Q-Learning Based Point to Point Data Transfer in Vanets

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

“…Finally, the destination node sends back the message with the route using unicast communication. In addition, there are some reactive protocols based on Artificial Intelligence (AI) and Reinforcement Learning (RL), such as Point to Point Ad-Hoc On-Demand Vector (PP-AODV) [74], Portable Fuzzy constraints Q-learning AODV (PFQ-AODV) [75], Adaptive Routing Protocol based on RL (ARPRL) [76], Reliable Self-Adaptive Routing (RSAR) [77], Q-Learning-based AODV for VANET (QLAODV) [78], Practical and Intelligent Routing protocol for VANET (PIRP) [79], and Heuristic Q-learning based VANET Routing (HQVR) [80].…”

Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks

“…• We propose hybrid infrastructure based traffic aware routing algorithm (HI-TAR), as a new hybrid routing technique that chooses the best V2V/I path to the destination accordingly, which can improve the APDR by up to 50% when compared to I-TAR as well as QTAR. • We provide performance analyses of the proposed hybrid routing scheme, HI-TAR, and compare it with I- Themes of Contributions HI-TAR I-TAR QTAR [10] iCar-II [13] PP-AODV [18] QGRID [16] PFQ-AODV [19] QL-AODV [15] GyTAR [20] A…”

Intelligent Processing in Vehicular Ad hoc Networks: a Survey

“…Using fuzzy logic for QoS guarantee in VANETs [30,35,[49][50][51][52][53][54][55][56][57][58][59][60][61][62] is a hot topic in recent years.…”

“…[63] focus on Sybil attack in VANETs, use fuzzy logic to form a robust detection mechanism. This work use the concept Fuzzy Logic V2V Improve the efficiency of the end-to-end communication Achieve an efficient use of wireless resources [35] Fuzzy Logic and Q learning V2V Cellular network is not sufficient due to its limited bandwidth Improve throughput in high-density scenarios [49] Fuzzy Logic V2I Selecting gateway candidate Better performance in delay and packet loss [52] Fuzzy Logic V2V Minimizing control plane modifications Reduce overhead [52] Fuzzy Logic V2V,V2I Emergency messages face a poor performance Reduces the congestion and increases the information accuracy [55] Fuzzy Logic V2V Security threats Guarantee road safety service quality [57] Data Mining V2V,V2I Fair channel allocation schemes among vehicles Improve transmission reliability and security [58] Fuzzy Logic V2V,V2I Improve IEEE802.11p Improve the routing performances in the network [59] Q-Learning V2V Multi hop communication in VANETs Good performance in packet delivery ratio, end to end delay and overhead [60] The mathematical model of normal distribution V2V,V2I…”