Abstract
In this paper, we present the results of a performance evaluation and optimization process of an indoor positioning system (IPS) designed to operate on portable as well as miniaturized embedded systems. The proposed method uses the Received Signal Strength Indicator (RSSI) values from multiple Bluetooth Low-Energy (BLE) beacons scattered around interior spaces. The beacon signals were received from the user devices and processed through an RSSI filter and a group of machine learning (ML) models, in an arrangement of one model per detected node. Finally, a multilateration problem was solved using as an input the inferred distances from the advertising nodes and returning the final position approximation. In this work, we first presented the evaluation of different ML models for inferring the distance between the devices and the installed beacons by applying different optimization algorithms. Then, we presented model reduction methods to implement the optimized algorithm on the embedded system by appropriately adapting it to its constraint resources and compared the results, demonstrating the efficiency of the proposed method.
Highlights
Positioning systems and related services are gaining increased popularity, since they can assist users in a wide range of applications
While there is a multitude of mature technologies available for outdoor positioning systems, offering accuracy down to the centimeter level [1], which cannot be said for indoor positioning systems (IPSs)
Based on the above remarks, we initially investigated the application of machine learning (ML) techniques to infer the position of a Bluetooth Low-Energy (BLE) device moving within a fixed grid of an interior space covered by Bluetooth signals of beacons installed in fixed locations in [17]
Summary
Positioning systems and related services are gaining increased popularity, since they can assist users in a wide range of applications. A final step that falls within the general problem category of multilateration remains to be solved to determine the location of the movable device in space using multiple ranges (distances) between the device/point and multiple spatially separated known locations In this respect, a variety of methods have been researched for several years to circumvent both technical and economical challenges, such as ultra-wideband (UWB)-based technologies [6] that spread the positioning signal among a large range of frequencies with different propagation and multipathing behaviors, ultrasonic-based signaling [7] that eliminate the problems presented by electromagnetic signals, and more widely available Wi-Fior Bluetooth-based technologies [8,9] that are readily available and run complex digital signal-processing algorithms to increase the accuracy.
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