A vehicular ad-hoc network (VANET) consists of vehicles that form a network without any additional infrastructure, thus allowing the vehicles to communicate with each other. VANETs have unique characteristics, including high node mobility and rapidly changing network topology. Because of these characteristics, routing algorithms based on greedy forwarding such as greedy perimeter stateless routing (GPSR) are known to be very suitable for a VANET. However, greedy forwarding just selects the node nearest to the destination node as a relay node within its transmission range. This increases the possibility of a local maximum and link loss because of the high mobility of vehicles and the road characteristics in urban areas. Therefore, this paper proposes a reliability-improving position-based routing (RIPR) algorithm to solve those problems. The RIPR algorithm predicts the positions, velocities, and moving directions of vehicles after receiving beacon messages, and estimates information about road characteristics to select the relay node. Thus, it can reduce the possibility of getting a local maximum and link breakage. Simulation results using ns-2 revealed that the proposed routing protocol performs much better than the existing routing protocols based on greedy forwarding.

This study proposes a new method to generate a three-dimensional (3D) geometric representation of an indoor environment by refining and processing an indoor point cloud data (PCD) captured through backpack laser scanners. The proposed algorithm comprises two parts to generate the 3D geometric representation: data refinement and data processing. In the refinement section, the inputted indoor PCD are roughly segmented by applying random sample consensus (RANSAC) to raw data based on an estimated normal vector. Next, the 3D geometric representation is generated by calculating and separating tangent points on segmented PCD. This study proposes a robust algorithm that utilizes the topological feature of the indoor PCD created by a hierarchical data process. The algorithm minimizes the size and the uncertainty of raw PCD caused by the absence of a global navigation satellite system and equipment errors. The result of this study shows that the indoor environment can be converted into 3D geometric representation by applying the proposed algorithm to the indoor PCD.