The researches on two-dimensional indoor positioning based on wireless LAN and the location fingerprint methods have become mature, but in the actual indoor positioning situation, users are also concerned about the height where they stand. Due to the expansion of the range of three-dimensional indoor positioning, more features must be needed to describe the location fingerprint. Directly using a machine learning algorithm will result in the reduced ability of classification. To solve this problem, in this paper, a “divide and conquer” strategy is adopted; that is, first through k-medoids algorithm the three-dimensional location space is clustered into a number of service areas, and then a multicategory SVM with less features is created for each service area for further positioning. Our experiment shows that the error distance resolution of the approach with k-medoids algorithm and multicategory SVM is higher than that of the approach only with SVM, and the former can effectively decrease the “crazy prediction.”
Wireless LAN (WLAN) technology is developing rapidly with the help of wireless communication technology and social demand. During the development of WLAN, the security is more and more important, and wireless monitoring and shielding are of prime importance for network security. In this paper, we have explored various security issues of IEEE 802.11 based wireless network and analyzed numerous problems in implementing the wireless monitoring and shielding system. We identify the challenges which monitoring and shielding system needs to be aware of, and then provide a feasible mechanism to avoid those challenges. We implemented an actual wireless LAN monitoring and shielding system on Maemo operating system to monitor wireless network data stream efficiently and solve the security problems of mobile users. More importantly, the system analyzes wireless network protocols efficiently and flexibly, reveals rich information of the IEEE 802.11 protocol such as traffic distribution and different IP connections, and graphically displays later. Moreover, the system running results show that the system has the capability to work stably, and accurately and analyze the wireless protocols efficiently.
With the development of mobile networks and positioning technology, extensive attention focuses on the location-based service (LBS) which processes the application data including user queries, information searches, and user comments by the location information. In LBS, the recognition of the location word in user messages is meaningful and important. The location word recognition in LBS is different from the traditional named entity recognition, owing to the additional information such as user location coordinates in LBS. This paper proposes a method that adds the service context information including user location coordinates and message timestamps into the machine learning to improve the accuracy of the Chinese location word recognition. The experiment based on microblog datasets in mobile environment proves the viability and effectiveness of this method.
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