To cope with explosive traffic demands on current cellular networks of limited capacity, Disruption Tolerant Networking (DTN) is used to offload traffic from cellular networks to high capacity and free device-to-device networks. Current DTN-based mobile data offloading models are based on simple and unrealistic network assumptions which do not take into account the heterogeneity of mobile data and mobile users. We establish a mathematical framework to study the problem of multiple-type mobile data offloading under realistic assumptions, where (i) mobile data are heterogeneous in terms of size and lifetime; (ii) mobile users have different data subscribing interests; and (iii) the storages of offloading helpers are limited. We formulate the objective of achieving maximum mobile data offloading as a submodular function maximization problem with multiple linear constraints of limited storage, and propose three algorithms, suitable for the generic and more specific offloading scenarios, respectively, to solve this challenging optimization problem. We show that the designed algorithms effectively offload data to the DTN by using both the theoretical analysis and simulation investigations which employ both real human and vehicular mobility traces.

Modeling complicated vehicular mobility behavior and the associated network connectivity in large scale vehicular ad hoc network has been a challenging problem for the past several years. In this paper, we carry out extensive experiments involving thousands of operational taxies in Shanghai and Beijing cities. Studying the taxi traces on the vehicular connectivity, we observe that the vehicle isolation probability exhibits an exponential decay with the increase of transmission range. This observation is in sharp contrast to the most widely used system models such as Random Way Point and Random Walk, where the vehicle isolation probability changes with transmission range as exp(-λR2), where R is the transmission range. We propose a simple theoretical model to explain the exponential decay of vehicle isolation. Validation of our model is carried through comparisons between the synthetic trace and the real trace, and the results show that our mobility model has a good approximation to the real scenario. Our results give insights on the behavior of connectivity, thus providing fundamental guidelines on design of new vehicular mobility models, new data forwarding protocols and their performance analysis.

Delay Tolerant Networks (DTNs) are able to provide communication services in challenged networks where the end-to-end path between the source and destination does not exist. In order to increase the probability of delivery, DTN routing mechanisms may require nodes in the network to store and carry messages in their local buffer and replicate many copies. When the limited buffer is consumed, choosing the appropriate messages to drop is critical to maximizing the system performance. Current methods for this are sub-optimal or assume unrealistic conditions. In this paper, we propose an adaptive optimal buffer management scheme for DTN in the situations where the bandwidth is limited and messages vary in size. In our proposal, the mobility model is adjusted according to the nodes' historical meeting information, and the message dropping policies are designed to optimize certain network performance goals, such as maximizing the average delivery rate or minimizing the average delivery delay. Furthermore, we theoretically prove that the designed message dropping policies are optimal to achieve the best system performance.