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 language | English (US) |
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Pages (from-to) | 357-366 |
Number of pages | 10 |
Journal | IEEE Internet of Things Journal |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2023 |
Externally published | Yes |
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)