Most recent issue published online in the International Journal of Sensor Networks.
International Journal of Sensor Networks
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International Journal of Sensor Networks
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© 2024 Inderscience Publishers Ltd
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International Journal of Sensor Networks
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http://www.inderscience.com/browse/index.php?journalID=186&year=2024&vol=44&issue=3
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A multi-node collaborative and iterative UWB localisation algorithm for indoor complex environments
http://www.inderscience.com/link.php?id=137335
Ultra-wide-band (UWB) has high positioning accuracy in line of sight (LOS) scenarios. However, in indoor environments with large regions and severe none line of sight (NLOS), the number of localisable nodes and positioning accuracy are unsatisfactory. In indoor complex environments with large regions, three propagation models exist: LOS, NLOS with direct path (DP-NLOS), and NLOS with none direct path (NDP-NLOS). This paper considers the indoor propagation model and proposes a multi-node collaborative and iterative UWB localisation algorithm that uses a set of anchor nodes to locate a small subset of unlocalised nodes and utilises information from these nodes to localise the remaining unlocalised nodes. Additionally, the algorithm utilises variance information of localisation inaccuracies to iterate for more accurate coordinates. Ultimately, we improve the node locatable rate by 67.7%. The root mean square error (RMSE) of localisation is reduced by 0.1 to 0.9 meters in different regions.
A multi-node collaborative and iterative UWB localisation algorithm for indoor complex environments
Zheng Li; Huan Fang; Jinyu Zhao; Lin Pang
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 133 - 143
Ultra-wide-band (UWB) has high positioning accuracy in line of sight (LOS) scenarios. However, in indoor environments with large regions and severe none line of sight (NLOS), the number of localisable nodes and positioning accuracy are unsatisfactory. In indoor complex environments with large regions, three propagation models exist: LOS, NLOS with direct path (DP-NLOS), and NLOS with none direct path (NDP-NLOS). This paper considers the indoor propagation model and proposes a multi-node collaborative and iterative UWB localisation algorithm that uses a set of anchor nodes to locate a small subset of unlocalised nodes and utilises information from these nodes to localise the remaining unlocalised nodes. Additionally, the algorithm utilises variance information of localisation inaccuracies to iterate for more accurate coordinates. Ultimately, we improve the node locatable rate by 67.7%. The root mean square error (RMSE) of localisation is reduced by 0.1 to 0.9 meters in different regions.]]>
10.1504/IJSNET.2024.137335
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 133 - 143
Zheng Li
Huan Fang
Jinyu Zhao
Lin Pang
State Grid Laboratory of Power Line Communication Application Technology, China Gridcom Co., Ltd., Shenzhen 518109, China ' School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China ' State Grid Laboratory of Power Line Communication Application Technology, China Gridcom Co., Ltd., Shenzhen 518109, China ' State Grid Laboratory of Power Line Communication Application Technology, China Gridcom Co., Ltd., Shenzhen 518109, China
indoor localisation
ultra-wide-band
UWB
line of sight
LOS
none line of sight
NLOS
localisation algorithm
2024-03-12T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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143
2024-03-12T23:20:50-05:00
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Accurate close contact identification: a solution based on P-RAN, fog computing and blockchain
http://www.inderscience.com/link.php?id=137336
Epidemic like COVID-19 has spread extensively, disrupting people's daily lives worldwide. Accurate close contact identification (ACCI) emerges as a crucial measure to mitigate the spread of these epidemics. The low accuracy, insufficient user privacy protection ability, and limited effectiveness of ACCI become important issues to be solved. This paper proposes a solution that emphasises collecting reliable ACCI data and proving the trusted behaviour supervision of all the participating entities based on proximity network awareness. A three-layer architecture consisting of a P-RAN layer, hierarchical blockchain layer, and application layer is provided. Based on the architecture, a well-structured management platform for the blockchain network deployment and a comprehensive process for ACCI are also designed. Furthermore, this paper has conducted a thorough analysis of the potential for implementation and outlined the future challenges. Finally, we design simulations to validate the efficiency of the proposed solution in specific scenarios.
Accurate close contact identification: a solution based on P-RAN, fog computing and blockchain
Meiling Dai; Yutong Wang; Zheng Zhang; Xiaohou Shi; Shaojie Yang
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 144 - 156
Epidemic like COVID-19 has spread extensively, disrupting people's daily lives worldwide. Accurate close contact identification (ACCI) emerges as a crucial measure to mitigate the spread of these epidemics. The low accuracy, insufficient user privacy protection ability, and limited effectiveness of ACCI become important issues to be solved. This paper proposes a solution that emphasises collecting reliable ACCI data and proving the trusted behaviour supervision of all the participating entities based on proximity network awareness. A three-layer architecture consisting of a P-RAN layer, hierarchical blockchain layer, and application layer is provided. Based on the architecture, a well-structured management platform for the blockchain network deployment and a comprehensive process for ACCI are also designed. Furthermore, this paper has conducted a thorough analysis of the potential for implementation and outlined the future challenges. Finally, we design simulations to validate the efficiency of the proposed solution in specific scenarios.]]>
10.1504/IJSNET.2024.137336
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 144 - 156
Meiling Dai
Yutong Wang
Zheng Zhang
Xiaohou Shi
Shaojie Yang
Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' Research Department of Big Data and Artificial Intelligence, China Telecom Research Institute, Beijing, China ' State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
close contact identification
fog computing
blockchain
proximity radio access network
2024-03-12T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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144
156
2024-03-12T23:20:50-05:00
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DDQN-based data laboratory energy consumption control model
http://www.inderscience.com/link.php?id=137338
With the rapid development of information technology, data laboratory plays an essential supporting role in various fields. A lot of energy consumption also accompanies its operation, and the intuitively controllable part is the air conditioning energy consumption expenditure. Our work is based on the sensor data of data centre infrastructure management (DCIM). We collect data such as server temperature and humidity, users, and data transmission rate, but we need more specific data on air conditioning energy consumption. To this end, we used the computational fluid dynamics (CFDs) model to simulate the time series data of air conditioning energy consumption, used the improved regression analysis method to align it with the sensor data, and constructed a complete time series data set of air conditioning energy consumption. Finally, based on double deep Q-network (DDQN), an improved neural network, we constructed an energy consumption control model for the data laboratory to improve energy efficiency to the greatest extent. The experimental results thoroughly verify the effectiveness of our proposed model, which achieves remarkable results in energy consumption control and provides a new concept and method for energy conservation.
DDQN-based data laboratory energy consumption control model
Hui Cao; Xin Xu; Chenggang Li; Hongda Dong; Xiangyu Lv; Qi Jin
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 157 - 168
With the rapid development of information technology, data laboratory plays an essential supporting role in various fields. A lot of energy consumption also accompanies its operation, and the intuitively controllable part is the air conditioning energy consumption expenditure. Our work is based on the sensor data of data centre infrastructure management (DCIM). We collect data such as server temperature and humidity, users, and data transmission rate, but we need more specific data on air conditioning energy consumption. To this end, we used the computational fluid dynamics (CFDs) model to simulate the time series data of air conditioning energy consumption, used the improved regression analysis method to align it with the sensor data, and constructed a complete time series data set of air conditioning energy consumption. Finally, based on double deep Q-network (DDQN), an improved neural network, we constructed an energy consumption control model for the data laboratory to improve energy efficiency to the greatest extent. The experimental results thoroughly verify the effectiveness of our proposed model, which achieves remarkable results in energy consumption control and provides a new concept and method for energy conservation.]]>
10.1504/IJSNET.2024.137338
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 157 - 168
Hui Cao
Xin Xu
Chenggang Li
Hongda Dong
Xiangyu Lv
Qi Jin
Jilin Provincial Electric Power Science Research Institute Co., Ltd., Changchun 130000, China ' Jilin Provincial Electric Power Science Research Institute Co., Ltd., Changchun 130000, China ' Jilin Provincial Electric Power Science Research Institute Co., Ltd., Changchun 130000, China ' Jilin Provincial Electric Power Science Research Institute Co., Ltd., Changchun 130000, China ' Jilin Provincial Electric Power Science Research Institute Co., Ltd., Changchun 130000, China ' Computer Engineering College, Northeast Electric Power University, Jilin 132012, China
deep reinforcement learning
energy consumption control
computational fluid dynamics
CFDs
data feature engineering
2024-03-12T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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168
2024-03-12T23:20:50-05:00
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RCAU-Net: convolutional networks with residual channel attention for non-uniformity correction
http://www.inderscience.com/link.php?id=137337
Infrared imaging technology takes on critical significance in surveillance and security. However, the non-uniformity present in the infrared detectors manifests mainly as stripe noise in infrared images, severely limiting the sensitivity and advancement of the infrared imaging system. Most existing non-uniformity correction methods suffer from redundant noise and incomplete preservation of image details. This study introduces a novel correction model for non-uniformity grounded in the U-Net framework. This model incorporates a deep residual network, a channel attention mechanism, and global residual learning with the U-Net encoder- decoder for effective stripe noise reduction, which facilitates the learning of more profound context features and enables more accurate extraction of stripe noise. Our proposed approach has undergone evaluation using both simulated and real-world image data, revealing promising results through comparative analysis. Compared with the U-Net model, our model has shown improvements of 1.26 dB in peak signal-to-noise ratio and 0.7% in structural similarity.
RCAU-Net: convolutional networks with residual channel attention for non-uniformity correction
Feng Deng; Shiqiang Chen; Yutian Ma; Shaoyi Cheng; Yuanjun Sun; Jingjing Yang
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 169 - 181
Infrared imaging technology takes on critical significance in surveillance and security. However, the non-uniformity present in the infrared detectors manifests mainly as stripe noise in infrared images, severely limiting the sensitivity and advancement of the infrared imaging system. Most existing non-uniformity correction methods suffer from redundant noise and incomplete preservation of image details. This study introduces a novel correction model for non-uniformity grounded in the U-Net framework. This model incorporates a deep residual network, a channel attention mechanism, and global residual learning with the U-Net encoder- decoder for effective stripe noise reduction, which facilitates the learning of more profound context features and enables more accurate extraction of stripe noise. Our proposed approach has undergone evaluation using both simulated and real-world image data, revealing promising results through comparative analysis. Compared with the U-Net model, our model has shown improvements of 1.26 dB in peak signal-to-noise ratio and 0.7% in structural similarity.]]>
10.1504/IJSNET.2024.137337
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 169 - 181
Feng Deng
Shiqiang Chen
Yutian Ma
Shaoyi Cheng
Yuanjun Sun
Jingjing Yang
Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing, 100192, China; Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing, 100192, China; School of Automation, Beijing Information Science and Technology University, Beijing, 100192, China ' Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing, 100192, China; Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing, 100192, China; School of Automation, Beijing Information Science and Technology University, Beijing, 100192, China ' National Key Laboratory of Ni&Co Associated Minerals Resources Development and Comprehensive Utilization, Jinchang, 737104, China ' National Key Laboratory of Ni&Co Associated Minerals Resources Development and Comprehensive Utilization, Jinchang, 737104, China ' National Key Laboratory of Ni&Co Associated Minerals Resources Development and Comprehensive Utilization, Jinchang, 737104, China ' Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing, 100192, China; School of Automation, Beijing Information Science and Technology University, Beijing, 100192, China
non-uniformity correction
NUC
stripe noise
channel attention
deep residual network
residual learning
2024-03-12T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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181
2024-03-12T23:20:50-05:00
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A text classification network model combining machine learning and deep learning
http://www.inderscience.com/link.php?id=137333
Text classification is significant in natural language processing tasks, which can deal with a large amount of data scientifically. However, for text feature extraction, it is not easy to simultaneously consider the characteristics of short and long texts. Moreover, it does not reflect the importance of words in the text, resulting in unsatisfactory text classification results. Therefore, this paper proposes a machine learning and deep learning model. Specifically, text features are extracted by joint training, and then an attention mechanism is introduced to classify short texts and long texts. Firstly, the pre-processed data is subjected to term frequency-inverse document frequency, text convolutional neural networks and rotary transformer models for joint extraction of text features. Subsequently, the attention mechanism is introduced for the weight distribution problem after model fusion to improve the focus on keywords. Eventually, the experimental results indicate that the model proposed in this paper has a good effect on long and short-text classification. We achieved 95.8%, 92.5% and 95.4% accuracy on three public datasets, respectively. In this way, the proposed model is significant in text classification.
A text classification network model combining machine learning and deep learning
Hao Chen; Haifei Zhang; Yuwei Yang; Long He
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 182 - 192
Text classification is significant in natural language processing tasks, which can deal with a large amount of data scientifically. However, for text feature extraction, it is not easy to simultaneously consider the characteristics of short and long texts. Moreover, it does not reflect the importance of words in the text, resulting in unsatisfactory text classification results. Therefore, this paper proposes a machine learning and deep learning model. Specifically, text features are extracted by joint training, and then an attention mechanism is introduced to classify short texts and long texts. Firstly, the pre-processed data is subjected to term frequency-inverse document frequency, text convolutional neural networks and rotary transformer models for joint extraction of text features. Subsequently, the attention mechanism is introduced for the weight distribution problem after model fusion to improve the focus on keywords. Eventually, the experimental results indicate that the model proposed in this paper has a good effect on long and short-text classification. We achieved 95.8%, 92.5% and 95.4% accuracy on three public datasets, respectively. In this way, the proposed model is significant in text classification.]]>
10.1504/IJSNET.2024.137333
International Journal of Sensor Networks, Vol. 44, No. 3 (2024) pp. 182 - 192
Hao Chen
Haifei Zhang
Yuwei Yang
Long He
School of Computer and Information Engineering, Nantong Institute of Technology, Nantong 226002, China ' School of Computer and Information Engineering, Nantong Institute of Technology, Nantong 226002, China ' School of Computer and Information Engineering, Nantong Institute of Technology, Nantong 226002, China ' School of Computer and Information Engineering, Nantong Institute of Technology, Nantong 226002, China
text classification
neural networks
machine learning
deep learning
term frequency-inverse document frequency
TF-IDF
text convolutional neural networks
TextCNN
rotary transformer
RoFormer
attention mechanism
2024-03-12T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
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192
2024-03-12T23:20:50-05:00