Abstract
In the era of the Internet of Things (IoT) and Industry 4.0, the indoor usage of smart devices is expected to increase, thereby making their location information more important. Based on various practical issues related to large delays, high design cost, and limited performance, conventional localization techniques are not practical for indoor IoT applications. In recent years, many researchers have proposed a wide range of machine learning (ML)-based indoor localization approaches using Wi-Fi received signal strength indicator (RSSI) fingerprints. This survey attempts to provide a summarized investigation of ML-based Wi-Fi RSSI fingerprinting schemes, including data preprocessing, data augmentation, ML prediction models for indoor localization, and postprocessing in ML, and compare their performance. Any ML-based study is heavily reliant on datasets. Therefore, we dedicate a significant portion of this survey to the discussion of dataset collection and open-source datasets. To provide good direction for future research, we discuss the current challenges and potential solutions related to ML-based indoor localization systems.
Highlights
C URRENTLY, Internet of Things (IoT) devices are becoming increasingly popular
The results demonstrated that the proposed approach can achieve more than 80% accuracy with a positioning error of less than 2m and requires only 40ms to provide location information
We discuss some of the significant challenges associated with machine learning (ML)-based fingerprinting techniques: lack of privacy, lack of standardization of algorithms, lack of databases, heterogeneity of devices, high energy consumption, Wi-Fi networks not made for localization, and handover delay during Wi-Fi roaming
Summary
C URRENTLY, Internet of Things (IoT) devices are becoming increasingly popular. With the advent of Industry 4.0, advanced Smart-X applications using smart devices, such as smart cities, homes, farms, and factories, are being developed rapidly [1]. In [52], the authors presented a survey on Wi-Fi fingerprint-based indoor positioning systems They discussed advances in terms of reducing labor-intensive tasks such as data collection, calibrating heterogeneous devices, and achieving energy efficiency for smartphones. Including database construction and enrichment techniques, performance metrics, ML structure in indoor localization, ML-based RSSI fingerprinting schemes, publicly available datasets, and technical challenges and solutions. This survey paper aims to help readers navigate the abundance of existing literature regarding ML-based indoor localization using WiFi RSSI fingerprints. C. CONTRIBUTION OF THE PAPER 1) This work provides a survey of ML-based indoor localization techniques using Wi-Fi RSSI fingerprints that have been proposed in the literature over recent years.
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