A Controllable RSU and Vampire Moth to Support the Cluster Stability in VANET

Abstract

VANET has a dynamic topology that affects cluster formation stability. It influences vehicle’s network quality though supporting this stability requires a fast and small cluster formation process. It is necessary because of the rapidly changing condition of the vehicle's position. Moreover, small cluster groups make network quality more evenly distributed among its members. They are essential components in the formation of good clusters in VANET. In the previous research, the algorithm used for this process is based on moth flame optimization. This study proposes modifications in this algorithm to speed up the convergence process, facilitated by a model of turning angles in the moth. Furthermore, the flame at the destination moth is created dynamically to approach the real conditions in VANET. The use of coefficients is further introduced to reduce cluster size according to the traffic conditions. The cluster management process uses a fully controlled Road Side Unit on the vehicle’s traffic conditions. In the final result, the convergence time is slightly faster, with better results in the throughput at 101.1%, the number of clusters at 58.1%, and the delay at 5.5%, respectively.

EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET

“…Few studies in the literature addressed RSU deployment, but the RSU is a component of VANET and is critical, as we have developed in our work. Te actual VANET network needs RSU communication too [29].…”

Continuous Integration/ Continuous Delivery Optimization on Network Automation using Gray Wolf Optimizer

“…Pemilihan algoritme GWO ini juga telah melewati beberapa tahap pengujian sehingga diperoleh bahwa algoritme GWO memiliki kecepatan penemuan nilai fungsi tujuan yang terbaik. Pengujian pembandingan metode ini melibatkan algoritme metaheuristik lainnya seperti algoritme Particle Swarm Optimization (PSO) [6], BAT [7], Moth Flame Optimization (MFO) [8], dan Modified Moth Flame Optimization (MMFO) [9].…”

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

“…In VANETs, vehicles have onboard sensing systems and transceivers that facilitate V2V communication directly, which allows real time exchange of important information with low latency independent of road side infrastructure. However, under certain conditions, V2V communication requires road side infrastructure to enable other safety, mobility and environmental supply of data to the travelling vehicles, provided through road side units (RSUs), thus forming vehicle to infrastructure communication (V2I) [11], [12], [13], [14], [15].…”