A Data Download Method from RSUs Using Fog Computing in Connected Vehicles

Communication is important for providing intelligent services in connected vehicles.Vehicles must be able to communicate with different places and exchange information while driving.For service operation,connected vehicles frequently attempt to download large amounts of data.They can request data downloading to a road side unit(RSU),which provides infrastructure for connected vehicles.The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU.Therefore,it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU.If the mobile network between a connected vehicle and an RSU has poor connection quality,the efficiency and speed of the data download from the RSU is decreased.This problem affects the quality of the user experience.Therefore,it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed.The proposed method maximizes download speed from an RSU using a machine learning algorithm.To collect and learn from network data,fog computing is used.A fog server is integrated with the RSU to perform computing.If the algorithm recognizes that conditions are not good for mass data download,it will not attempt to download at high speed.Thus,the proposed method can improve the efficiency of high speed downloads.This conclusion was validated using extensive computer simulations.

Cluster-Based Content Download and Forwarding Scheme for Highway VANETs

The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high mobility and limited transmission range of vehicles, bringing users poor performance. To address this issue, we exploit the combination of both clustering and carry-and-forward schemes in this paper. Our scheme coordinates the cooperation of multiple infrastructures, cluster formation in the same direction and data forwarding of reverse vehicles to facilitate the target vehicle to download large-size content in dark areas. The process of data dissemination and achievable data download volume are then derived and analyzed theoretically. Finally, we conduct extensive simulations to verify the performance of the proposed scheme. Results show significant benefits of the proposed scheme in terms of increasing data download volume and throughput.

A Fast Insertion Tabu Search with Conflict-Avoidance Heuristic for the Multisatellite Multimode Crosslink Scheduling Problem

An agile earth-observing satellite equipped with multimode cameras capable of transmitting observation data to other satellites is developed to rapidly respond to requests with multiple observation modes.This gives rise to the Multisatellite Multimode Crosslink Scheduling(MMCS)problem,which involves allocating observation requests to agile satellites,selecting appropriate timing and observation modes for the requests,and transmitting the data to the ground station via the satellite communication system.Herein,a mixed integer programming model is introduced to include all complex time and operation constraints.To solve the MMCS problem,a two-stage heuristic method,called Fast insertion Tabu Search with Conflict-avoidance(FTS-C)heuristic,is developed.In the first stage,a conflict-avoidance insertion algorithm is designed to generate a high-quality initial solution by considering the requests transmission and download.Further,the tabu search-based second stage optimizes the initial solution.Finally,an extensive empirical study based on a real-world situation demonstrates that FTS-C can generate a solution with higher quality in less time than other state-of-the-art algorithms and the CPLEX solver.