)
Article number9043589
Pages (from-to)5562-5569
Number of pages8
JournalIEEE Transactions on Vehicular Technology
Volume69
Issue number5
DOIs
StatePublished - May 2020

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Internet of things (IoT)
  • energy consumption
  • flying control
  • fog computing
  • internet of drones (IoD)
  • quality of service (QoS)
  • task allocation
  • unmanned aerial vehicles (UAV)

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