Back close

Stochastic traffic flow modeling for multi-hop cooperative data dissemination in VANETs

Publication Type : Journal Article

Publisher : Elsevier

Source : Physical Communication (Q3), (Elsevier), DOI: 2021.101290

Url :

Campus : Chennai

School : School of Engineering

Department : Electronics and Communication

Year : 2021

Abstract : Vehicular networks need to ensure their systems to perform efficient resource allocations to the customer’s need. Vehicular networks have issues like data dissemination, packet loss and delay. Vehicles in the transmission range of a few hundred meters receive the messages in a single-hop scenario. Multi-hop forwarding messages can disseminate from source node to destination node. Due to intense stochastic traffic flow modeling on highways, servers remain busy, leading to a tremendous increase in waiting time, service time, blocking probability, and system response time. The Markov model analyzes the mobility at intersections to provide a solution. The model reduces congestion control and maximizes the network performance in Vehicular Ad-hoc Networks (VANETs). With this optimized network flow control performance, we propose a Multi-hop Cooperative Data Dissemination (MHCDD) based on buffer control, which can be more efficient when applied with a Markov process. We formulate and analyze the delay in the multi-class queueing system, enhancing data traffic forwarding from V2V and RSU or BS. Performance analysis and simulation outcomes show the proposed MHCDD significantly improves the stochastic multi-hop broadcast model. The single-hop end-to-end delay, waiting time, response time, throughput, blocking probability, and packet loss probability are optimized. System modeling improves the queue length, delay, and packet loss of the broadcast scheme’s queue using the IEEE 802.11p system.

Cite this Research Publication : Banoth Ravi, Jaisingh Thangaraj, "Stochastic Traffic Flow Modeling for Multi-hop Cooperative Data Dissemination in VANETs," Physical Communication (Q3), (Elsevier), DOI: 2021.101290

Admissions Apply Now