ArticleSensors (Basel, Switzerland)2019
Deep CNN for Indoor Localization in IoT-Sensor Systems.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Design of Low-Cost and Highly Energy-Efficient Convolutional Neural Networks Based on Deterministic Encoding.Sensors (Basel, Switzerland) · 2025Article
- Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies.Sensors (Basel, Switzerland) · 2023Review
- Applied Machine Learning for IIoT and Smart Production-Methods to Improve Production Quality, Safety and Sustainability.Sensors (Basel, Switzerland) · 2022Article
- Support Vector Regression for Mobile Target Localization in Indoor Environments.Sensors (Basel, Switzerland) · 2022Article
- DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization.Sensors (Basel, Switzerland) · 2021Article
- Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture).Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.