Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

As the location-based service (LBS) plays an increasingly important role in real life, the topic
of positioning attracts more and more attention. Under different environments and principles …

Federated learning (FL) is a distributed learning paradigm that maximizes the potential of
data-driven models for edge devices without sharing their raw data. However, devices often …

The increasing popularity of wearable-based Collaborative Indoor Positioning Systems
(CIPSs) has led to the development of new methods for improving positioning accuracy …

In this article, the use of channel state information (CSI) for indoor positioning is studied. In
the considered model, a server equipped with several antennas sends pilot signals to users …

Federated learning (FL) is an emerging paradigm for decentralized training of machine
learning models on distributed clients, without revealing the data to the central server. The …

Indoor localization (IL) is a significant topic of study with several practical applications. The
area of IL has evolved greatly in recent years due to the introduction of numerous …

Abstract In recent years, Indoor Positioning Systems (IPS) have emerged as a critical
technology to enable a diverse range of Location-based Services (LBS) across different …

Safety helmet wearing detection based on video surveillance is an important means of
safety monitoring in many industrial scenes. The training of safety helmet wearing detection …

In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of
Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data …