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

4 Scopus citations

Abstract

This article 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 multichromosome genetic algorithm (GA), a hybrid genetic-ant colony algorithm, and a GA 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)357-366
Number of pages10
JournalIEEE Internet of Things Journal
Volume10
Issue number1
DOIs
StatePublished - Jan 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

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

Keywords

  • Ant colony optimization (ACO)
  • information placement and delivery
  • multichromosome genetic algorithm (GA)
  • unmanned aerial vehicles (UAVs)

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