IoT Indoor Localization with AI Technique

D’Aloia, Matteo; Longo, Annalisa; Guadagno, Gianluca; Pulpito, Mariano; Fornarelli, Paolo; Laera, Pietro Nicola; Manni, Dario; Rízzí, María

doi:10.1109/metroind4.0iot48571.2020.9138275

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…D’Aloia et al investigated BLE fingerprinting indoor localization using RSS signals. They used KNN and ANN to localize a dataset collected by five anchors in a building [ 35 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Shahbazian

Macrina

Scalzo

et al. 2023

Sensors

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The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper’s main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.

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“…D’Aloia et al investigated BLE fingerprinting indoor localization using RSS signals. They used KNN and ANN to localize a dataset collected by five anchors in a building [ 35 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Shahbazian

Macrina

Scalzo

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Early work was primarily focused on the potential usefulness of the IoT concept for indoor positioning, rather than its practical applications. While some papers, such as [ 43 , 44 , 45 ], mentioned Industry 4.0 and IoT in their title or abstract, they did not discuss the development or practical implementation of these concepts. Table 1 presents a review of research articles that focus on implementing indoor positioning through IoT or DT infrastructure at different levels.…”

Section: Related Workmentioning

confidence: 99%

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach

Karakusak,

Kivrak,

Watson

et al. 2023

Sensors

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In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

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“…Thus, industry and academia are developing new ML models to provide highly accurate solutions to the end-users. Some of these ML models are already used in Internet of Things (IoT) and wearable devices [1]. In this case, it is essential to keep a low computational load and high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

Quezada-Gaibor,

Torres-Sospedra,

Nurmi

et al. 2022

Preprint

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Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1%).

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IoT Indoor Localization with AI Technique

Cited by 11 publications

References 8 publications

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach

Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

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