Abstract
Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks,k-nearest neighbors,K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.
Highlights
Positioning systems aka localization systems both for outside and inside buildings is an ever-exciting area of research and development due to increasing market shares as in smart buildings, assistive and assisted living, safer metropolitans using geographical information systems, and tracking of IoT objects for commercial purposes
Experimental Results and Discussion is section entails hardware equipment, software used, and particulars of experiments that were used to evaluate the performance of CEnsLoc in light of accuracy, precision, recall, training, and response time
An Intel machine (64-bit Xeon: X5650) with a master clock at 2.67 GHz with 24 GB RAM, and 64-bit Windows 10 Education was used for experimentation in MATLAB. e real dataset was developed through FP collection at the ground floor of Software Engineering (SE) Centre, University of Engineering and Technology (UET), Lahore Pakistan. e building’s dimensions are 39 m × 31 m (1209 m2) containing offices, class rooms, laboratories, and open corridors
Summary
Positioning systems aka localization systems both for outside and inside buildings is an ever-exciting area of research and development due to increasing market shares as in smart buildings, assistive and assisted living, safer metropolitans using geographical information systems, and tracking of IoT objects for commercial purposes. Several kinds of sensory input such as images [6], video, ambient sound [7], accelerometer [8], magnetometer [9], pedometer, gyroscope readings, and their amalgamation with various aforesaid RF signals [10] have been explored Several approaches and their combinations such as time of arrival (TOA), time difference of arrival (TDOA), pedestrian dead reckoning (PDR), and angle of arrival (AOA) [11] have been utilized for indoors. Wi-Fi networks are deployed as in Access Points (APs) that are prevalent everywhere Utilizing these to capture RSSI fingerprint (FP) is conveniently possible with device as simple as smartphones or phablets. Wi-Fi fingerprint-based localization has the following benefits: no requirement of extra hardware at both sender and receiver sides; utilization of already existent infrastructure; implementable; and no essential need of propagation model building which may or may not depict real signal propagation at run time [13]. Our dataset was collected using a single Android phone; with a large team using a variety of devices, data collection can be performed over different times of the year to obtain data both for training and testing of our approach
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