To improve the efficiency of data sensing,collection and transmission in mobile crowd sensing network,this paper presents an opportunistic transmission mechanism based on QoS sensing and cooperative competition.In the mechanism,a mobile crowd sensing network model of multi-relay mobile node cooperative transmission is established based on spatial frequency correlation.The sensing area is divided according to the opportunity perception cycle and data acquisition frequency.The QoS sensing analysis model including measures like throughput,transmission delay and packet loss rate is built based on the mobile node dynamic transmission model.In order to weaken channel contention and optimize power allocation,this paper designs a cooperation and competition strategy.Analysis results show that the proposed mechanism has higher throughput as well as lower delay,packet loss rate and transmission load compared with direct transmission scheme and cooperative transmission scheme without direct transmission path.
According to the large number of named entities and deep domain of feature words in military text information,this paper proposes a vector description method for domain feature words.It compresses the vector space through the optimization of word segmentation and domain feature word selection,improves the extraction rules for four important types of named entity,including time,place name,troop name and weapon equipment,and extends the word segmentation dictionary library.It improves the domain feature word selecting algorithm combining domain relevance and domain consistency,enlarges the difference between domain words and common words,and further filters domain feature words.Experimental results show that after optimizing word segmentation,the named entities and some specific vocabulary in military texts can be extracted,and the number of feature words can be reduced.The accuracy and recall rate of the improved domain feature word selecting method are increased by 20% and 16.7% respectively.The feature word vector generated by the proposed method has strong domain feature.
Since the traditional outlier detection technology based on Omeasure needs to search all paths while detecting abnormal data and it is easy to make misjudgments under the scenario of less amount of data.Hence,it has obvious defects on the efficiency and precision ratio.According to the positive feedback feature of ant colony algorithm,a method which combines ant colony algorithm and attribute correlation analysis is put forward for attribute outlier detection.The method chooses the converged paths of the ant colony as the exception paths,then computes Omeasure value of each node on those paths,and identifies the outlier based on the Omeasure values.Experimental results show that this method performs better in recall,precision and efficiency than traditional outlier detection technology based on Omeasure.
Currently,the discernibility matrix method which is based on HU is not only time-consuming but also takes up a lot of storage space,so that the efficiency of the method is not high.Some scholars use the method of pairwise comparisons between elements to construct the enriching discernibility matrix algorithm.However,the time complexity is too high.It is not suitable for the processing of big data.There are many other methods which store the discernibility element in a compressed FP tree,but they cannot get rid of those unwanted elements.For this reason,this paper introduces the idea of binary tree,gives a method of a short discernibility set to establish binary tree and a long discernibility set to seek and compare in the binary tree,and puts forward an improved enriching discernibility matrix algorithm.On this basis,it proposes the extended binary discernibility matrix,and directly extracts rules from the matrix.Experimental results show that the acquisition algorithm of the enriching discernibility matrix not only reduces the time complexity but also gets rid of the unwanted elements and reduces the storage space.