Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Sensors

Theofanis Orphanoudakis¹  Helen-Catherine Leligou¹  Konstantinos Kotrotsios¹  Anastasios Fanariotis¹ 

[1] School of Sciences and Technology, Hellenic Open University, 26334 Patras, Greece;

关键词: indoor localization;    Bluetooth;    beacons;    machine learning;    embedded IPS;   

DOI  :  10.3390/s22020570

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown