Multilayer ANN indoor location system with area division in WLAN environment

An indoor location system based on multilayer artificial neural network（ANN） with area division is proposed.The characteristics of recorded signal strength（RSS）,or signal to noise ratio（SNR） from each available access points（APs）,are utilized to establish the radio map in the off-line phase.And in the on-line phase,the two or three dimensional coordinates of mobile terminals（MTs） are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio map.Although the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points,the efficient inputs for better training performances are difficult to be determined because of complex and dynamic indoor environment.Then,the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance is presented.And also,the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division,K-nearest neighbor（KNN） and probability methods in typical office environment.

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.

A Semi-Supervised WLAN Indoor Localization Method Based on l1-Graph Algorithm

For indoor location estimation based on received signal strength( RSS) in wireless local area networks( WLAN),in order to reduce the influence of noise on the positioning accuracy,a large number of RSS should be collected in offline phase. Therefore,collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper,the traditional semisupervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed,and the result shows that the k-NN or ε-NN graph are sensitive to data noise,which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem,it proposes a l1-graph-algorithm-based semi-supervised learning( LG-SSL) indoor localization method in which the graph is built by l1-norm algorithm. In our system,it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase,and then uses LG-SSL to estimate user's location in online phase. Extensive experimental results show that,benefit from the robustness to noise and sparsity ofl1-graph,LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.

Floor positioning method indoors with smartphone’s barometer

This paper presents an indoor floor positioning method with the smartphone’s barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology.First,an initial floor position algorithm with the“entering”detection algorithm has been obtained.Second,the user’s going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation.Third,the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position.In order to solve the problem of the floor misjudgment from different mobile phone’s barometers,this paper calculates the pressure data from the different cell phones,and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones.The experiment results show that the average correct rate of the floor identification is more than 85%for three types of the cell phones while reducing environmental dependence and improving availability.Further,this paper compares and analyzes the three common floor location methods–the WLAN Floor Location(WFL)method based on the fingerprint,the Neural Network Floor Location(NFL)methods,and the Magnetic Floor Location(MFL)method with our method.The experiment results achieve 94.2%correct rate of the floor identification with Huawei mate10 Pro mobile phone.