Recent years have witnessed the huge popularity of Information-Centric Networking (ICN) and its realization as Named Data Networking (NDN) in the context of wireless sensor networks (WSNs). The participating nodes in WSNs are usually equipped with a single radio interface. The existing solutions lack in providing the efficient next forwarder selection in NDN-based single radio WSNs. In this work, we propose a collaborative Interest and Data Forwarding (CIDF-WSN) Strategy for Named Data Wireless Sensor Networks. CIDF-WSN develop a Neighbor Information Base (NFIB) which enables the node to select the optimal next-hop relay in Interest packet forwarding. An efficient Interest packet processing mechanism assisted by the Interest Cache Table (ICT) is provided to avoid Interest packets loss and frequent re-transmissions. In addition, CIDF-WSN also provides a robust Data packet transfer mechanism accompanied by the Temp Cache Table (TCT) to avoid Data packet losses and to ensure well-timed content delivery. Simulation results reveal that CIDF-WSN outperforms the recently published works in terms of Interest satisfaction rate, total energy consumption, Data retrieval delays, and communication overhead.

CIDF-WSN: A Collaborative Interest and Data Forwarding Strategy for Named Data Wireless Sensor Networks

Physics-informed learning for industrial Internet is essential especially to safety issues. Consequently, various methods have been developed to conduct Industrial Internet of Things (IIoT) intrusion detection. However, the conventional methods usually require the help of auxiliary equipment (e.g., spectrum analyzers, log-periodic antennas), which proves to be unsuitable for general IIoT systems due to their poor versatility. Facing the dilemma mentioned above, this article proposes a microcontroller unit (MCU) chip temperature fingerprint informed machine learning method, called MTID, for IIoT intrusion detection. Specifically, first, the node's MCU temperature sequence is recorded and the relationship between the temperature sequence and the computational complexity of the node is analyzed. Then, we calculate the temperature residuals and construct a temperature residuals dataset. Finally, to identify the security status of the nodes, a self-encoder-based intrusion detection model is constructed. Furthermore, to ensure the model's applicability under the diversified deployment environment of IIoT systems, an online incremental training method is developed and applied. In the end, we use the Raspberry Pi 4B for experimental analysis when testing the performance of MTID. The results show that the accuracy of MTID for intrusion detection reaches 89%, which also demonstrates the feasibility of the intrusion detection method based on MCU temperature. 

Microcontroller Unit Chip Temperature Fingerprint Informed Machine Learning for IIoT Intrusion Detection

A novel health assessment method for pumped storage units (PSUs) is presented in this article. First, multihead self-attentive mechanism (MSM) combined with quantile regression neural network (QRNN) are proposed to establish a health benchmark model for PSUs to reveal the intricate relationship between the vibration and its multiple influencing factors. Especially, MSM automatically learns the complex interaction features among multiple influencing factors, while QRNN explores the upper bounds of health vibration under specific operational parameters. Then, a fuzzy dimensionless function is constructed to map the deviation of the currently measured vibration from the predicted health vibration to the performance degradation indexes. Finally, an improved radar chart method is proposed to visually illustrate the health condition of multiple measurement locations and give comprehensive health assessment for PSUs. The proposed method is applied in a PSU in Zhejiang province of China. The results of comparative experiments illustrate its effectiveness and feasibility. 

Health Condition Assessment for Pumped Storage Units Using Multihead Self-Attentive Mechanism and Improved Radar Chart

Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).

/pdf/a-review-of-fingerprint-sensors-mechanism-characteristics-2immjfaa.pdf

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Queue stability and dynamic throughput maximization in multi-agent heterogeneous wireless networks

Selecting reliable beacon nodes plays a significant role in relocalizing unknown nodes in a wireless sensor network When the position of a beacon node is drifted or is spoofed, it becomes an unreliable beacon node, which would lead to a large relocalization deviation of unknown nodes in its neighbor However, when selecting reliable beacon nodes, most relocalization algorithms only screen either drifting beacon nodes or malicious beacon nodes whose position is drifted or spoofed This article proposes an algorithm that can simultaneously screen drifting beacon nodes and malicious beacon nodes The algorithm is divided into four steps First, three indicators are introduced, where two are for describing position drifting and one is for describing position spoofing Second, the entropy method is used to weight the contributions of three indicators Third, a technique for order preference by similarity to an ideal solution is used to construct a reliability evaluation model Finally, using the reliability evaluation model select reliable beacon nodes Experimental results illustrate that the detection accuracy of drifting beacon nodes and malicious beacon nodes of the proposed algorithm is 75% and 82% higher than that of the state-of-the-art algorithms, respectively

A TOPSIS-Based Relocalization Algorithm in Wireless Sensor Networks

Detection of weak electromagnetic interference attacks based on fingerprint in IIoT systems

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A TOPSIS-Based Relocalization Algorithm in Wireless Sensor Networks

Detection of weak electromagnetic interference attacks based on fingerprint in IIoT systems