Browsing by Author "Naghdi, Sharareh"

Now showing 1 - 3 of 3
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    Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration
    (MDPI, 2022-06-07) Naghdi, Sharareh; O'Keefe, Kyle
    The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.
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    Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
    (2020-02-29) Naghdi, Sharareh; O'Keefe, Kyle P. G.
    One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.
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    Improving Bluetooth-based Indoor Positioning Using Vision and Artificial Networks
    (2020-07-09) Naghdi, Sharareh; O'Keefe, Kyle P. G.; Noureldin, Aboelmagd M.; Lichti, Derek D.; Liang, Steve H. L.; Barsocchi, Paolo
    The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Global Navigation Satellite Systems (GNSS) receivers, while ubiquitous in outdoor positioning, are not effective indoors. One of the best solutions to solve this problem and increase the accuracy of the user's position in indoor areas is to apply other sensors. This research takes advantage of Bluetooth Low Energy (BLE) technology, vision systems, and Artificial Neural Networks (ANNs) to improve the accuracy of the position solutions in indoor environments for pedestrian applications. BLE technology faces challenges related to the Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This thesis presents methods to compensate for losses in the RSSI values by applying ANN algorithms to RSSI measurements from three BLE advertising channels. The resulting improved RSSI values are then converted into ranges using path loss models and trilateration is applied to obtain indoor positions. Two neural network algorithms were implemented. The first used only the RSSI values while the second incorporated a wearable camera as an additional source of information about the presence or absence of human obstacles. The results showed that the two proposed artificial-based systems could enhance RSSI due to human body shadowing and provide significantly better ranging and positioning solutions than fingerprinting and trilateration techniques with uncorrected RSSI values. Two proposed systems provided 3.7 m and 6.7 m positioning accuracy in 90 % of the time in a complex environment with the presence of the human body, nevertheless, the fingerprinting and the classic algorithms offered 8.7 m and 12.3 m position accuracy in the same situation, respectively.