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Eucalyptus is the main fast-growing and high-yielding tree species in southern China and is the main source of wood production. Over the years, there have been many reports that eucalyptus is a water pump and that eucalyptus causes serious damage to understory vegetation. In this paper, a sensor network of eucalyptus plantation has been established in Yinling branch of Gaofeng Forest Farm in Guangxi, the main eucalyptus producing province. The sensor network can monitor the DBH changes, trunk runoff, soil moisture, environmental information and so on of eucalyptus in the stand in real time. The collected data are uploaded to the artificial forest Internet of Things monitoring information cloud management system established in this paper. This paper introduces the functions of the cloud management system in detail. On this basis, this paper discusses the fusion of high-frequency information collected from monitoring points to meet different data requirements. The research on data fusion in this paper is very preliminary. In the conclusion part, the future research on long-term monitoring data fusion of eucalyptus forest is prospected.

Compared with the Euclidean space, road network is restricted by its direction in traveling, velocity and some other attribute profiles. So the algorithms that designed for the Euclidean space are usually invalid and difficult to provide privacy protection services. In order to cope with this problem, we have proposed an algorithm to provide the service of collecting anonymous users that their directions in traveling similar with the initiator in the road networks. In this algorithm, the shortest distance between multiple road segments is calculated, and then utilizes the distance to select the user who has the same direction in traveling with the initiator. Consequently, the problem of the discrepancy of the anonymous users in the routing that invalidates the location privacy protection is solved. At last, we had compared this algorithm with other similar algorithms, and through the results of the comparison and the cause of this phenomenon, we have concluded that this algorithm is better not only in the level of privacy protection, but in the performance of execution efficiency.