Delay-aware and Energy-Efficient Computation Offloading in Mobile Edge Computing Using Deep Reinforcement Learning

Keyphrases

• Mobile Edge Computing 100%
• Energy-efficient Computing 100%
• Computation Offloading 100%
• Delay-aware 100%
• Deep Reinforcement Learning (deep RL) 100%
• Internet of Things Devices 75%
• Edge Server 50%
• Smart City 25%
• Energy Consumption 25%
• Channel Condition 25%
• Energy Minimization 25%
• Internet of Things 25%
• Computational Resources 25%
• Operating Environment 25%
• Internet of Things Applications 25%
• Resource-constrained 25%
• High Demand 25%
• Cloud Resources 25%
• Storage Capacity 25%
• Dynamically Generated 25%
• Promising Applications 25%
• Enabling Platform 25%
• Computational Tasks 25%
• Computing Servers 25%
• Computing Capacity 25%
• Massive Devices 25%
• Smart Transportation 25%
• Multiple Edges 25%
• Reinforcement Learning Approach 25%
• Long-term Utility 25%

Computer Science

• Energy Efficient 100%
• Efficient Computation 100%
• Computation Offloading 100%
• Deep Reinforcement Learning 100%
• Multi-Access Edge Computing 100%
• Internet of Things Device 75%
• Edge Server 50%
• Internet-Of-Things 50%
• Potential Solution 25%
• Channel Condition 25%
• Energy Consumption 25%
• Operating Environment 25%
• Computational Resource 25%
• Learning Approach 25%
• Data Processing 25%
• Smart City 25%
• Computational Task 25%

INIS

• energy 100%
• calculation methods 100%
• learning 100%
• internet 100%
• devices 80%
• resources 60%
• applications 40%
• simulation 20%
• solutions 20%
• cities 20%
• data 20%
• dynamics 20%
• environment 20%
• storage 20%
• processing 20%
• demand 20%
• energy consumption 20%
• capacity 20%
• clouds 20%
• utilities 20%