Research on Three-Dimensional Fast Location and Application Platform of Internet of Things Based on Local Information Characterization for Global Distribution Density Information

In recent years, the Internet of Things has been widely concerned with the comprehensive application of various sensing technologies. The amount of data handled by the Internet is huge compared with any previous network, showing a real sense of mass. Wireless sensor network is an important part of the Internet Things, and it has become one of the research hotspots in the realization of the target technology. As a result of many factors such as environment, obstacle, network attack and hardware error, the data is easy to produce large errors, and the formation of erroneous data has a serious impact on positioning. Although there have been a number of positioning algorithms and models, but for the wrong data to achieve positioning research is also rare, especially in the country, almost blank. In order to solve the above problem, this paper proposes a new method for locating the global distribution density information with local information by using the network topology information. The typical local correlation model of robust local analysis is similar to the existing typical. In the real environment, the optimized algorithm has higher localization robustness and stability compared with the experimental results in the real environment.