Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Fingerprint Database Clustering <s> We previously developed an analytical model in [8] to predict the precision and accuracy performance of indoor positioning systems using location fingerprints. A by-product of the model is the ability to eliminate unnecessary fingerprints to reduce the number of fingerprints in a radio map for comparison without loss in performance. This model enables computation of an approximate probability distribution of location selection, by employing a proximity graph to extract neighbor and non-neighbor sets of a given fingerprint. However, employing the model 'as is' in a system with many location fingerprints requires determining a single large proximity graph derived from all location fingerprints which may involve significant computational effort. In this paper, we consider two techniques to divide location fingerprints into smaller clusters. Separate proximity graphs for each cluster are now used to predict performance and eliminate unnecessary location fingerprints. Results show that the computational effort can be reduced, creating a more scalable analytical model, while still predicting and enabling good precision performance. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Fingerprint Database Clustering <s> Abstract. Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework. Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Fingerprint Database Clustering <s> Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases the localization difficulty and computational consumption. To cope with this, a novel AP selection method, LocalReliefF-C( a novel method based on ReliefF and correlation coefficient), is proposed. Firstly, on each reference location, the positioning capability of APs is ranked by calculating classification weights. Then, redundant APs are removed via computing the correlations between APs. Finally, the set of best-discriminating APs of each reference location is obtained, which will be used as the input features when the location is estimated. Furthermore, an effective clustering method is adopted to group locations into clusters according to the common subsets of the best-discriminating APs of these locations. In the online stage, firstly, the sequence of RSS observations is collected to calculate the set of the best-discriminating APs on the given location, which is subsequently used to compare with cluster keys in order to determine the target cluster. Then, hidden naive Bayes (HNB) is introduced to estimate the target location, which depicts the real WLAN environment more accurately and takes into account the mutual interaction of the APs. The experiments are conducted in the School of Environmental Science and Spatial Informatics at the China University of Mining and Technology. The results validate the effectiveness of the proposed methods on improving localization accuracy and reducing the computational consumption. <s> BIB003
With the indoor environment continually expanding, the required fingerprint data gradually increases. This increase directly affects the positioning efficiency and time consuming, which can also increase the number of invalid fingerprint data, resulting in reducing positioning accuracy, so the optimization of fingerprint data is particularly necessary. Clustering evaluates the fingerprint data based on their similarity degree and classifies them by it to optimize the fingerprint data. After clustering, the fingerprint database can be divided into several parts, improving the efficiency of positioning and reducing the system consumption. Swangmuang BIB001 used median clustering and K-mean clustering to cluster the fingerprint database separately. The contrast experiment they undertook indicated that the clustering method could save many operations required from the model without clustering, which reduced around a third of the operations separately. Altintas BIB002 proposed a positioning solution using K-means clustering in combination with the KNN method which outperformed the traditional KNN. Instead of clustering the fingerprint database, they clustered the k reference fingerprint data obtained by KNN with K-means clustering to participate in positioning. The K-means clustering reduces the required reference data and decreases the positioning average distance errors. Lee BIB003 proposed a novel clustering-based approach to achieve indoor positioning. They presented the support vector machine-based clustering approach, named SVM-C, which used the margin between two canonical hyper planes for classification instead of using the Euclidean distance between two centroids of reference locations. They clustered the fingerprint data with SVM margins in the offline phase and utilized clustering match technology in the online phase. The experimental results indicated that the positioning mean errors were lower than other approaches like the means clustering method, SVC, etc.