Traffic and Computation Co-Offloading with Reinforcement Learning in Fog Computing for Industrial Applications

Keyphrases

• Industrial Application 100%
• Reinforcement Learning 100%
• Fog Computing 100%
• Congestion 66%
• Network Data 66%
• Energy Services 33%
• Mobile Environment 33%
• Energy Consumption 33%
• Service Delay 33%
• Rapid Growth 33%
• Processing Time 33%
• Limited Resources 33%
• Decision Problems 33%
• Explosives 33%
• Network Traffic 33%
• Scheduling Algorithm 33%
• Data Communication 33%
• Computation-intensive 33%
• Cost Minimization 33%
• Mobile Services 33%
• Delay Time 33%
• Heterogeneous Networks 33%
• Network Delay 33%
• Device-to-device 33%
• Vehicular Ad Hoc Networks 33%
• Mobile Edge Networks 33%
• Offloading Decision 33%
• Fundamental Tradeoffs 33%
• Automatic Driving 33%
• Mobile Vehicle 33%
• User Willingness 33%
• Markov Decision 33%
• Consumption Delay 33%
• Content-centric Network 33%
• Network Processing 33%
• Network Computation 33%
• Mobility Trajectories 33%
• Dynamic Scheduling Algorithm 33%

Computer Science

• Reinforcement Learning 100%
• Fog Computing 100%
• Industrial Applications 100%
• Scheduling Algorithm 66%
• Vehicular Network 33%
• Intensive Computation 33%
• Network Traffic 33%
• Mobile Environment 33%
• Decision Problem 33%
• Data Communication Network 33%
• Heterogeneous Network 33%
• Device-To-Device 33%
• mobile service 33%
• Energy Consumption 33%
• Processing Time 33%
• Minimization Problem 33%