Machine learning based code dissemination by selection of reliability mobile vehicles in 5G networks Abstract   Recently, the evolving of 5G networks is foreseen as a major driver of future mobile vehicular social networks (VSNs), which can provide a novel method of code disseminations. Based on this concept, vehicles can be used as code disseminators. That is, infrastructures of a smart city can be upgraded by receiving updated program codes that are disseminated by vehicles in the VSNs. Specifically, vehicles in the 5G network are hard to be managed. Under this domain, safety of program codes is a key challenge. Meanwhile, improving coverage of program codes is also challenging. However, arranging plenty of vehicles as code disseminators will incur large costs of the ground control station (GCS). Therefore, by utilizing machine learning methods, this paper proposes a xe2x80x9cMachine Learning based Code Dissemination by Selecting Reliability Mobile Vehicles in 5G Networksxe2x80x9d (MLCD) scheme to choose vehicles with higher reliable degree and coverage ratio as code disseminators to deliver code with lower costs. Firstly, reliable degrees of vehicles are calculated and selected to improve safety degree of code disseminations. Secondly, vehicles with higher coverage ratio are preferred to promise code coverage. Thirdly, machine learning methods are utilized to select vehicles with both higher coverage ratios and reliable degrees as code disseminators with limited costs. Compared to randomselection and coverageonly scheme respectively, the MLCD scheme can improve safety degree of code dissemination process by 83.6% and 18.86% in 5G networks, and can improve coverage ratio of updated information by 23.16%. Comprehensive performances of the proposed scheme can be improved by 80.56% and 17.25% respectively. Future works focus on improving code security in 5G networks by more advanced and suitable machine learning methods.

Largevolume data dissemination for cellularassisted automated driving with edge intelligence Abstract   Automated driving emerges as a potential technology for safe, convenient, and efficient transportation systems. In order to assist autonomous vehicles with largevolume data dissemination such as high definition (HD) map, cellular network becomes more inevitable to provide vehicles with the ability of accessing Internet. However, due to the bottleneck of wireless bandwidth and the high mobility of vehicles, efficient data dissemination assisted by cellular network is a challenging task. In this work, we investigate the largevolume data dissemination problem for cellularassisted automated driving within given delay limit. Firstly, a data dissemination scheme is designed that transmits data from the base stations to the target vehicle via a collection of relay vehicles. Secondly, a novel user satisfaction model is derived by considering the vehicular mobility patterns and erasure coding, and the data dissemination is formulated into an optimization problem. Then, to solve this NPhard problem, a lowcomplexity dynamic programming based data dissemination solution with edge intelligence is proposed. Finally, by carrying out extensive simulation, the effectiveness of proposed data dissemination scheme for cellularassisted automated driving is demonstrated, in comparison with the optimal solution based on CPLEX as well as other conventional schemes.