Occupancy Estimation Using Wifi Motion Detection via Supervised Machine Learning Algorithms WiFi signals have tendency of getting disturbed by the motion of occupants and other movements in a zone. If we measure the level of this variation, it can represent the human activity in that zone. In this paper, we have proposed estimation of the occupancy by classifying the activity level obtained by disturbance in WiFi signal using several supervised machine learning approaches. We have prepared class labels using the schedule of people in the zone and verified it by counting the number of persons each hour. The proposed framework is tested and validated by collecting the data from an office space in a building and different performance measures are computed to see the effectiveness of this framework in occupancy estimation. In this task, Decision Tree and Random Forest are most stable with the highest accuracy of 95%.

Received Signal StrengthBased Indoor Localization Using Hierarchical Classification Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. WiFi received signal strength (RSS)based indoor localization techniques have attracted extensive attentions because WiFi access points (APs) are widely deployed and we can obtain the WiFi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classificationbased method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved KMeans clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the KNearest Neighbor (KNN) algorithm and support vector machine (SVM) with the oneversusone strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in realworld environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.