A Genetic Algorithm-Based Sparse Coverage over Urban VANETs

Vehicular ad hoc networks have emerged as a promising area of research in academic fields. However, to design a realistic coverage algorithm for vehicular networks presents a challenge due to the irregularity of the service area, assorted mobility patterns, and resource constraints. In order to resolve these problems, this paper proposes a genetic algorithm-based sparse coverage with statistical analysis, which aims to consider the geometrical attributes of road networks, movement patterns of vehicles and resource limitations. By taking the dimensions of road segments into account, our coverage algorithm provides a buffering operation to suit different types of road topology. By discovering hotspots from the historical trace files, our coverage algorithm can depict the mobility patterns and discover the most valuable regions of a road system. We model this resource-constrained problem as an NP-hard budget coverage problem and resolve it by genetic algorithm. The simulation results verify that our coverage is reliable and scalable for urban vehicular networks.