In Wireless Sensor Networks (WSNs), when multiple data packets meet during routing to the sink, redundant data can be removed through data fusion, thereby reducing the amount of data transmitted, and increasing the life of the network. However, how to increase the data fusion rate as much as possible and ensure that the delay is lower than the deadline is a challenge issue. To solve this problem, a Differentiated Threshold Configuring joint Optimal Relay Selection based Data Aggregation (DTC-ORS-DA) scheme is proposed, which can significantly reduce redundant data routing and guarantee the delay for WSN. The main innovation is as follow: (1) In DTC-ORS-DA scheme, there are two thresholds: data volume threshold $N_{y}$ and time threshold $T_{y}$ . Data routing can only be performed when $N_{y}$ or $T_{y}$ of the node meet the threshold requirements, so that the data can be fully integrated to minimize the amount of data to be transmitted. More importantly, DTC-ORS-DA adopts differentiated threshold settings based on the characteristics of unbalanced energy consumption in WSNs, and sets a smaller threshold in the far sink area with sufficient energy, so that data packets can be routed quickly. And the near sink area where the energy is tight uses a larger threshold to maximize data fusion, so that the combination can make the data fusion high, the energy is effectively used, and the delay is small. (2) We propose a priority-based relay selection algorithm, which enables child nodes to dynamically select the parent node with the highest priority based on the number of data packets, waiting time, and remaining energy. In the process of routing, the probability of nodes with many data packets or long waiting time being selected as transmission relay is high, which can either increase the data fusion rate or reduce the delay. Finally, the performance comparison with Common data collection Scheme (CS) proves that, the DTC-ORS-DA scheme reduces the average delay by 10.74%-19.91%, increases the life cycle by 9.81% at most, and the energy utilization rate is increased by 6.67%-9.48%.

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An Efficient Data Aggregation Scheme Based on Differentiated Threshold Configuring Joint Optimal Relay Selection in WSNs

To ensure the security of Internet of Things (IoT) communications, one can use deep packet inspection (DPI) on network middleboxes to detect and mitigate anomalies and suspicious activities in network traffic of IoT, although doing so over encrypted traffic is challenging. Therefore, in this article, an efficient and privacy-preserving encrypted traffic detection scheme is proposed. The scheme uses only lightweight cryptographic operations (i.e., symmetric encryption, hash functions, and pseudorandom functions) to achieve both privacy and security within an inspection round. A dispute resolution mechanism is also designed to address potential disputes between client(s) and server(s). We also present the corresponding security proof and experimental evaluation, which demonstrate that our proposed scheme achieves strong security and privacy preservation and good performance. 

Privacy-Preserving Encrypted Traffic Inspection With Symmetric Cryptographic Techniques in IoT

Integration of Blockchain and Edge Computing in Internet of Things: A Survey

A comparative study of machine learning classifiers for secure RF-PUF-based authentication in internet of things

To ensure the security of Internet of Things (IoT) communications, one can use deep packet inspection (DPI) on network middleboxes to detect and mitigate anomalies and suspicious activities in network traffic of IoT, although doing so over encrypted traffic is challenging. Therefore, in this article, an efficient and privacy-preserving encrypted traffic detection scheme is proposed. The scheme uses only lightweight cryptographic operations (i.e., symmetric encryption, hash functions, and pseudorandom functions) to achieve both privacy and security within an inspection round. A dispute resolution mechanism is also designed to address potential disputes between client(s) and server(s). We also present the corresponding security proof and experimental evaluation, which demonstrate that our proposed scheme achieves strong security and privacy preservation and good performance.

As an emerging technology, industrial Internet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this article, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.

MAGLeak: A Learning-Based Side-Channel Attack for Password Recognition With Multiple Sensors in IIoT Environment

This study proposes a quantitative evaluation method of monocular video dance movements based on deep learning, and provides a scientific and quantitative dance movement evaluation system to help students and dance teachers quickly identify and judge their dance movement standards. In this paper, the neural network-based 3D human pose estimation method is used to realize the identification and collection of key points of video human pose and skeleton. Aiming at the problem that it is difficult for users to ensure the time consistency with the reference video in the actual video acquisition process, a video frame alignment method based on ant colony algorithm is proposed. Aiming at the problem of large coordinate displacement caused by the tester's height, fat and thinness, and different video shooting angles, the evaluation and analysis method of moving human body posture based on similarity matching between feature planes is used to solve the problem. The scores of the input movements, the user video and the benchmark video are output at the same time, and provided to dance learners, so that the students' “non-standard” dance movements can be identified in a timely and effective manner, and the evaluation results are fed back to the students in a friendly way, so that Students can correct dance movements in time to achieve better training results.

Yaohua Luo

Papers

MAGLeak: A Learning-Based Side-Channel Attack for Password Recognition With Multiple Sensors in IIoT Environment

Research on Dance Movement Evaluation Method Based on Deep Learning Posture Estimation