Hierarchical Q-learning-enabled neutrosophic AHP scheme in candidate relay set size adaption in vehicular networks

Keyphrases

• Vehicular Ad Hoc Networks 100%
• Set Size 100%
• Hierarchical Q-learning 100%
• Neutrosophic AHP 100%
• Cooperative Transmission 66%
• Probability-based 66%
• Threshold Method 66%
• Fixed-length 66%
• Analytic Hierarchy Process 66%
• Transmission Cost 66%
• Threshold Probability 66%
• Size Threshold 66%
• Neutrosophic Set 66%
• Redundancy 33%
• Processing Techniques 33%
• Reward Function 33%
• Performance Efficiency 33%
• Multiple Vehicles 33%
• Delivery Ratio 33%
• NS-2 33%
• Multiple Relays 33%
• Single-hop 33%
• Data Packet 33%
• Data Redundancy 33%
• Packet Delivery Ratio 33%
• Data Delivery 33%
• Transmission Process 33%
• Vehicular Environment 33%
• Vehicle Velocity 33%
• Comprehensive Simulation 33%
• Data Packet Transmission 33%
• Preference Value 33%
• Duplicate Data 33%
• Urban Scenario 33%
• Q-learning Algorithm 33%
• Broadcast Scheme 33%
• Pairwise Comparison Matrix 33%
• Set Stability 33%
• Redundancy Rate 33%
• Transmission Throughput 33%
• Highway Scenario 33%
• Opportunistic Broadcast 33%
• High Delay 33%
• Q-learning Method 33%

Computer Science

• Vehicular Network 100%
• Analytic Hierarchy Process 100%
• Analytical Hierarchy Process 100%
• Relative Preference 100%
• Neutrosophic Sets 100%
• Probability 100%
• Decision-Making 50%
• Learning Algorithm 50%
• Learning Approach 50%
• End-to-End Delay 50%
• Vehicular Environment 50%
• Data Redundancy 50%
• Packet Delivery Ratio 50%
• Transmission Process 50%
• Packet Transmission 50%
• Including Restriction 50%
• Duplicate Data 50%
• Synchronism 50%
• Topology 50%

Engineering

• Q-Learning 100%
• Cooperative Transmission 66%
• Data Packet 66%
• Learning Approach 33%
• Learning Algorithm 33%
• Multiple Relay 33%
• Data Delivery 33%
• Packet Transmission 33%