Opportunistic Networks develops rapidly in recent years. With the popularity of GPS (Global Positioning System), velocity or position prediction plays an important role in opportunistic network routing. In this paper, a new opportunistic routing protocol named LSMPSF (Least Squares Method Prediction-based Spray and Focus) is proposed. With least squares method, LSMPSF predicts velocity by curve fitting. According to the predicted velocities, it estimates the neighbor nodes' delivery ratio and makes routing decision. Simulation results reveal that LSMPSF effectively increases the prediction accuracy and reduces resource consumption. Compared with SF and PROPHET, LSMPSF achieves better performance on delivery ratio and overhead ratio.

Publish/subscribe distributed systems are often used in critical applications. It is necessary to monitor their running patterns in real time to detect abnormal status. Therefore, identifying the normal running pattern is the precondition of monitoring publish/subscribe distributed systems. Based on Apriori algorithm, this paper presents a weighted frequent itemset mining algorithm for running pattern recognition of publish/subscribe distributed systems. By introducing the transaction matrix, the algorithm only needs to scan the transaction database once. By weighting the items from two aspects of influence and frequency, the support of the items with few occurrences but much importance can be improved, so that the running pattern containing small frequency events can be mined out. Experimental results show that the algorithm can effectively mine the running patterns, and has better performance than Apriori algorithm and FP-growth algorithm.