Optimal Roadside Units Placement in Urban Areas for Vehicular Networks Baber Aslam, Faisal Amjad and Cliff C. Zou University of Central Florida, Orlando, FL, USA {ababer, czou}@eecs.ucf.edu, faisal@knights.ucf.edu Abstract— The most important component of a vehicular ad hoc network (VANET), besides VANET-enabled vehicles, is roadside units (RSUs). The effectiveness of a VANET largely depends on the density and location of these RSUs. During the initial stages of VANET, it will not be possible to deploy a large number of RSUs either due to the low market penetration of VANET-enabled vehicles or due to the deployment cost of RSUs. There is, therefore, a need to optimally place a limited number of RSUs in a given region in order to achieve maximum performance. In this paper, we present two different optimization methods for placement of a limited number of RSUs in an urban region: an analytical Binary Integer Programming (BIP) method and a novel Balloon Expansion Heuristic (BEH) method. BIP method utilizes branch and bound approach to find an optimal analytical solution whereas BEH method uses balloon expansion analogy to find an optimal or near optimal solution. Our evaluations show that both methods perform optimally or near optimally compared with the exhaustive method. Further, BEH method is more versatile and performs better than BIP method in terms of computational cost and scalability. Keywords-VANET; roadside unit; initial deployment stage; optimization; placement; urban areas I. INTRODUCTION A vehicular ad hoc network (VANET) relies on three types of communication for its setup and provision of services: vehicle to vehicle (V2V) communication, vehicle to infrastructure (V2I) communication and infrastructure to infrastructure (I2I) communication. All VANET applications depend on either one or more of these communication types. V2V communication depends on the number and location of vehicles, V2I communication depends on the number and location of roadside units (RSUs) and I2I communication depends on availability of interconnecting network between RSUs. During the initial deployment stages of VANET, there will be very small number of vehicles and RSUs due to the low market penetration of VANET-enabled vehicles or due to the deployment cost of RSUs. Given a small number of RSUs, there is, therefore a need to optimally place these RSUs in a given region/scenario in order to achieve maximum performance. Information flow in most VANET applications is either from vehicles to infrastructure or from infrastructure to vehicles. Our focus, in this paper, is on applications that depend on information flow from vehicles to infrastructure (or RSUs), such as collection of information from vehicles about traffic/road conditions, traffic accidents, etc. We present two different solutions to the RSUs placement problem with objective of maximizing the information flow from vehicles to RSUs in an urban environment: Binary Integer Programming (BIP) method and a novel Balloon Expansion Heuristic (BEH) method. BIP method utilizes branch and bound method to find optimal solution, whereas, BEH method uses balloon expansion analogy to find optimal solution. We have incorporated the vehicle density, vehicle speed, and the occurrence likelihood of an incident/event in our optimization schemes. The optimization aims at minimizing the reporting time for a given number of RSUs; reporting time is defined as the time duration from occurrence of an event till it is reported by a vehicle to an RSU. The RSUs are assumed to be interconnected. Our proposed optimization schemes can easily be extended to applications that depend on information flow from infrastructure to vehicles where the optimization goal can be area covered within some reporting time bounds. Our contributions in this paper include: 1) study of optimization problem in context of VANET applications that depend on flow of information from vehicles to roadside units in an urban environment, 2) modeling of optimization problem with the objective of minimizing average response time, 3) presentation of two optimization methods (BIP method and BEH method) and formalization of problem into these two optimization methods, and 4) analysis of the two presented optimization methods. The rest of the paper is organized as follows. Section II discusses the optimization problem modeling, section III presents our proposed optimization schemes, section IV gives the results and discussion, section V discusses the related work and finally section VI presents conclusion and future work. II. OPTIMIZATION PROBLEM MODELING A. System Model The scope of this paper is restricted to urban environment such as the one shown in Fig. 1. Fig. 1(a) shows a partial map of Miami, FL, USA. The map shows a grid of major roads (shown in yellow and red color) and a number of smaller/local streets. The major roads are shared by all users/buses for commuting whereas the smaller streets are used only by users who need to visit a particular home or business on that street. The traffic on smaller streets is therefore very small/negligible as compared to that on major roads and we can safely ignore these for our system model. Fig. 1(a) can be approximated to a grid network of roads as shown in Fig. 1(b) after removing the local/smaller streets. Consider the road network (shown in Fig. 1(b)) as a graph with each intersection as a vertex and each road segment as an edge. V is set of all vertices, let i  V (or v i 