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 language | English (US) |
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Pages (from-to) | 1 |
Number of pages | 1 |
Journal | IEEE Internet of Things Journal |
DOIs | |
State | Accepted/In press - 2022 |
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)