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检索条件"任意字段=International Conference on Sensor Technologies and Applications, SensorComm"

共 9413 条记录，以下是101-110 订阅

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Enhancing Network Lifetime in IoT-Based Wireless sensor Networks Through MSSA-Driven Cluster Head Optimization

Enhancing Network Lifetime in IoT-Based Wireless Sensor Netw...

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Disruptive technologies (ICDT), international conference on

作者： Rajaram Pichamuthu Saravanan Matheswaran Prabaharan Sengodan K. Rajkannan Prabhakaran. M School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Engineering and Technology Chennai India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The integration of the Internet of Things with WSN s has given emergence to various applications in industries starting from precision agriculture to healthcare, and smart cities. These networks completely depend on well-organized data collection and transmission, where energy consumption is one of the crucial issues. One of the major challenges in WSN is the optimum selection of cluster heads that can directly influence energy efficiency and network lifetime and hence the overall performance. In this context, this paper deals with this challenge by proposing a new method of selecting cluster heads based on Multi-Objective Salp Swarm Algorithm-MSSA. MSSA is proposed to deploy loT for data gathering in the WSN, therefore optimization of cluster head selection have been done with MSSA application. We assess the performance of this approach by comparing it with various established algorithms such as LEACH, PSO, GA, and FCM. These will provide performance metrics necessary for energy consumption, network lifetime, and packet delivery ratio; thus, all critical parameters determining WSN's sustainability could be given in an overview. The experimental results depict that the MSSA-based approach outperforms the benchmark algorithms in terms of energy consumption, lifetime elongation of the network, and data transmission efficiency. This research contributes to the literature by providing an advanced solution to the problem of cluster head selection, offering a more energy-efficient and scalable approach to IoT-enabled WSN applications, such as environmental monitoring and smart infrastructure management.

关键词： Wireless sensor networks Energy consumption Smart cities Clustering algorithms Medical services Energy efficiency Internet of Things Environmental monitoring Sustainable development Optimization

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Improving IoT Service Quality with the Allocation of Dynamic Bandwidth

Improving IoT Service Quality with the Allocation of Dynamic...

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Disruptive technologies (ICDT), international conference on

作者： Shekhar Vikash Bhardwaj Ammar Nadal Shareef Ramendra Singh Hasanain A.J. Gharban Israa M. Essa Department of ECE Dewan VS Institute of Engineering & Technology Meerut India Department of Electrical Engineering Dewan VS Institute of Engineering & Technology Meerut India College of Basic Education Al-Muthanna University Samawah Iraq Department of CSE IOT Raj Kumar Goel Institute of Technology Ghaziabad India College of Veterinary Medicine University of Wasit Wasit Iraq College of Veterinary Medicine University of Basrah Basra Iraq

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The Internet of Things (IoT) effectively facilitates interaction between individuals and devices in contemporary society. The IoT industry is experiencing substantial growth annually, attributed to many applications, from miniature sensors to large-scale industrial equipment. The proliferation of billions of connected devices creates distinctive business opportunities, while IoT applications contribute to economic enhancement. The IoT market is rapidly growing; yet, it encounters several challenges, including security issues, heterogeneity, and absence of defined protocols, communication topology, and bandwidth (BW) selection. This paper aims to enhance the comprehension of providing Internet of Things devices with stable bandwidth to increase communication. This article primarily contributes an elucidation of mechanisms associated with optimized dynamic bandwidth selection, prevalent protocols, and network topologies.

关键词： Protocols Network topology Quality of service Bandwidth Channel allocation sensor phenomena and characterization Topology Internet of Things Security Standards

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Smart Drying technologies: A Case Study on Red Flesh Apple (Kissabel®) Using Computer Vision and Deep Learning

Smart Drying Technologies: A Case Study on Red Flesh Apple (...

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international Mid-Term conference of the Italian Association of Agricultural Engineering, MID-TERM AIIA 2024

作者： Benelli, Alessandro Pandolfi, Flavia Bandiera, Andrea Taormina, Eleonora Russo, Paola Adiletta, Giuseppina Massantini, Riccardo Moscetti, Roberto Department for Innovation in Biological Agro-Food and Forest Systems University of Tuscia Viterbo Italy Department of Chemical Engineering Materials Environment Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031842115

Food drying is a crucial process that helps in enhancing the shelf-life of food at ambient temperature, also lowering transportation costs. However, erroneous applications can degrade product quality and lead to a significant energy consumption as common dryers use fossil fuels. The present study aims to develop a "smart" prototype hot-air dryer augmented with Computer Vision (CV) and Deep Learning (DL) technologies to monitor quality and drying status of red-flesh sliced apples (Malus domestica B. - cultivar Kissabel®) during the process. Both CV and DL represent Process Analytical Technology (PAT) tools that, combined with a Quality by Design (QbD) approach, can lead to real-time data from the process, which are potentially vital for understanding and controlling the drying dynamics of products. The experimentation was conducted on apple slices (approx. 5-mm thick) using the following drying parameters: drying time, 24 h;temperature, 35 ℃;R.H., 35%;and airflow, 3 m s−1. The "smart" drying system consisted of (i) a CMOS digital camera;(ii) a LED illumination setup (4200 K);(iii) a load cell;(iv) an Arduino UNO microcontroller (master);(v) a Jumo Dicon Touch controller (slave);(vi) a temperature and R.H. sensor;and (vii) a Raspberry Pi 4 microcomputer. The open-source software Node-Red was used as an orchestrator for controlling devices, as well as acquiring and storing product features in real-time, such as weight, color, size, and shape of slices. All acquired images of apple slices were subjected to semantic segmentation using a deep learning model. With the slice shrinkage data obtained from semantic segmentation and the moisture data, time-independent moisture ratio prediction models were developed. The approach opens new perspectives for the use of CV and DL technologies in food drying. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer vision deep learning moisture red flesh apples shrinkage smart drying

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Hospital Bed Vacancy Detection using Depthwise Separable CNN

Hospital Bed Vacancy Detection using Depthwise Separable CNN

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Advanced Computing technologies (ICoACT), international conference on

作者： R Balaji V Deepan S Faiz J Mythili Satyanarayana Murthy Polisetty Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Bank of America United States

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The rising demand for real-time hospital bed man-agement in healthcare institutions requires creative solutions to improve patient care and resource allocation. This research introduces an IoT-enabled system for detecting hospital bed vacancies utilising a Depthwise Separable Convolutional Neural Network (DSCNN). The proposed system utilises IoT sensors to perpetually monitor bed occupancy, guaranteeing precise and prompt updates to medical personnel. The DSCNN is utilised to diminish computing complexity while preserving high detection accuracy, attaining a notable accuracy of 92.4%. The model's performance is assessed in comparison to traditional CNNs, revealing notable enhancements in both speed and accuracy, with accelerated convergence times enabled by the Adam optimiser. The system guarantees minimal latency in hospital bed detection, rendering it suitable for real-time applications on resource-limited devices. This system provides a scalable and effective method for optimising hospital resource management, especially during high demand or emergencies.

关键词： Accuracy Hospitals Rendering (computer graphics) Real-time systems sensor systems sensors Internet of Things Convolutional neural networks Resource management Personnel

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1.2 From Chips to Thoughts: Building Physical Intelligence into Robotic Systems

1.2 From Chips to Thoughts: Building Physical Intelligence i...

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IEEE international conference on Solid-State Circuits (ISSCC)

作者： Daniela Rus CSAIL Massachusetts Institute of Technology Cambridge MA

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

The rapid growth of AI technologies has brought unprecedented advancements across numerous domains, from healthcare to autonomous systems, yet this progress has been accompanied by substantial energy demands. Generative AI has revolutionized the accessibility of advanced machine learning models, putting powerful AI capabilities directly into the hands of everyday users. Tools that generate text, images, video, and audio have made AI more democratic, fueling creativity, productivity, and innovation across industries. This explosion of generative AI applications, from chatbots to art generators, has transformed AI from a niche tool into an indispensable asset in our digital lives, as many can now access these technologies through their phones or computers. However, this widespread accessibility comes with significant trade-offs, particularly in terms of the energy required to train and deploy these enormous models. Large generative models such as GPT, DALL-E, and other multi-modal AI systems demand immense computational resources, translating to high energy consumption both during the extensive training process and when they are used in practice.

关键词： Energy consumption Technological innovation Liquids Generative AI Computational modeling Energy efficiency State-space methods Smart grids Artificial intelligence sensor arrays

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Comprehensive Mix-Method Approach for Efficient Waste Management using IoT-Driven Solutions

Comprehensive Mix-Method Approach for Efficient Waste Manage...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Neha Gupta School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad Haryana

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Escalating the global waste management crisis calls for innovative technological interventions transforming the age-old practice of traditional waste handling. The present system remains mostly inefficient, labor-intensive, and environmentally unsustainable even when rising levels of urbanization and consumption of resources increase waste production. This study examines the transformative potential of IoT technologies in developing intelligent data-driven solutions for waste management to solve critical infrastructure challenges in urban centers. This study developed IoT-enabled smart waste collection systems in three metropolitan areas using a comprehensive mixed-method approach that includes quantitative sensor data analysis, geospatial mapping, and longitudinal municipal waste tracking. Advanced sensor networks and associated machine learning algorithms were used to monitor volume, composition, and efficient collections of wastes. Predictive analytics and the edge computing framework were adapted to optimize routes for better waste collection, reduce associated operational costs, and hence also minimize the environmental footprint. The key results are possible savings of 37% in waste collection costs, an increase of 42% in route optimization for collections, and a reduction of several percent in greenhouse gas emissions due to smart routing and scheduling. IoT-led strategy outperformed conventional strategies for waste management with better operational efficiency, further signifying the revolutionary changes brought by digital technologies in managing urban infrastructure.

关键词： Waste management Waste handling Technological innovation Costs Urban planning Geospatial analysis Predictive analytics Sustainable development Optimization Edge computing

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IoT-based Women's Safety Monitoring in Educational Campuses using WSN and Deep Learning

IoT-based Women's Safety Monitoring in Educational Campuses ...

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Intelligent Data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Pallavi Raj Sinha Department of Electronics & Communication Jayoti Vidyapeeth Women's University Jaipur Rajasthan India School of Computer Applications Lovely Professional University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The recognition of human activity is significant in such domains as smart environments, security, and health. A novel approach to IoT-based women's safety monitoring at educational campuses using a hybrid deep learning model that combines CNNs and LSTMs has been proposed in this study. Using wearable inertial sensors, the system classifies six predetermined activities - walking, walking upstairs, walking downstairs, sitting, standing, and laying. The Human Activity Recognition dataset preprocessed with noise filtering and feature extraction was utilized in training and testing. Spatial features are extracted from CNNs while the LSTMs capture temporal dependencies, giving a 95.38% accuracy that is augmented with F1-score, precision, and recall exceeding 95%. This approach offers a robust, scalable safety solution for monitoring of activities that would be accomplished in real- world educational settings.

关键词： Legged locomotion Deep learning Training Wireless sensor networks Feature extraction Safety Internet of Things Human activity recognition Monitoring Wearable sensors

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Designing a Context-Aware Virtual Machine (CWVM) to Enhance Data Integration in Pervasive Wireless sensor Networks (PWSN) 1st

Designing a Context-Aware Virtual Machine (CWVM) to Enhanc...

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1st international conference on Computational technologies and Electronics, ICCTE 2023

作者： Das, Sushovan Mondal, Uttam Kr. Department of CSE College of Engineering & Management Kolaghat WB Kolaghat India Department of Computer Science Vidyasagar University WB Midnapore India

ISBN: (纸本)9783031819803

Pervasive Wireless sensor Networks (PWSNs) have emerged as a prominent technology for diverse applications such as environmental monitoring, industrial automation, healthcare, smart cities, etc. However, the efficient integration of data from numerous sensor nodes in a WSN remains a challenging task due to the inherent limitations of resource-constrained sensor devices and dynamic environmental conditions. This paper presents a comprehensive study on enhancing data integration in PWSNs by leveraging context-aware techniques. The proposed approach aims to improve data integration processes’ accuracy, reliability, and overall performance those are essential for informed decision-making in various applications. This work explores the concept of context awareness, that is, contextual information where contextual information related to sensor nodes, network topology, environmental conditions, and user requirements during data aggregation, fusion, and transmission under a universal Context-aware Virtual Machine (CWVM) in heterogeneous pervasive mode. The proposed method has been deployed with a scalable and flexible solution for deploying context-aware applications in heterogeneous IoT (Internet of Things) environments with improved data accuracy where leaf nodes may differ significantly in their hardware capabilities and configurations. Through comprehensive real-world experiments, the effectiveness of the context-aware approach is demonstrated with improving data accuracy, reducing energy consumption, and enhancing overall network performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data accuracy

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Exploring IoT and Machine Learning Integration for Soil Nutrients Monitoring and Crop Recommendation: A Survey

Exploring IoT and Machine Learning Integration for Soil Nutr...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： Vijaya R. Khemnar Mininath R. Bendre Vikas K. Kolekar Department of Computer Engineering Pravara Rural Engineering College Loni Department of Computer Engineering Vishwakarma Institute of Information Technology Pune

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The integration of machine learning (ML) and Internet of Things (IoT) technologies has a scope of improvement in precision farming techniques and revolutionise the agriculture sector. This research paper examines the integration of IoT and ML in soil nutrient monitoring and crop recommendation systems. The aim is to provide a thorough overview of the most recent developments, applications, challenges, and potential future directions. IoT sensors collect and give immediate soil health information, while ML algorithms analyses these data to suggest nutrient deficits and recommend the most suitable crops. This research work evaluates advancements in IoT and Machine Learning applications, emphasising their benefits for precision agriculture. This integration aims to improve agricultural production and sustainability via data driven decision making and resource optimisation. At the end research gaps are discussed, along with future research directions.

关键词： Precision agriculture Machine learning algorithms Crops Machine learning Soil Nutrients sensor systems sensors Sustainable development Monitoring

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Neural Networks in Human Activity Recognition: Trends, Challenges, and Future Directions

Neural Networks in Human Activity Recognition: Trends, Chall...

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Intelligent Data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： C. Valli R. Amutha Department of EEE Sri Venkateswaraa college of Technology Sriperumbudur Department of ECE Sri Sivasubramaniya Nadar College of Engineering Kalavakkam

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Human Action Analysis is a procedure in tracking individual gestures, motion and actions which finds applications, including healthcare, surveillance, security and human-computer communication. With the proliferation of sensors and deep learning techniques, HAR has marked remarkable development in the past few years. This review paper gives a brief survey of the current studies in Activity Analysis, focusing on deep learning-based approaches. The various sensors employed in HAR, including accelerometers, gyroscopes, and vision-based sensors were used in acquiring signals. The main objective is to design an appropriate model for recognizing/classifying human actions effectively to support elderly and differently abled people. The evolution of deep learning architectures used in detection such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks can be utilized for action recognition. The characterization of these algorithms is evaluated using various metric indices including precision, accuracy, recall, Fl-score, and mean average precision. The challenges and limitations of existing HAR systems, including data purity, imbalance nature of class, and positioning of sensor were also presented. This review aims to provide a clear picture in Action Recognition, highlighting the Pros and cons of different approaches. By identifying the prospects and obstacles, this review seeks to inspire future research in behaviour analysis, ultimately leading to the development of more accurate, efficient, and robust systems.

关键词： Deep learning Surveys Accuracy Reviews Tracking Surveillance sensor systems Human activity recognition Convolutional neural networks Long short term memory

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