Vehicular ad-hoc networks (VANETs) are highly mobile wireless ad hoc networks for vehicular safety and other commercial applications, whereby vehicles move non-randomly along roads while exchanging information with other vehicles and roadside infrastructures. Inter-vehicle communication (IVC) is achieved wirelessly using multihop communication, without access to fixed infrastructure. Rapid movement and frequent topology changes cause repeated link breakages, increasing the packet loss rate. Geographical routing protocols are suitable for VANETs. However, they select the node nearest to the destination node as a relay node within the transmission range, increasing the possibility of a local maximum and link loss because of high mobility and urban road characteristics. We propose a grid-based predictive geographical routing (GPGR) protocol, which overcomes these problems. GPGR uses map data to generate a road grid and to predict the moving position during the relay node selection process. GPGR divides roads into two-dimensional road grids and considers every possible node movement. By restricting the position prediction in the road grid sequence, GPGR can predict the next position of nodes and select the optimal relay node. Simulation results using ns-2 demonstrated performance improvements in terms of local maximum probability, packet delivery rate, and link breakage rate.

Park, J.-K., Heo, J.-H., and Lee, K.-W., 2023. Applicability analysis of drone LiDAR through comparison with manned airborne LiDAR. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 210-214. Coconut Creek (Florida), ISSN 0749-0208. The coastline is a very important dataset that represents the boundaries of the land, and the survey has been made since 2001. KHOA (Korea Hydrographic and Oceangraphic Agency) surveys the change in the coastline and sets the coastline of Korea through the coastline survey project. Drone images and water surveying methods have been used for coastline surveys, and manned airborne LiDAR has recently been applied. In this study, the drone LiDAR's coastline surveying application was to be experimented with and presented the possibility of application. Data was built using drone LiDAR for the test bed used in the coastline survey project, and the accuracy was evaluated by comparing with GNSS surveys. In addition, the comparison between manned airborne LiDAR data and drone LiDAR data was performed. According to the study, the accuracy of the drone LiDAR showed a value within 5 cm, and it was possible to use the coastline survey by satisfying the coastline survey work regulations. The comparison of drone LiDAR and Manned Airborne LiDAR data suggested that the density of drone LiDAR is very high. If you use drone LiDAR data, it will be advantageous for coastline surveying and change detection. Drone LiDAR is expected to be applied to coastline surveys.