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A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned
Rico Valentino1, Woo-Sung Jung2, Young-Bae Ko3
1Department of Computer Engineering, Ajou University, Suwon 16499, Korea. ricovalentino94@ajou.ac.kr.
Sensors (Basel, Switzerland)
|November 8, 2018
Summary
This study introduces an opportunistic computational offloading system for drone clusters. By intelligently borrowing resources, drone swarms can significantly reduce task completion times and improve efficiency.
Area of Science:
- Computer Science
- Robotics
- Artificial Intelligence
Background:
- Drones are increasingly used in military and civilian sectors for complex tasks.
- Drone clusters offer advantages in coverage, flexibility, and reliability.
- Limited onboard computing power and energy resources pose challenges for drone task completion.
Purpose of the Study:
- To propose an opportunistic computational offloading system for drone clusters.
- To address the challenge of limited resources in drone swarms.
- To enable drone clusters to complete tasks more efficiently.
Main Methods:
- Developed an opportunistic computational offloading scheme for drone clusters.
- Integrated an artificial neural network (ANN) for response time prediction.
- Offloading decisions are made based on predicted offloading response time versus local computing time.
Main Results:
- The proposed scheme allows drone clusters to borrow computing resources opportunistically.
- The ANN-based prediction module accurately estimates offloading response times.
- Simulation results demonstrate a decrease in drone cluster response times.
Conclusions:
- Opportunistic computational offloading is an effective strategy for enhancing drone swarm performance.
- ANN-based prediction enhances the decision-making process for offloading.
- The system improves the efficiency and timeliness of complex drone operations.
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