Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing
steadily due to their potential to improve on the performance of their non-collaborative …

Indoor localization (IL) is a significant topic of study with several practical applications,
particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has …

With rising demand for indoor location-based services (LBS) such as location-based
marketing, mobile navigation, etc., there has been considerable interest in indoor …

Benefiting from the development of wireless communication, WiFi localization plays a vital
role in various mobile applications. However, users' locations are breached by untrusted …

Clustering is a common technique for data analysis, which aims to partition data into similar
groups. When the data comes from different sources, it is highly desirable to maintain the …

The way people access services in indoor environments has dramatically changed in the
last year. The countermeasures to the COVID-19 pandemic imposed a disruptive …

Indoor positioning is becoming more popular with increasing user demands on Location-
based Services (LBS) and Social Networking Services (SNS). Location fingerprinting is …

Machine Learning as a Service (MLaaS) is a growing paradigm in the Machine Learning
(ML) landscape. More and more ML models are being uploaded to the cloud and made …

Clustering is an unsupervised machine learning technique that outputs clusters containing
similar data items. In this work, we investigate privacy-preserving density-based clustering …

Due to the extensive applicability of Location-Based Services (LBSs) and the Global
Navigation Satellite System (GNSS) failure in indoor environments, indoor positioning …