DeepFloat: ResourceEfficient Dynamic Management of Vehicular Floating Content. Opportunistic communications are expected to playa crucial role in enabling contextaware vehicular services. A widely investigated opportunistic communication paradigm for storing a piece of content probabilistically in a geographica larea is Floating Content (FC). A key issue in the practical deployment of FC is how to tune content replication and caching in a way which achieves a target performance (in terms of the mean fraction of users possessing the content in a given region of space) while minimizing the use of bandwidth and host memory. Fully distributed, distancebased approaches prove highly inefficient, and may not meet the performance target,while centralized, modelbased approaches do not perform well in realistic, inhomogeneous settings. In this work, we present a datadriven centralized approach to resourceefficient, QoSaware dynamic management of FC.We propose a Deep Learning strategy, which employs a Convolutional Neural Network (CNN) to capture the relationships between patterns of users mobility, of content diffusion and replication, and FC performance in terms of resource utilization and of content availability within a given area. Numerical evaluations show the effectiveness of our approach in deriving strategies which efficiently modulate the FC operation in space and effectively adapt to mobility pattern changes over time.

Mobile Offloading in Vehicular Networks: A RewardBased Incentive Caching Scheme The massive growth of mobile vehicles arises immense mobile traffic demand on the current cellular communication system. To cope with this challenge, a new transmission pattern that vehicle nodes cache some data contents in advance and provide data access for future requests via direct contacts has become an appealing solution for data offloading. However due to the limited resources, the fact that vehicle nodes might not be willing to assist data caching and transfer is plausible. Therefore in this paper, we build a rewardbased cachingoffloading framework, which efficiently offload cellular traffic by exploiting the storage capability and contact opportunity among vehicle nodes. The whole framework is divided into request phase and caching phase, an auction mechanism which comprises the bidding strategy, the responseforbids strategy, and the prediction method is designed in the former phase and the optimal caching decision can be derived by solving a 0xe2x80x931 knapsack problem in the latter phase. We conduct simulations on timeevolving topology traces, which validates the feasibility and efficiency of the proposed offloading paradigm.