A Roadside Unit Deployment Optimization Algorithm for Vehicles Serving as Obstacles

As an important direction of topology management and infrastructure construction in Internet of Vehicles (IoV), the problem of roadside unit deployment has been discussed a lot. Considering the problem of communication occlusion caused by mobile vehicles, a novel multi-objective optimization problem of roadside unit deployment under the constraints of target road coverage and communication reliability is proposed in this paper. Firstly, the traffic flow model of the vehicle is introduced, and the channel model considering the occlusion of a mobile vehicle is proposed by a practical two-ray model and knife-edge diffraction model. Then, on the basis of analyzing the difficulty of the problem, an Improved Artificial Bee Colony algorithm based on Neighborhood Ranking (NR-IABC) and a Greedy Heuristic (GH) algorithm are proposed to approximately solve the problem. The NR-IABC algorithm applies the “Neighborhood Ranking” method to reduce the search domain, and then to further reduce the solution time. In order to avoid a local optimum, the sensitivity and pheromone are used as the selection strategy to replace the traditional roulette selection method in the NR-IABC algorithm. In addition, the mutual attraction between bees is involved in the neighborhood search of the following bees, and a new nectar source is generated according to the reverse learning strategy to replace the worst nectar source at the end of each iteration. Finally, results of comparative simulations based on real-life datasets show that the NR-IABC-based solution can always deploy fewer RSUs, and thus is more cost-effective compared with the GH-based solution.