Research Publications

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Open Access
Machine Learning-Based Indoor Localization System with Human- Computer Interaction System
(SLIIT, Faculty of Engineering, 2023-10) Jayasundara, A; Malasinghe, L
Understanding the indoor whereabouts of individuals and objects is important, especially for those who fall within the 71% of visually impaired individuals with a school education, students in 450 special education units and many other areas and aspects in Sri Lanka. Researchers have declared that, there isn’t any particularly good localization system, and the performance should be evaluated considering the approach and application. The most well-known indoor positioning (IP) technologies that have been historically deployed are Bluetooth, Wi-Fi, RFID (radio frequency identification), IR (Infrared), and UV (ultraviolet) whereas received signal strength (RSSI), fingerprinting, and triangulation methods have been used as common IP techniques. The combination of both IP technologies and techniques creates an IP system, and the integration of machine learning and IoT with the structured system essentially delivers an accurate and more advanced system. This paper contains a detailed, analytical review of a developed indoor positioning system derived from the existing indoor localization techniques, localization technologies, localization systems, algorithms, and performance matrixes. This also provides a comprehensive comparison between numerous existing systems to justify the proposed solution. This project has been developed to achieve better accuracy through low-cost deployment as an effective system to fill the gap in the scarcity of positioning systems in the world. This paper presents a descriptive introduction and problem definition, a critical discussion of results, machine learning models, benefits of the project, and future works. As later justified, ESP32 microcontroller and BLE beacons are utilized with RSSI fingerprinting method to develop this IP system and, as a part of the project, two data visualization methods have been introduced here using NodeRED dashboard and LC display. Overall, this project was developed with an effective combination of RSSI fingerprinting, IoT protocols, machine learning, and data interpretation methods.
Embargo
Location based fingerprinting techniques for indoor positioning
(Faculty of Graduate Studies and Research, 2017-01-26) Gettapola, K.I.; Ranaweera, R. R. W. M. H. D.; Godaliyadda, G. M. R. I.; Imara, M. N. F.
Indoor positioning is a popular and a novel researched area mainly in terms of its vast applications on providing location based services. The requirement for indoor positioning has mainly occurred due to the infeasibility of the Global Positioning System (GPS) in indoor environments which consisting of many obstacles and having non-line-of-sight. As there is no exact method applicable globally For indoor positioning, several techniques are employed considering the environments they are being used. Among these techniques, fingerprinting provides several propitious features for indoor environments having obstacles and contains severe multipath channels. Our focus of study is to identify the applicability of audible sound for location based fingerprinting which could be augmented in environments with non-line-of sight and multipath wave propagating conditions, with low cost and a simple implementable solution. The study was conducted to locate the position of a receiver, by an acoustic signal generated from a fixed source transmitter at a controlled environment. Initially, the environment is divided into several grid points in order to generate a database comprising the location data. A large number of realizations were obtained at each of the grid points in order to create a robust database. The location is estimated via comparison of the feature vector of signatures generated at the receiver, with the pre-generated database. More focus was given on selecting suitable parameters to be used as unique signatures of a given acoustic signal of very short duration and to explore model based and non-model based methods on determining the position with minimum error.

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