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检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

共 5112 条记录，以下是21-30 订阅

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Optimized Low-Power Mixed-Signal sensor systems: Improved Cross-Domain Approach to Maintain Classification Accuracy 3

Optimized Low-Power Mixed-Signal Sensor Systems: Improved Cr...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kumar, M. Kiran Musnmusawi, Muntather Kodati, Sarangam Suresh, G.R. Suresh, T. Anurag University Department of Electronics and Communication Engineering Hyderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Cvr College of Engineering Department of Information Technology Hyderabad India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering chennai India

ISBN: (纸本)9798331508456

Optimizing the mixed-signal systems-on-Chips (SoCs) is a difficult task, particularly while including analog developing blocks and Machine Learning (ML)-based algorithms that make overall performance hard to predict. These designs for minimizing energy consumption increases the lifetime from harvested energy from the environment. In this manuscript, a method is developed for optimizing difficult mixed-signal sensor system that includes three phases. Initially, Pareto-optimal local performance of individual circuits is modelled, developing the macro-model of mixed-signal system performance involving local nonidealities and identifying optimum group of system-level parameters utilizing numerical optimization. The method is employed for Electrocardiogram (ECG) sensor system with integrated arrhythmia classification with minimizing system energy consumption and constraints on inference accuracy. Various numerical optimization algorithms are compared to evaluate its performance for minimizing task are Bayesian Optimization and Gradient-based optimization. The experimental results obtained optimization in reasonable execution time of 9.5 h while compared to existing algorithms on complex mixed-signal system. © 2025 ieee.

关键词： Pareto principle

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Data Fusion Algorithm in Agricultural Internet of Things System 3

Data Fusion Algorithm in Agricultural Internet of Things Sys...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Wang, Fang School of Business Changchun College of Electronic Technology Changchun China

ISBN: (纸本)9798331508456

Agricultural production relies on a large number of sensors to obtain environmental data, and how to effectively integrate this data has become a major challenge in current agricultural IoT applications. Therefore, this article proposes a novel data fusion algorithm. Firstly, the weighted average method is used to preliminarily fuse data from different types of sensors;Then, deep neural networks (DNNs) are used to further extract potential features from the fused data. Finally, the weighting strategy and network model were optimized through experiments. The experimental results indicate that the research method performs well in terms of accuracy, spatiotemporal consistency, and real-time performance. Compared with the weighted average method, this research method reduced the mean square error (MSE) by about 29% and increased the dynamic time warping (DTW) score by about 40.7%. The results verified that the method is practical in improving data quality and suitable for large-scale data processing in agricultural IoT. © 2025 ieee.

关键词： sensor data fusion

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Voice Command Fingerprinting: An in-Depth Examination of Challenges and Issues 3

Voice Command Fingerprinting: An in-Depth Examination of Cha...

引用

3rd ieee international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Singh, Paras Nath Mathews, Ann Antu, Anisha Suryanarayana, Ch.V.S. GMR Institute of Technology Department of CSE Rajam532127 India CMR Institute of Technology Department of CSE Bengaluru560037 India Federal Institute of Sc. & Technolgy Department of CSE Kerala Angamaly683577 India Mallareddy Engg. College Maisammaguda Department of CSE-AIML Secundrabad500100 India

ISBN: (纸本)9798331533205

Voice assistant systems have gained widespread adoption across diverse sectors, including electronics, healthcare, automotive, and retail. Spoken Language Understanding is essential for voice-text interaction systems. Security researchers have already identified vulnerabilities in encrypted network traffic, particularly in the context of traffic analysis, enabling the network-level adversaries to infer the websites visited by users. This security threat, common in traditional web communication, extends to Website Fingerprinting attacks in voice query communication, giving rise to the concept of Voice Command Traffic Fingerprinting. This paper analyses the features, challenges and linguistic properties of VCF(Voice Command Fingerprinting) considering various features. A detailed analysis reveals that the proposed models for voice command categories and factors are more capable to handle the issues. The results and methods in this paper pave the way for further research on the secured models in voice command processing in. The Python tools like Tensor-flow, Scikit-learn and specially Pyttsx3 have been used for generating and filtering datasets. An CTC-Attention-based hybrid model recurrent unit which effectively combine the two models. The proposed model achieves over 99.7% accuracy. © 2025 ieee.

关键词： distributed computer systems

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Optimization of Node Deployment in Underwater sensor Networks Using Maximum Coverage and Connectivity Self-Deployment Algorithm Based on Particle Swarm Optimization 3

Optimization of Node Deployment in Underwater Sensor Network...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Renuka, G. Gurumurthy, V. Hameed, Raed Chidambaram, P.K. Chinnakathiyan, Sudhakar Anurag University Department of Electronics and Communication Engineering Hyderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan University Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331508456

In recent years, Underwater Wireless sensor Networks (UWSNs) monitor underwater environment such as aquatic life. Moreover, to enhance network efficiently, sensor nodes are strategically placed in a 3D underwater space. However, underwater networks failed to find the optimal depth for each sensor in a 3D underwater network and failed in maintaining the connectivity to the surface station. Further, this research proposes a Maximum Coverage and Connectivity self-deployment algorithm based on Particle Swarm Optimization namely (MCC-PSO) for maximum coverage of water surface. Initially, the system models such as Network Scenario and Coverage Model are designed to optimize node deployment in UWSNs by simulating realistic environments. Then the problem identified that the optimal depth for N sensors deployed on a water surface to maximize network coverage and ensure connectivity to the surface station. To address this, a MCC and Connectivity Scheme is designed, Further, the MCC-PSO enhanced for optimizing the node development and PSO optimizes moving particles in search space by dynamic adaptation to environmental factors, such as node distribution and communication range. From the results, proposed MCC-PSO achieves a 50% higher maximum coverage rate compared to the existing Growth Ring Style Uneven Node Depth-Adjustment Self-Deployment Optimization Algorithm (GRSUNDSOA). © 2025 ieee.

关键词： Particle swarm optimization (PSO)

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PROCEEDINGS - 3rd international conference ON distributed computing systems.

PROCEEDINGS - 3RD INTERNATIONAL CONFERENCE ON DISTRIBUTED CO...

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Proceedings - 3rd international conference on distributed computing systems.

作者： Anon

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Multi-Agent systems for Autonomous IoT Network Management Using distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 ieee.

关键词： distributed Reinforcement Learning IoT Network Management Multi-Agent systems Resource Optimization

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Innovative AI Approaches in Oral Proficiency Testing: Ethical Implications of ASR systems 3

Innovative AI Approaches in Oral Proficiency Testing: Ethica...

引用

3rd ieee international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Periyasamy, A. Manoharan, Geetha Alazzam, Malik Bader Mythili, M. Giftsy Dorcas, E. Prema, S. Rajalakshmi Institute of Technology Department of English Chennai India School of Business SR University Telangana Warangal India Jadara University Faculty of Information Technology Irbid Jordan Department of English Erode India Department of English Karnataka Bengaluru India Panimalar Engineering College Department of English Chennai India

ISBN: (纸本)9798331533205

Oral proficiency testing plays a critical role in language assessment;however, classic ASR faces such problems as Americans Bias, which means difficulty for ASR as application to recognize non-American accents, and the evaluation inaccuracy. The major problem here is that classic ASR systems cannot recognize and assess non-American accents as they would reward non-biased, unbiased, and fair scores for speakers with regional or non-native English accents. This bias in particular distorts the fairness of the oral proficiency test. The current approaches are inefficient with regards to the fairness and standardization in natural language scenarios. Unfortunately, in practice the conventional ASR models are not perfect and become a source of errors and biases when trying to assign a score for the given speech;the specific problem with the current approach is that the speakers with regional or non-native accent will be almost impossible to score correctly. To address this bias, this study proposes BERTAdv, incorporating BERT which focuses on contextual understanding into the Adversarial training method for enhancing the ASR's accuracy in OPI. In an attempt to improve the BERTAdv to analyze both simple and complex linguistic attributes, the model is trained on a large and balanced dataset. The model presents desirable accuracy with a high R-squared of 0.93, MAE of 0.26, and RMSE of 0.46 and is more accurate and fairer than existing methods. © 2025 ieee.

关键词： distributed computer systems

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Design and Implementation of a Personalized Recommendation System Based on Deep Learning distributed Collaborative Filtering Algorithm on Social Media Platforms 3

Design and Implementation of a Personalized Recommendation S...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Chen, Junyu Alexa Music Amazon SeattleWA United States

ISBN: (纸本)9798331508456

Social media platforms mainly provide personalized recommendation services for users, and usually adopt recommendation methods related to user or project similarity based on the characteristics of different platforms. However, current recommendation techniques mostly rely on simple similarity calculations, which can significantly reduce accuracy when dealing with large amounts of data. To address this issue, this paper proposes a collaborative filtering recommendation method based on social media gene maps. Specifically, this article draws on the concept of genes in biology, collects social media user behavior data, extracts biological genes, social genes, and network gene fragments, calculates the core gene matching degree of each gene fragment, and uses entropy weight method to assign different weights to gene fragments, thereby constructing a complete social media gene map and optimizing the recommendation mechanism. The experimental results show that this method outperforms traditional recommendation algorithms in terms of recommendation performance. Subsequently, this article proposes to combine social media gene maps with project similarity to further improve recommendation algorithms. When measuring project similarity, a fusion of scoring similarity and semantic similarity was used, combined with user gene matching degree, to optimize the weighted Slope One algorithm and achieve more accurate recommendation services. The results indicate that the algorithm proposed in this paper has significantly improved in evaluation metrics such as MAE and MRSE. © 2025 ieee.

关键词： Tweets

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 ieee.

关键词： Agricultural Automation Fruit Trees Real-Time Data Processing sensor Integration Sustainable Farming

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A Hybrid Framework for Secure Group Communication Using Quantum-Classical Cryptography and Reinforcement Learning 3

A Hybrid Framework for Secure Group Communication Using Quan...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Renisha, P.S. Rudra, Bhawana National Institute of Technology Department of Information Technology Karnataka Surathkal India

ISBN: (纸本)9798331523893

This research work describes a framework for secured and effective way of group interaction incorporating classical cryptography and quantum communication technique. This framework employs a classical cryptographic methods for group logistics like as transmitting of messages and membership management while employing quantum communication strategy for secure key distribution and related authentication. In order to increase the flexibility and data-security of the proposed system further, a supervisor learning based mechanism is incorporated into the framework where reinforcement learning will employed in controlling the interaction of the protocols and the decision-making processes in an active manner. This combination of classical, quantum and supervisor learning strategies gives a solution to the issues of scalability, efficiency, effective and timely actionable response to increasing cyber threats especially in the era of quantum computing. The framework is highly effective and secured for group interaction in distributed network infrastructures. It will be a leading the approach for advanced cryptographic mechanism in the future. © 2025 ieee.

关键词： authentication classical cryptography distributed systems hybrid framework key distribution machine learning quantum communication quantum computing reinforcement learning secure group communication

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