Middle-Order Vehicle-Based Clustering Model for Reducing Packet Loss in Vehicular Ad-hoc Networks

Abstract

Vehicular Ad-hoc NETworks (VANETs) are typically termed as a wireless ad-hoc network that contains extreme node mobility and also the network carries a great significance in various traffic-oriented commercial applications and safety services. Due to its high mobility, routing in VANET has been a challenging work and also proving a higher rate of packet delivery ratio with reduced packet loss has been more important to be considered in route formations. With that note, this paper contributes to developing a clustering model called Middle-Order Vehicle-based Clustering (MOVC) model for managing the frequent topological change and high vehicle mobility, and efficiently handling the typical road traffic scenario. Moreover, the algorithm is intended to maintain the cluster to be constant for managing the vehicles in effective ways and also to provide uninterrupted communication between the vehicles. An algorithm for Effective Cluster Head Election (ECHE) is also derived in this paper for proficiently handling the frequency variation on the highways. Further, the model is simulated and evaluated on the basis of various metrics of VANET routing, specifically packet loss, packet delivery ratio, network lifetime and throughput. The results show that the proposed mechanism outperforms the results of existing models.

Vehicle‐Mounted Self‐Organizing Network Routing Algorithm Based on Deep Reinforcement Learning

“…The protocol is based on a predictable connection concept and uses the history of meeting nodes to determine the best way to route and discard data packets on the network, and this method can effectively improve the transmission efficiency of routing data [22]. David and Vanathi proposed a vehicle-mounted self-organizing network clustering model that reduces data packet loss and uses clustering algorithms to cope with frequent topology changes and high mobility of vehicle networks, manage vehicles in an effective way, and provide intervehicle uninterrupted communication [23]. Meng et al designed a control strategy for official vehicles in the traffic road network to improve the K-means algorithm to make the nodes of the official vehicle network adapt to the route, increase the weight of the backpressure strategy according to traffic pressure conditions, and improve the parameters through optimization the adaptability of official vehicles in the traffic road network [24].…”

Dynamic Vehicular Communication using Gaussian Interpolation of Cluster Head Selection (GI-CHS)

“…Clustering is one among the major classification in energy efficient Vehicular Ad Hoc Networks (VANETs covering vehicle to vehicle and vehicle to infrastructure communication. Clustering is classified according to vehicle position, traffic density and congestion level as a function of mobility [16], [17], [18], [19], [20].…”