ITAR: A Method for Indoor RFID Trajectory Automatic Recovery

Cao, Ziwen; Wang, Siye; Sun, Degang; Zhang, Yanfang; Feng, Yue; Jiang, Shang

doi:10.1007/978-3-031-24386-8_22

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…This data may then be utilized to forecast a user's position based on sensor readings [165]- [167]. The authors [168] suggest an Indoor Trajectory employing a sequence-to-sequence learning architecture, a generation trajectory using a graph neural network, and a multi-head attention mechanism to capture correlations among trajectory points to increase performance. In this study the authors [169] proposed an effective hybrid technique for local feature matching and transformer deployment using a graph neural network.…”

Section: Heterogeneous Graphsmentioning

confidence: 99%

See 1 more Smart Citation

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods within IoT environments. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, the survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of IoT-related industries, such as evacuationegress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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Section: Heterogeneous Graphsmentioning

confidence: 99%

“…This can lead to more accurate and robust predictions, as well as increased model interpretability. [168], [179]- [181].…”

Section: ) Attention Mechanismsmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This data may then be utilized to forecast a user's position based on sensor readings [152][153][154]. The authors [155] suggest an Indoor Trajectory employing a sequence-to-sequence learning architecture, a generation trajectory using a graph neural network, and a multi-head attention mechanism to capture correlations among trajectory points to increase performance. The this study the authors [156] proposed an effective hybrid technique for local feature matching and transformer deployment using a graph neural network.…”

Section: Graphical Neuronal Network (Gnn)mentioning

confidence: 99%

“…These attention weights describe the relative relevance of each input element and are used to weight each element's contribution to the output.In general, allows a DL model to dynamically focus on the most important information in the input data while disregarding less relevant information. This can lead to more accurate and robust predictions, as well as increased model interpretability [155,[166][167][168].…”

Section: Attention Mechanismsmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj¹,

Himeur²,

Sohail³

et al. 2023

Preprint

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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 technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

show abstract

ITAR: A Method for Indoor RFID Trajectory Automatic Recovery

Cited by 2 publications

References 25 publications

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

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