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sensors and information Technology (ICSI), International conference on

作者： Xiaoping Tian Xiaoyan Zhang Yunlong He Song Wu College of Information Engineering Beijing Institute of Petrochemical Technology Beijing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the widespread application of wireless sensor networks in intelligent transportation, urban security, and other fields, the efficient identification of target movement directions has become a pressing challenge. To address this issue, this paper proposes a dual-channel analysis method based on deep learning for accurate identification of target movement directions. First, sensor nodes are deployed to collect dual-channel Received Signal Strength Indication (RSSI) signals generated by target movement. The mean and variance are calculated using a sliding window, and time-series feature maps are constructed through Z-Score normalization. A residual network (ResNet50) is then employed to automatically learn features related to movement direction. Experiments demonstrate that the ResNet50 model achieves an identification accuracy of 80.43% in indoor scenarios, surpassing the traditional VGG16 model by 10.86%. The residual structure effectively captures waveform amplitude variations associated with movement direction. This method provides a novel technical solution for applications such as traffic flow monitoring and abnormal behavior detection.

关键词： Deep learning Wireless sensor networks Accuracy Time series analysis Zigbee Feature extraction Windows Security Monitoring Residual neural networks

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Energy Efficient and Trust Based Multi-hop Routing Using Particle Swarm Induced Tunicate Swarm Optimisation Algorithm in Internet of Things Based Wireless sensor networks

Energy Efficient and Trust Based Multi-hop Routing Using Par...

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International conference on 6G Communications Networking and Signal processing, SGCNSP 2023

作者： Lande, Jayapal Borra, Subba Reddy Abood, Baydaa S. Z. Dineshkumar, P. Geetha, K. School of Computer Science and Artificial Intelligence SR University Warangal India Maisammaguda Telangana Hyderabad India National University of Science and Technology Dhi Qar Iraq Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Excel Engineering College Komarapalayam India

ISBN: (纸本)9789819778751

Internet of Things (IoT)-Wireless sensor networks (WSNs) depend heavily on routing, particularly multi-hop routing, which makes large-scale deployments of data transmission efficient. Multi-hop routing increases overall dependability, extends network lifetime, and enables communication between sensors through intermediary nodes. In order to solve energy problems in WSN, a multi-hop routing architecture is developed that is both secure and energy efficient. The two key stages of the framework are transmission and clustering. Energy-efficient cluster heads are chosen during the clustering phase based on a number of factors, including throughput, security, packet delivery ratio (PDR), communication cost, intra- and inter-cluster distances, and residual energy. For optimal Cluster Head (CH) selection based on various objectives, the novel Particle Swarm Induced Tunicate Swarm Optimisation Algorithm (PITSO), a combination of Particle Swarm Optimisation Algorithm (PSO) and Tunicate Swarm Algorithm (TSA), is utilised. Furthermore, choosing the shortest path between CHs determines multi-hop communication. As a result, the proposed model develops a multi-hop routing framework for WSNs that is safe and energy efficient, improving the reliability and efficiency of the network resulting 0.09 J of energy consumption, 1.325 ms end to end delay, 2156.23kbps of throughput, and 0.82 trust at 100 nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： sensor nodes

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Orthopedic Walker Fall Detection and Motion Classification Using Bidirectional Gated Recurrent Unit Neural Network 10

Orthopedic Walker Fall Detection and Motion Classification U...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Accurate fall detection for orthopedic walker users is essential for timely medical intervention and enhancing safety for elderly and mobility-impaired individuals. This paper introduces a new method using a bidirectional gated recurrent unit (BiGRU) neural network to detect falls and classify motion patterns in walker usage. We use the Walker dataset, which includes inertial measurement unit sensor data for four motion classes: idle, motion, step, and fall. The dataset consists of 6-axis IMU readings (3-axis accelerometer and 3-axis gyroscope) from sensors mounted on orthopedic walkers, with around 620 samples per class. Our BiGRU model leverages temporal dependencies in both forward and backward directions to improve classification accuracy. We assess the model's performance in two scenarios: binary classification (fall vs. non-fall) and multi-class classification of all four motion types. The proposed architecture shows strong performance, achieving 99.79% accuracy and a 99.73% F1-score in binary classification, and 98.51% accuracy and a 98.50% F1-score in multi-class classification. It outperforms traditional deep learning models like CNN, LSTM, and GRU. The experimental results confirm the effectiveness of bidirectional processing in capturing temporal motion patterns, indicating significant potential for realtime fall detection in walker-assisted mobility. The high precision and recall values in both scenarios demonstrate the model's reliability for practical healthcare monitoring applications. © 2025 IEEE.

关键词： Recurrent neural networks

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A Comparative Study of Validation Methods for sensor-Based Human Activity Recognition Using Deep Learning Models 10

A Comparative Study of Validation Methods for Sensor-Based H...

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作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) - on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems. © 2025 IEEE.

关键词： Convolutional neural networks

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CSRP Protocol for High Accuracy Performance in Wireless sensor networks

CSRP Protocol for High Accuracy Performance in Wireless Sens...

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Computing and information Technology (ICCIT-1441), 2020 International conference on

作者： Ahmed Subhi Abdalkafor Salah A. Aliesawi College of Computer Science and Information Technology University of Anbar Anbar Iraq

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

In recent years, Wireless sensor Network (WSN) has played a broad role in many vital applications that are used to monitor environmental and physical events. One of the major issues in WSN is the overlapping region of sensing bands, which occurs due to densely and randomly deployed nodes in the target environment. The data aggregation technique is used as a solution that processes overlapping regions. This technique manages and redirects these data to the sink node through a multi-hop network. Therefore, the data transmission number and the duration toward the sink node are reduced, which fixes the overlapping region issue. However, using data aggregation techniques limited network performance due to energy consumption. The objective of this paper is to address this limitation by employing intelligent algorithms to enhance the operation strategies of data aggregation techniques. A protocol based on the Hierarchical Agglomerative Clustering (HAC), Self-Organized Map (SOM), and Enhanced Radial Basis Function (ERBF) neural network is proposed in order to accomplish this goal. The clustered SOM and RBF-based intelligent Protocol (CSRP) is the name of this proposed protocol. When compared to pertinent recent studies, the suggested protocol produced the best results in terms of the accuracy of the data arriving at the base station.

关键词： Training Wireless sensor networks Base stations Protocols Accuracy Artificial neural networks Spread spectrum communication Radial basis function networks Data aggregation sensors

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Construction of Computer Network Security Monitoring Platform Based on intelligent sensor Technology

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Security and Privacy 2025年第2期8卷

作者： He, Guoliang Cyberspace Great Wall Guangdong Ltd Guangdong Zhongshan China

As computer networks become ubiquitous, ensuring their security has become a critical concern. Existing network security monitoring systems still face challenges, including network intrusions and data loss. Therefore, the security of some confidential information and files has decreased and a new type of computer network security monitoring platform inevitably emerges. This paper constructs the computer network security monitoring platform from the aspects of network architecture design, intelligent sensor selection and deployment, monitoring server establishment, and system monitoring maintenance, analyzes the fitness function of the computer network security monitoring platform combined with particle swarm optimization algorithm, and evaluates the resource utilization, data storage capacity, and other performance of the platform in the experiment. In the experiment, it was found that the resource utilization and data storage capacity of the computer network security monitoring platform under intelligent sensors have significantly improved. Results demonstrate significant improvements with the introduction of intelligent sensors. Specifically, resource utilization increased by 12% and data storage capacity improved by 11%, highlighting the effectiveness of intelligent sensors in enhancing platform performance. Additionally, tests on the platform's processing capabilities revealed a 62% reduction in response time compared to traditional systems, with response times ranging from 0.23 to 0.50 s. intelligent sensor technology is of great significance in improving the reliability and fault tolerance of computer network security monitoring platforms. © 2025 John Wiley & Sons Ltd.

关键词： Network intrusion

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Smart Agriculture using Cloud Computing

Smart Agriculture using Cloud Computing

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International conference on ent Computing and Control Systems (ICICCS)

作者： Vijayashaarathi S Saranya K Dhivyadharsan R Akilesh S Sasirekha N Hari Rajkumar J Department of ECE Sona College of Technology Salem India Department of ECE Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Smart agriculture could increase output while preserving resources in the face of rising global food demand and environmental concerns. This project's objective is to develop a smart agriculture system that uses DHT11 sensors, ESP32 microcontrollers, and soil sensors to track important environmental data including temperature, humidity, and soil moisture. Real-Time data collection from the sensors is done by the ESP32 microcontroller, which serves as the central processing unit. While the DHT11 sensor detects temperature and humidity, two critical elements affecting plant growth and health, the soil sensor offers information on soil moisture levels, assisting in effective irrigation management. The ESP32 microcontroller is programmed using Arduino, which permits the sensors to be integrated seamlessly and makes data gathering and processing easier. After being gathered, the data is sent to the cloud-based IoT platform Thing Speak for analysis and visualization. Farmers can remotely obtain important information about the environmental conditions of their crops by utilizing this smart agriculture technology. This makes it possible to take prompt action, make the most use of available resources, and eventually increase agricultural yield and quality.

关键词： Temperature sensors Smart agriculture Cloud computing Microcontrollers Soil moisture Crops Humidity Internet of Things intelligent sensors Farming

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Semantic sensor Network Ontology for Forest Fire Management Decision Support

Semantic Sensor Network Ontology for Forest Fire Management ...

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intelligent Control, Computing and Communications (IC3), International conference on

作者： S Kirubakaran Ramy R. Hussein Thanneeru Venkata Ramakrishna Duraipandi Gurram Vijendar Reddy Sandeep C S Department of CSE CMR College of Engineering & Technology Hyderabad Telangana India Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq General Surgery Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Technology Sethu Institute of Technology Pulloor Kariapatti Tamil Nadu India Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of ECE Jawaharlal College of Engineering and Technology Palakkad Kerala

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Forests are essential resources for each country, since they are essential to maintaining environmental equilibrium. The negative effects of deforestation, especially those caused by forest fires, highlight the need of efficient management and monitoring. When determining the risk of a fire, giving warnings, and assigning resources for emergency management, fire weather indices are essential measures. Utilising sensor networks-Semantic sensor networks (SSN) in particular-improves the ability to gather data on a range of meteorological factors, including temperature, relative humidity, and wind speed. processing data streams produced by these sensors poses difficulties for estimating fire weather indicators. This methodology guarantees the system's flexibility in responding to changing circumstances, facilitating reasoning and preserving the open world's generic semantics as established by all participating ontologies. The suggested fire management system contributes to effective forest fire prevention and control by functioning in accordance with the current circumstances

关键词： Temperature measurement Forests Wildfires Web services Wind speed Semantics Humidity Ontologies Cognition Monitoring

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On Integrating Robotic Data with GIS Tools in a Cloud Environment (Application Paper)

On Integrating Robotic Data with GIS Tools in a Cloud Enviro...

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Workshops of the EDBT/ICDT 2025 Joint conference, EDBT/ICDT-WS 2025

作者： Wrembel, Robert Kasprzyk, Jean-Paul Billen, Roland Bimonte, Sandro d'Orazio, Laurent Sacharidis, Dimitris Skrzypczyński, Piotr Université de Liège Liège Belgium INRAE Clermont-Ferrand France Université de Rennes Rennes France Université Libre de Bruxelles Bruxelles Belgium Poznan University of Technology Poznań Poland

Merging robotic technologies, sensor networks, and Geographic information Systems (GIS) offers significant potential across various domains, including agriculture and urban planning. However, a critical challenge lies in the lack of interoperability between data generated by these technologies and existing GIS tools. The EU-funded GIS4IoRT project addresses this gap by developing a plug-and-play and cloud-based middleware. This middleware facilitates seamless integration and visualization of multi-dimensional and multi-modal data within GIS environments. Key GIS4IoRT components include: a middleware architecture, a scalable cloud-based infrastructure, real-time robot querying capabilities, data quality assurance, spatio-temporal query support within the cloud, integration with GIS tools, and adherence to relevant standards. The middleware supports diverse data types, including LiDaR, imagery, and sensor data. This paper (1) presents an initial data integration architecture specifically designed for the sustainable architecture domain, (2) outlines the challenges encountered in designing such an architecture, and (3) explores novel data processing paradigms enabled by the architecture. © 2025 Copyright © 2025 for this paper by its authors.

关键词： data integration geographical information system images LiDaR robots sensors sustainable agriculture

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Multi-sensor Fusion for Environmental Perception and Decision-Making in Smart Fertilization Robots

Multi-Sensor Fusion for Environmental Perception and Decisio...

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sensors and information Technology (ICSI), International conference on

作者： Guangping Qiu Jincan Li Jiahang Zhang Jizhong Deng Weixing Wang Zhujiang College of South China Agricultural University Guangzhou China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

To address the issues of resource wastage, environmental pollution, and low crop productivity associated with traditional agricultural fertilization methods, we present a smart fertilization robot system based on multi-sensor fusion. This system integrates visual (OpenMV), temperature and humidity (DHT11), and light intensity (BH1750) sensors, leveraging advanced algorithms such as Kalman filtering, Bayesian inference, and Long Short-Term Memory (LSTM) networks to achieve robust environmental perception and precise fertilization decision-making in complex agricultural settings. Experimental results demonstrate significant improvements in fertilization accuracy and resource efficiency, with a 20% reduction in fertilizer waste and an increase in fertilization precision from ±20% to ±2% compared to manual fertilization. The system also exhibits strong adaptability and stability under varying light intensities, soil moistures, and crop growth stages. Our study not only provides an effective technological solution for precision agriculture but also paves a new way for the application of multi-sensor fusion in agricultural robots.

关键词： Precision agriculture Temperature sensors Decision making Soil moisture Crops Robot sensing systems sensor systems Kalman filters Long short term memory intelligent sensors

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