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Reinforcement Learning-Driven Cluster Head Selection for Reliable Data Transmission in Dense Wireless sensor Networks

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International Journal of advanced Computer Science and applications 2025年第4期16卷 427-438页

作者： Du, Longyang Wang, Qingxuan Zhang, Zhigang School of Artificial Intelligence Jiaozuo University Henan Jiaozuo454000 China

Wireless sensor Networks (WSNs) have made significant advances towards practical applications. Data gathering in WSNs has been carried out using various techniques, such as multi-path routing, tree topologies, and clustering. Conventional systems lack a reliable and effective mechanism for dealing with end-to-end connection, traffic, and mobility problems. These deficiencies often lead to poor network performance. We propose an Internet of Things (IoT)-integrated densely distributed WSN system. The system utilizes a tree-based clustering approach dependent on the installed sensors' density. The cluster head nodes are structured in a tree-based cluster to optimize the process of gathering data. Each cluster's most efficient aggregation node is selected using a fuzzy inference-based reinforcement learning technique. The decision is based on three crucial factors: algebraic connectedness, bipartivity index, and neighborhood overlap. The proposed method significantly enhances energy efficiency and outperforms existing methods in bit error rate, throughput, packet delivery ratio, and delay. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： sensor nodes

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Abnormal Data Detection Model Based on Autoencoder and Random Forest Algorithm: Camera sensor Data in Autonomous Driving systems

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International Journal of advanced Computer Science and applications 2025年第3期16卷 222-231页

作者： Shengwen, Geng Osman, Mohd Hafeez Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia

This project develops an AI-based anomaly detection system. In the field of autonomous driving, abnormal data will directly affect the safety of autonomous driving systems, especially in terms of abnormal camera sensor data. sensor failure, environmental changes, or bad weather can lead to the emergence of abnormal data, which can affect the decision-making process and may have disastrous consequences. Based on the above problems, this study addresses this challenge by proposing a hybrid anomaly detection model (called CAE-RF) that combines convolutional autoencoders and random forest algorithms to achieve efficient and accurate identification of abnormal data patterns to improve the safety of autonomous driving systems. The proposed method will use convolutional autoencoders to calculate the reconstruction error and combine the hidden features extracted by the encoder as the input of the random forest to distinguish normal data from abnormal data. The key performance indicators such as accuracy, precision, recall, and F1 score are used to evaluate the model, and the robustness is guaranteed by cross-validation. Experimental results show that the CAE-RF model has an accuracy of 92% in distinguishing normal and abnormal data. Compared with traditional methods, the CAE-RF model achieves higher accuracy and reliability. The implementation of this model can timely identify and process abnormal data, reduce the risks brought by sensor failure or external environment changes, prevent potential accidents, and improve the safety and reliability of the autonomous driving system. © (2025), (Science and Information Organization). All rights reserved.

关键词： Tachometers

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Improving Efficiency in Multi-Modal Autonomous Embedded systems Through Adaptive Gating

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IEEE TRANSACTIONS ON COMPUTERS 2025年第2期74卷 691-704页

作者： Hou, xiaofeng xu, Cheng Li, Chao Liu, Jiacheng Tang, xuehan Cheng, Kwang-Ting Guo, Minyi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

The parallel advancement of AI and IoT technologies has recently boosted the development of multi-modal computing ((MC)-C-2) on pervasive autonomous embedded systems (AES).(MC)-C-2 takes advantage of data from different modalities such as images, audio, and text and is able to achieve notable improvements in accuracy. However, achieving these accuracy gains often comes at the cost of increased computational complexity and energy consumption. Furthermore, the presence of numerous advanced sensors in these systems significantly contributes to power consumption, exacerbating the issue of limited power resources. Collectively, these challenges pose difficulties in deploying (MC)-C-2 on small embedded devices with scarce energy resources. In this article, we propose an Adaptive Modality Gating technique called AMG for in-situ (MC)-C-2 applications. The primary objective of AMG is to conserve energy while preserving the accuracy advantages of (MC)-C-2. To achieve this goal, AMG incorporates two first-of-its-kind designs. Firstly, it introduces a novel semi-gating architecture that enables partial modality sensor power gating. Specifically, we devise the de-centralized AMG (D-AMG) and centralized AMG (C-AMG) architecture. The former buffers raw data on sensors while the latter buffers raw data on the computing board, which are suitable for different edge scenarios respectively. Secondly, it facilitates a self-initialization/tuning process on the AES, which is supported by carefully-built analytical model. Extensive evaluations demonstrate the effectiveness of AMG. It achieves a 1.6x to 3.8x throughput higher than other power management methods and improves the lifespan of AES by 10% to 280% longer within the same energy budget, while satisfying all performance and latency requirements across various scenarios.

关键词： sensors Accuracy Computer architecture Energy consumption sensor systems sensor arrays Hardware Embedded systems Computers Pipelines Edge AI autonomous embedded systems multi-modal DNNs modality gating architecture efficiency management

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Hierarchically-Interlocked, Three-Axis soft Iontronic sensor for Omnidirectional Shear and Normal Forces

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advanced MATERIALS TECHNOLOGIES 2025年第5期10卷

作者： Shen, Zequn Ren, Jieji Zhang, Ningbin Li, Jinhao Gu, Guoying Shanghai Jiao Tong Univ Robot Inst Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Meta Robot Inst Shanghai 200240 Peoples R China

Artificial tactile sensing capable of measuring shear and normal forces is crucial for the diverse human-machine interactions and dexterous robotic manipulations. However, existing soft multi-axis force sensors usually suffer from limited detectable directions and complex coupling mechanisms, limiting their applications in realistic wearable and robotic systems. Here, a hierarchically-interlocked, three-axis soft iontronic sensor is presented with an asymmetrical electrode pattern that leverages the mortise-and-tenon structure and ultracapacitive principle to detect omnidirectional shear and normal forces. The designed sensor facilitates the decoupling process and achieves enhanced sensing performances including high accuracy, fast response ability, and mechanical robustness. Prototypical integration and application of the sensor are demonstrated to perform wearable telecontrol of virtual platforms and assist robot gripper through closed-loop force feedback. A sensing array is further developed to construct a touch panel and identify handwriting based on the continuously measured directions of applied forces. This work may offer a potentially promising solution for the next-generation intelligent electronic skins requiring multimodal tactile sensing information. A hierarchically-interlocked, three-axis soft iontronic sensor is proposed to detect omnidirectional shear and normal forces. Asymmetrical electrode pattern with mortise-and-tenon sensing architecture facilitates decoupling process and achieves enhanced sensing performances. The fabricated sensor can be applied as electronic skins to provide three-axis force feedback for wearable devices and robots. image

关键词： hierarchically-interlocked structure omnidirectional shear and normal forces robotic skins three-axis soft iontronic sensors wearable electronics

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SEC-MAC: A Secure Wireless sensor Network Based on Cooperative Communication

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INTERNATIONAL JOURNAL OF advanced COMPUTER SCIENCE AND applications 2025年第1期16卷 79-87页

作者： Khairat, Yassmin Diab, Tamer O. Fawzy, Ahmed Osama, Samah Mahmoud, Abd El- Hady Elect Res Inst ERI Informat Res Dept Cairo Egypt Benha Univ Fac Engn Elect Engn Dept Banha Egypt Elect Res Inst ERI Nanotechnol Lab Cairo Egypt

Wireless sensor Networks (WSNs) are essential for a wide range of applications, from environmental monitoring to security systems. However, challenges such as energy efficiency, throughput, and packet delivery delay need to be addressed to enhance network performance. This paper introduces a novel Medium Access Control (MAC) protocol that utilizes cooperative communication strategies to improve these critical metrics. The proposed protocol enables source nodes to leverage intermediate nodes as relays, facilitating efficient data transmission to the access point. By employing a cross-layer approach, the protocol optimizes the selection of relay nodes based on factors like transmission time and residual energy, ensuring optimal end-to- end paths. The protocol's performance is rigorously evaluated using a simulation environment, demonstrating significant improvements over existing methods. Specifically, the protocol enhances throughput by 12%, boosts energy efficiency by 50%, and reduces average packet delivery delay by approximately 48% than IEEE 802.11b. These results indicate that the protocol not only extends the lifespan of sensor nodes by conserving energy but also improves the overall reliability and efficiency of the WSN, making it a robust solution for modern wireless sensor networks. Security in Wireless sensor Networks (WSNs) is crucial due to vulnerabilities like eavesdropping, data tampering, and denial of service attacks. Our proposed MAC protocol addresses these challenges by incorporating authentication techniques, such as the handshaking protocol. These measures protect data integrity, confidentiality, and availability, ensuring reliable and secure data transmission across the network. This approach enhances the resilience of WSNs, making them more secure and trustworthy for critical applications such as healthcare and security monitoring.

关键词： Wireless sensor Networks (WSNs) energy efficiency Media Access Control (MAC) cooperative communication handshaking algorithm

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2nd International conference on advanced Nanomaterials and applications, ICANA 2024

2nd International Conference on Advanced Nanomaterials and A...

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2nd International conference on advanced Nanomaterials and applications, ICANA 2024

ISBN: (纸本)9789819608270

The proceedings contain 10 papers. The special focus in this conference is on advanced Nanomaterials and applications. The topics include: GO/ZnO—Intensified Hybrid Nanomaterial as an Electrochemical sensor for Formaldehyde Detection in Environmental applications;synthesis of 4-Substituted 2,2′;6′,2″-Terpyridine Copper Complexes and Study of Their Anti-cancer Activities;synthesis of Cobalt Complexes and Study of Their Catalytic Activities in Various Organic Transformations;Recent Developments in Doped TMO and TMO-Based Composites for Energy Harvesting and Pollutant Removal systems;recent Progress in Multiferroics and Its Composites;dilute Magnetic Semiconductors and Its applications—An Overview;study of Melting Phenomenon of a Palladium Nanocluster Using Molecular Dynamics Simulations.

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Enhanced Percolation Effect in Sub-100 Nm Nanograting Structure for High-Performance Bending Insensitive Flexible Pressure sensor

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advanced ELECTRONIC MATERIALS 2025年第0期

作者： Yang, Jae-Soon Seo, Min-Ho Jo, Min-Seung Choi, Kwang-Wook Lee, Jae-Shin Chung, Myung-Kun Koo, Bon-Jae Yoo, Jae-Young Yoon, Jun-Bo Korea Adv Inst Sci & Technol KAIST Sch Elect Engn 291 Daehak Ro Daejeon 34141 South Korea Pusan Natl Univ Coll Informat & Biomed Engn Sch Biomed Convergence Engn 49 Busandaehak Ro Yangsan Si 50612 Gyeongsangnam D South Korea Pusan Natl Univ Dept Informat Convergence Engn 2 Busandaehak Ro 63 Beon Gil Busan 46241 South Korea Sungkyunkwan Univ Dept Semicond Convergence Engn Suwon 16419 South Korea

Flexible pressure sensors have emerged as indispensable components in advancing wearable electronics, healthcare systems, and next-generation human-machine interfaces. To enable these applications, significant progress has been made in improving the sensitivity of flexible pressure sensors. However, achieving bending insensitivity-crucial for reliable pressure detection under dynamic and curved conditions-remains a critical challenge. In this study, a high-performance flexible capacitive pressure sensor is presented that successfully integrates bending insensitivity with enhanced pressure sensitivity. By leveraging the percolation effect within a sub-100 nm nanograting structure, the design of the pressure sensor is optimized through numerical analysis and finite element method (FEM) simulations. Fabricated using a nanoscale wet-chemical digital etching process and nanoimprint lithography, the sensor features a sub-100 nm valley nanograting structure. It exhibits an exceptional sensitivity of 0.05 kPa(-)(1), achieving capacitance changes 4.2 times greater than those of flat substrate designs. Furthermore, the sub-100 nm nanostructured pressure sensor effectively reduces bending strain to 0.175 times that of flat substrates, ensuring stable performance even at a 2.5 mm radius of curvature. This highly reliable flexible pressure sensor array enables real-time pressure mapping and human artery pulse monitoring, making it highly suitable for tactile and wearable sensing applications.

关键词： bending insensitive flexible pressure sensor nanograting percolation effect wearable sensor

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Programmable and Scalable Embroidery Textile Resistive Pressure sensors for Integrated Multifunctional Smart Wearable systems

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advanced FIBER MATERIALS 2025年第2期7卷 574-586页

作者： Yang, Yiduo Chen, Yu Liu, Yang Yin, Rong North Carolina State Univ Wilson Coll Text Text Engn Chem & Sci Raleigh NC 27695 USA

Conformable and breathable textile structures are ideal for flexible wearable pressure sensors, yet challenges remain in scalable fabrication, easy integration, and programmability. This study presents a cost-effective and customizable method to create fully textile-based pressure sensors using machine embroidery, enabling seamless integration into smart wearable systems. Two sensing configurations were developed: a single-layer satin block embroidered with conductive yarn, which exhibited high piezoresistivity, fast response (35 ms), quick recovery (16 ms), and robust durability over 5000 press-and-release cycles, proven effective for monitoring activities such as plantar pressure and muscle contraction, and making it suitable for personalized health and fitness applications. The second configuration, a double-layer embroidery sensor with a conductive path and two parallel spacers anchored beneath a satin block, allows for array integration with minimal wiring, demonstrated by a 3 x 3 sensing array that, with the help of a convolutional neural network (CNN) machine learning model, accurately recognized handwritten numbers (0-9) with a 98.5% accuracy, showing its potential for user authentication and secure passcode entry. These findings underscore the potential of machine embroidery for developing scalable, integrated, and high-performance intelligent textile systems, paving the way for wearable technologies that are customizable, comfortable, and aesthetically appealing for a wide range of applications.

关键词： Smart textiles Human augmentation Health monitoring Interactive control User verification Machine learning

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Smart Night-Vision Glasses with AI and sensor Technology for Night Blindness and Retinitis Pigmentosa

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INTERNATIONAL JOURNAL OF advanced COMPUTER SCIENCE AND applications 2025年第2期16卷 962-975页

作者： Qazi, Shaheer Hussain Batumalay, M. INTI Int Univ Fac Data Sci & Informat Technol Nilai Malaysia

This paper presents the conceptualization of Smart Night-Vision Glasses, an innovative assistive device aimed at individuals with night blindness and Retinitis Pigmentosa (RP). These conditions, characterized by significant difficulty in seeing in low-light or dark environments, currently have no effective medical solution. The proposed glasses utilize advanced sensor technologies such as LiDAR, infrared, and ultrasonic sensors, combined with artificial intelligence (AI), to create a real-time, visual representation of the surroundings. Unlike conventional camera-based systems, which require light to function, this device relies on non-visible, non-harmful rays to detect environmental data, making it suitable for use in pitch-dark conditions. The AI processes the sensor data to generate a simplified, user-friendly view of the environment, outlined with clear, cartoon-like visuals for easy identification of objects, obstacles, and surfaces. The glasses are designed to look like regular prescription eyewear, ensuring comfort and discretion, while a button or trigger can switch them to "night mode" for enhanced vision in low-light settings. This concept aims to improve the independence, safety, and quality of life for individuals with night blindness and RP, offering a transformative solution where no medical alternatives currently exist. However, challenges such as sensor miniaturization, power consumption, and AI integration must be addressed for successful implementation. Beyond its direct benefits for users, the device could have broader societal and economic impacts by enhancing accessibility, reducing nighttime accidents, and fostering technological innovation in assistive wearables. The paper also discusses future directions for research and refinement of the technology while supporting the Process Innovation.

关键词： -Night-vision glasses night blindness Retinitis Pigmentosa (RP) IoT assistive technology sensor technology AI data processing low-light navigation wearable devices process innovation

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Application of big data analysis in improving the efficiency of sensor reliability testing

Application of big data analysis in improving the efficiency...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Wan, Wangjun Zhao, Minghua Song, Weijie Dong, Chunfa Ling, Zhixiang State Grid Jinan Power Supply Company Shandong Jinan250000 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

In modern technological applications, the reliability of sensors plays a crucial role in ensuring the proper functioning of systems. Therefore, sensor reliability testing has become an important aspect of quality control and system maintenance. However, traditional testing methods often face challenges in terms of efficiency and accuracy, especially when dealing with large-scale sensor data. In order to address these challenges, this paper proposes an innovative approach to improving sensor reliability testing efficiency by leveraging big data analytics, particularly through the application of neural networks. The method involves extracting features from different scales and abstraction levels using a multi-scale neural network architecture. Subsequently, the extracted detailed information is fused with high-level semantic features to minimize information loss, enhance feature utilization, and obtain advanced features, thereby improving the performance of the network. Additionally, a spatial attention module is introduced into the network to extract critical information and enhance overall network performance, effectively detecting sensor faults. Through comprehensive experiments conducted on real-world sensor data, the effectiveness and efficiency of the proposed method have been demonstrated, highlighting its significant potential in enhancing sensor reliability testing processes. This research not only contributes to the advancement of sensor reliability testing but also underscores the broader potential of big data analytics in improving the performance and reliability of complex technological systems. © 2025 SPIE.

关键词： Data reliability

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