AC-RDV: a novel ant colony system for roadside units deployment in vehicular ad hoc networks

Abstract

Vehicular ad hoc network (VANET) is a mobile and wireless network that consists of connected vehicles, and stationary nodes called roadside units (RSUs) placed on the aboard of roads to improve traffic safety and to ensure drivers' and passengers' comfort. However, deploying RSUs is one of the most important challenges in VANETs due to the involved placement, configuration, and maintenance costs in addition to the network connectivity. This study focuses on the issue of deploying a set of RSUs that is able to maximize network coverage with a reduced cost. In this paper, we propose a new formulation of RSUs deployment issue as a maximum intersection coverage problem through a graph-based modeling. Moreover, we propose a new bio-inspired RSU placement system called Ant colony optimization system for RSU deployment in VANET (AC-RDV). AC-RDV is based on the idea of placing RSUs within the more popular road intersections, which are close to popular places like touristic and commercial areas. Since RSU deployment problem is considered as NP-Hard, AC-RDV inspires by the foraging behavior of real ant colonies to discover the minimum number of RSU intersections that ensures the maximum network connectivity. After a set of simulations and comparisons against traditional RSU placement strategies, the results obtained showed the effectiveness of the proposed AC-RDV in terms of number of RSUs placed, the average area coverage, the average connectivity and the overlapping ratio.

Multiobjective Optimization Management of Construction Engineering Based on Ant Colony Algorithm

“…After the number of cycles reaches the preset value, the relative optimal solution is found in the generated solution set, so as to determine the global optimal solution of the original problem. Since the solutions obtained in the preliminary cycle are the optimal solutions in the corresponding field rather than the global optimal solutions, it is necessary to compare each solution to screen the global optimal solutions [20,21]. e specific algorithm flow is shown in Figure 2.…”