Energy-Efficient Information Placement and Delivery Using UAVs

Ahmed A. Al-Habob, Octavia A. Dobre, Sami Muhaidat, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper focuses on minimizing the energy consumption of a fleet of unmanned aerial vehicles (UAVs) disseminating information to a set of Internet-of-Things devices. In the considered scenario, each device wants to download a subset of files from a library of files. Considering the storage capacity of the UAVs, a framework is provided that minimizes energy consumption by optimally selecting the contributing UAVs, placing files, and planning the trajectory of each contributing UAV. In this framework, a combinatorial optimization problem is formulated, which is hard to solve directly for a practical number of devices, files, and/or UAVs. In order to tackle this challenge, we develop three solution approaches, namely a multi-chromosome genetic algorithm, a hybrid genetic-ant colony algorithm, and a genetic algorithm with heuristic file placement. Results show that the proposed solution approaches minimize the total energy consumption and provide near-optimal solutions. Results also illustrate that the proposed framework optimizes the number of UAVs participating in the information delivery mission.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
DOIs
StateAccepted/In press - 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Ant colony optimization (ACO)
  • Autonomous aerial vehicles
  • Energy consumption
  • Genetic algorithms
  • information placement and delivery
  • Internet of Things
  • multi-chromosome genetic algorithm
  • Performance evaluation
  • Servers
  • Trajectory
  • unmanned aerial vehicles (UAVs)

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