Machine learning based cluster formation in vehicular communication

Bhatti, Dost Muhammad Saqib; Rehman, Yawar; Rajput, Prem Singh; Ahmed, Saleem; Kumar, Pardeep; Kumar, Dileep

doi:10.1007/s11235-021-00798-7

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…Spectrum scarcity issue in vehicular communication can be addressed by incorporating cognitive radio spectrum sensing model. For maximizing throughput in VANET communication, optimistic spectrum sensing model is introduced in [4] that senses an optimal vehicle and machine learning approach is used for clustering the nodes to reduce communication burden. Combining VANET and internet of things (IoT) suggests internet of vehicle (IoV) and trust management is one of the main challenge in vehicular communication it plays vital role for providing security which needs responsive and adaptive methods to confirm security.…”

Section: Introductionmentioning

confidence: 99%

Developing a pervasive edge computing environment for Vehicular Communication using modified Reinforcement Learning in Routing and Dynamic Traffic Flow Prediction

Alqahtani,

Ramakrishnan,

Saravanan

et al. 2023

Preprint

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Vehicular networking in smart autonomous connected vehicle communications evolved with high mobility and due to high dynamics in an urban environment, new challenges are addressed by academicians and researchers for providing better support. Dynamic changes of vehicular nodes position, routing in Vehicular Adhoc Network (VANET) using existing traditional networking routing algorithms may not provide optimal solution for efficient communication. Also predicting or forecasting traffic flow in VANET can be improved through sharing traffic information in real-time using intelligent transportation systems. In this paper we proposed modified reinforcement learning algorithm that supports for optimal route identification for dynamically disconnected vehicles in urban environment by considering its previous state and predicts flow of traffic generated by vehicles in various time interval. Experimental result shows better performance in routing parameters like packet delivery ratio, routing overhead, latency and predicting traffic flow by proposed algorithms achieves significant accomplishments comparing to existing algorithms.

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Section: Introductionmentioning

confidence: 99%