Fingerprint-based localization plays an important role in indoor location-based services,
where the position information is usually collected in distributed clients and gathered in a …

Localization and tracking of objects using data-driven methods is a popular topic due to the
complexity in characterizing the physics of wireless channel propagation models. In these …

Crowdsourcing dramatically benefits WiFi fingerprinting localization in reducing the costs of
collecting received signal strength (RSS) data during offline site survey and has gained …

Clustered federated Multitask learning is introduced as an efficient technique when data is
unbalanced and distributed amongst clients in a non-independent and identically distributed …

Fingerprint-based indoor localization has drawn increasing attention with the development
of deep learning. Nevertheless, it faces challenges from frequent data collection and the …

Crowdsourcing Federated learning (CFL) is a new crowdsourcing development paradigm
for the Deep Neural Network (DNN) models, also called “software 2.0”. In practice, the …

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 …

Indoor tracking is one of the most attractive topics in communication and information
technology. Indoor localization mechanisms can help people for navigating in complex …

This paper surveys Artificial Intelligence (AI) methods for acquiring and managing context-of-
operation awareness of radio communication nodes, links, and networks. The meaning and …

Deep learning-assisted indoor fingerprint localization based on frequent data collection is
motivating renewed interest via crowdsourcing. Uploading raw training data may cause …