A novel energy-efficient and cost-effective task offloading approach for UAV-enabled MEC with LEO enhancement in Internet of Remote Things networks

Computer Science

• Unmanned Aerial Vehicle 100%
• Energy Efficient 100%
• Multi-Access Edge Computing 100%
• Task Offloading 100%
• Decision-Making 66%
• Preference Order 33%
• Energy Consumption 33%
• Energy Efficiency 33%
• Process Data 33%
• Scheduling Algorithm 33%
• Conventional Method 33%
• Deep Q-Network 33%
• Static Routing Algorithm 33%
• Collecting Data 33%
• Dynamic Decision 33%
• Layer Architecture 33%
• Isolated Environment 33%
• Harsh Environment 33%
• Computational Capacity 33%