Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Zhang, Zhengwan; Zhang, Chunjiong; Ma, Yuanliang; Xie, Tao

doi:10.1109/access.2020.3008373

Cited by 46 publications

(51 citation statements)

References 34 publications

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“…In the experiment, the WSN node adopts CC2530 sensor network node of TI (Texas instruments) company [30], which has an enhanced C51 single chip microcomputer and 8k RAM storage space, and is powered by two No.5 batteries [31,32]. WSN nodes are shown in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

IIBE: An Improved Identity-Based Encryption Algorithm for WSN Security

Cao

Tang

Huang

et al. 2021

Security and Communication Networks

Self Cite

114

104

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Wireless sensor networks (WSN) have problems such as limited power, weak computing power, poor communication ability, and vulnerability to attack. However, the existing encryption methods cannot effectively solve the above problems when applied to WSN. To this end, according to WSN’s characteristics and based on the identity-based encryption idea, an improved identity-based encryption algorithm (IIBE) is proposed, which can effectively simplify the key generation process, reduce the network traffic, and improve the network security. The design idea of this algorithm lies between the traditional public key encryption and identity-based public tweezers’ encryption. Compared with the traditional public key encryption, the algorithm does not need a public key certificate and avoids the management of the certificate. Compared with identity-based public key encryption, the algorithm addresses the key escrow and key revocation problems. The results of the actual network distribution experiments demonstrate that IIBE has low energy consumption and high security, which are suitable for application in WSN with high requirements on security.

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Section: Methodsmentioning

confidence: 99%

IIBE: An Improved Identity-Based Encryption Algorithm for WSN Security

Cao

Tang

Huang

et al. 2021

Security and Communication Networks

Self Cite

114

104

View full text Add to dashboard Cite

show abstract

“…Related Work. E-learning helps the traditional learning process take a step forward by providing students with materials that can help them learn anytime and anywhere [23,24]. However, many studies have shown that web-based online learning still lacks intelligence that may not be appropriate for each learner's characteristics [25].…”

Section: Introductionmentioning

confidence: 99%

Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Wang

Shi

2021

Security and Communication Networks

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Due to the epidemic, online course learning has become a major learning method for students worldwide. Analyzing its massive data from the massive online education platforms becomes a challenge because most learners watch online instructional videos. Thus, analyzing learners’ learning behaviors is beneficial to implement personalized online learning strategies with sentiment classification models. To this end, we propose a context-aware network model based on transfer learning that aims to predict learner performance by solving learners’ problems and improving the educational process, contributing to a comprehensive analysis of such student behavior and exploring various learning models in MOOC video interactions. In addition, we visualize and analyze MOOC video interactions, enabling course instructors and education professionals to analyze clickstream data generated by learners interacting with course videos. The experimental results show that, in the process of “massive data mining,” personalized learning strategies of this model can efficiently enhance students’ interest in learning and enable different types of students to develop personalized online education learning strategies.

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“…Forest fires are initially unstable flames. Since fire pixels increases with fire area, size is an important feature of fire [7,18]. To recognize the variability of fire area, the size change of fire area was calculated based on two consecutive images.…”

Section: Fire Feature Extractionmentioning

confidence: 99%

“…These networks face a high application cost, due to their huge computing load. Later, Emmy Prema adopts the background difference method to find moving pixels, and looks for flame color regions with a color model; Afterwards, a spatiotemporal analysis was performed on these regions to identify irregular and flickering fire features [7].…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Recognition Based on Feature Extraction from Multi-View Images

Wu¹,

Zhang²,

Ji³

et al. 2021

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111

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Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

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Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

Cited by 46 publications

References 34 publications

IIBE: An Improved Identity-Based Encryption Algorithm for WSN Security

IIBE: An Improved Identity-Based Encryption Algorithm for WSN Security

Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model

Forest Fire Recognition Based on Feature Extraction from Multi-View Images

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