Due to the lack of end-to-end paths between the communication source and destination in the Disruption Tolerant Network (DTN), its routing employs the store-carry-and-forward mechanism. In order to provide communication service in the DTN where there is only intermittent connectivity between nodes, a variety of epidemic-style routing algorithms have been proposed to achieve high message delivery probability at the cost of energy consumption. In this contribution, we investigate the problem of optimal multi-frame content transmission. By formulating the optimization problem with a Markov model, we derive the optimal policies under the two conditions of with and without energy constraint. We also investigate the performance of the proposed optimal policies through extensive numerical analyses, and conclude that the optimal policies give the best performance and the energy constraint critically degrades the system performance in the multi-frame content transmission.

As an important type of science and technology service resource, energy consumption data play a vital role in the process of value chain integration between home appliance manufacturers and the state grid. Accurate electricity consumption prediction is essential for demand response programs in smart grid planning. The vast majority of existing prediction algorithms only exploit data belonging to a single domain, i.e., historical electricity load data. However, dependencies and correlations may exist among different domains, such as the regional weather condition and local residential/industrial energy consumption profiles. To take advantage of cross-domain resources, a hybrid energy consumption prediction framework is presented in this paper. This framework combines the long short-term memory model with an encoder-decoder unit (ED-LSTM) to perform sequence-to-sequence forecasting. Extensive experiments are conducted with several of the most commonly used algorithms over integrated cross-domain datasets. The results indicate that the proposed multistep forecasting framework outperforms most of the existing approaches.

The propagation of messages among a group of people, which forms opportunistic Disruption Tolerant Networking (DTN), can be modeled as dynamic graph with links joining every two nodes up and down at a stationary speed. As people in DTN might have different probabilities of sending messages to each other, they should be divided into distinct groups with different link generate speed λ and link perish speed µ. In this letter, we focus on the two-group case, and apply Edge-Markovian Dynamic Graphs to present an analysis framework to evaluate the average delay for the information dissemination in DTN. We also give extensive simulation and numerical results revealing the influence of various parameters.