Support Vector Machine (SVM) has good performance in dealing with dimensionality disaster, over fitting and nonlinearity, which other traditional situation assessment algorithms does not have. However SVM has low efficiency when dealing with large-scale data. To effectively confront the challenge of handling big data, a MapReduce-based SVM (MR-SVM) situation assessment algorithm was proposed. Considering the characteristics of SVM algorithm, the parallelization and parameter selection of SVM based on MapReduce programming was implemented by designing procedures of map function and reduce function. The performances of MR-SVM and SVM were compared on Hadoop platform, MR-SVM had lower efficiency than SVM when dealing with small-scale data, but much better performance when dealing with large-scale data. SVM had an exponential growth on training time with the growth of data scalability while MR-SVM has slow growth. The experiment results show that MR-SVM solves the problem of data scalability, therefore it is suitable for situation assessment in big data environment.

To solve the congestion problem at node in delay tolerant networks, an active congestion control strategy based on historical probability was proposed. The strategy put forward the concept of referenced probability that could be adjusted dynamically by the degree of congestion. Referenced probability would control the forwarding conditions to avoid and control the congestion at node. At the same time the utilization of idle resources and the transmission efficiency of the network would be promoted. The simulation results show that the strategy upgrades delivery ratio of the entire network and reduces the load ratio and message loss rate. As a result, the active congestion control is realized and the transmission performance of the network is enhanced.