Deep CNN-LSTM Network for Indoor Location Estimation Using Analog Signals of Passive Infrared Sensors

Ngamakeur, Kan; Yongchareon, Sira; Yu, Jian; Sheng, Quan Z.

doi:10.1109/jiot.2022.3183148

Cited by 24 publications

(8 citation statements)

References 57 publications

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“…This comprehensive algorithmic approach, combining ANN, LSTM, permutation invariance, and data augmentation, enables PIRILS to accurately track and localize multiple targets in indoor environments, showcasing a significant advancement in the application of PIR sensors augmented by deep learning techniques. Ngamakeur et al introduced a different deep learning-based method for people localization in their study [ 80 ], employing CNN and LSTM networks. The process begins with preprocessing the PIR sensor data.…”

Section: Resultsmentioning

confidence: 99%

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

Shokrollahi,

Persson,

Malekian

et al. 2024

Sensors

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Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings’ status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

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Section: Resultsmentioning

confidence: 99%

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

Shokrollahi,

Persson,

Malekian

et al. 2024

Sensors

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“…Ngamakeur et al proposed a deep CNN-LSTM architecture for PIR-based indoor location estimation using deep learning. The CNN network extracted features from the PIR analog output, and the LSTM network learned temporal dependencies between the extracted features in their proposed method [ 59 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“… Classification of the reviewed literature, with emphasis on the learning algorithms used [ 28 , 29 , 31 , 32 , 33 , 34 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. …”

Section: Figurementioning

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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“…In indoor localization tasks, fingerprinting can be fundamentally viewed as a supervised learning problem, where deep learning algorithms can be employed to learn the mapping relationship between fingerprint data and reference points (RPs), thereby achieving improved localization accuracy [13][14][15] . These learning models have demonstrated considerable success in cases where data exists in Euclidean domains or grid structures 16 .…”

Section: Introductionmentioning

confidence: 99%

Indoor localization algorithm based on geometric deep learning

Kang,

Liang,

Wang

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Popular machine learning based fingerprint localization methods often struggle to effectively capture non-Euclidean characteristics present in fingerprint data, while geometric deep learning can effectively process such data. In this paper, we propose a geometric fingerprinting based graph neural network indoor localization algorithm (GFGNN), which is models access points (APs) and reference points (RPs) using received signal strength (RSS) fingerprint. This approach maximizes the utilization of the unstructured nature of fingerprint data to enhance indoor localization accuracy and stability in dynamic environments. The algorithm establishes the fingerprint data as a graph feature representation, we first employ a graph convolutional network at the AP level to aggregate RSS values containing spatial relationships. Subsequently, graph isomorphism networks are employed at the RP level to further extract and update the aggregated fingerprint features. Finally, a multi-layer Perceptron is utilized to regressively predict the localization of the target to be located. We evaluate the proposed GFGNN on a self-built dataset, and the localization accuracy remains within 0.43 meters at the 80th percentile of the cumulative distribution function, with stable localization performance even in dynamic scenarios.

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Deep CNN-LSTM Network for Indoor Location Estimation Using Analog Signals of Passive Infrared Sensors

Cited by 24 publications

References 57 publications

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

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

Indoor localization algorithm based on geometric deep learning

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