Genetic Algorithm for Multi-hop VANET Clustering Based on Coalitional Game

Various applications of intelligent transport systems require road traffic data that can be collected from vehicles and sent over a vehicular ad hoc network (VANET). Due to rapid mobility and limited channel capacity in a VANET, where vehicles must compete to access the roadside units (RSUs) to report their data, clustering is used to create a group of vehicles to collect, aggregate, and transfer data to RSUs acting as sink nodes. Unlike prior works that mostly focus on cluster head selection for prolonging cluster lifetime or maximizing throughput, we applied the coalitional game model to create a multi-hop cluster with the largest possible coverage area for a given transmission delay time constraint to economize the number of RSUs. The coalitional game models the profit and cost of nodes as the utility, which is a weighted function of the coverage area, amount of cluster’s members, relative velocities, distances among nodes, and transmission delay toward the sink nodes. Due to the problem complexity, the genetic algorithm is developed to obtain the model solution. The simulation results reveal that the solution quickly converges within a few generations, where the most suitable structure attains the maximum summation utility from all nodes in the coalition. Additionally, the GA-based solution approach outperforms the brute-force approach in terms of the problem scale, and the coalitional game model yields higher coverage areas compared to those obtained from the non-cooperation model.