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

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ICDCN 2023 - Proceedings of the 24th international conference on distributed computing and Networking

ICDCN 2023 - Proceedings of the 24th International Conferenc...

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24th international conference on distributed computing and Networking, ICDCN 2023

ISBN: (纸本)9781450397964

The proceedings contain 64 papers. The topics discussed include: analyzing program transitions to compute benefit of tolerating consistency violation faults;GeoChain: a locality-based Sharding protocol for permissioned blockchains;impact of redundancy on resilience in distributed optimization and learning;the snow plow problem: perpetual maintenance by mobile agents on the line;self-stabilizing synchronous unison in directed networks;dynamic graph models inspired by the bitcoin network-formation process;scalable skill-oriented task allocation in crowdsourcing within a serverless ecosystem;distributed data minimization for decentralized collaborative filtering systems;the geodesic mutual visibility problem for oblivious robots: the case of trees;almost-surely terminating asynchronous byzantine agreement against general adversaries with optimal resilience;learned load balancing;structured hypergraphs in cellular mobile communication systems;and an approximation algorithm for path planning of vehicles for data collection in wireless rechargeable sensor networks.

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BlinkRadar: Non-Intrusive Driver Eye-Blink Detection with UWB Radar 42

BlinkRadar: Non-Intrusive Driver Eye-Blink Detection with UW...

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Hu, Jingyang Jiang, Hongbo Liu, Daibo Xiao, Zhu Dustdar, Schahram Liu, Jiangchuan Min, Geyong Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Vienna Univ Technol Comp Sci Heading Res Div Distributed Syst Vienna Austria Simon Fraser Univ Sch Comp Sci Burnaby BC Canada Univ Exeter Coll Engn Math & Phys Sci Exeter Devon England

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

The eye-blink pattern is crucial for drowsy driving diagnostics, which has become an increasingly serious social issue. However, traditional methods (e.g., with EOG, camera, wearable, and acoustic sensors) are less applicable to real-life scenarios due to the disharmony between user-friendliness, monitoring accuracy, and privacy-preserving. In this work, we design and implement BlinkRadar as a low-cost and contact-free system to conduct fine-grained eye-blink monitoring in a driving situation using a customized impulse-radio ultra-wideband (IR-UWB) radar which has superior spatial resolution with the ultra-wide bandwidth. BlinkRadar leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. BlinkRadar aims to single out the eyeblink induced waveforms modulated by body movements and vehicle status. It solves the serious interference caused by the unique characteristics of blinking (i.e., subtle, sparse, and nonperiodic) and from the human target itself and surrounding objects. We evaluate BlinkRadar in a laboratory environment and during actual road testing. Experimental results show that BlinkRadar can achieve a robust performance of drowsy driving with a median detection accuracy of 92.2% and eye blink detection of 95.5%.

关键词： Eye Blink detection RFID Signal Drowsy driving detection

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Quantitative Analysis and Optimization of sensor Deployment for Enhanced Activity Recognition in Smart Home Environments: A Multidimensional Approach to Data-Driven Intelligence and Efficiency in Universal computing Spaces

Quantitative Analysis and Optimization of Sensor Deployment ...

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2024 international conference on Advancements in Power, Communication and Intelligent systems, APCI 2024

作者： Aziz, Khalid Dua, Sakshi Gupta, Prabal Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350363289

This paper presents a comprehensive study on the enhancement of smart home automation and monitoring systems through the application of advanced activity recognition algorithms and sensor deployment optimization. We start by taking a CASAS dataset capturing a wide range of daily living activities, detected through an array of environmental sensors. This study conducts an in-depth analysis of activity recognition performance, leveraging precision, recall, and f1score metrics to identify areas for improvement in existing machine learning models. Further, we investigate sensor activation data to propose optimizations in sensor deployment, aimed at increasing the accuracy of activity recognition while reducing unnecessary sensor data processing. Through temporal analysis of activity patterns, we develop predictive models that forecast future activities with significant accuracy, facilitating smarter resource management and personalized automation strategies. This paper also explores the implications of our findings on user experience enhancement. Our methodology showcases the integration of machine learning techniques, statistical analysis, and practical considerations for deploying smart home technologies. The results indicate a promising avenue for future research and development in smart environments, with potential applications extending beyond residential settings to include healthcare facilities and office spaces. The findings underscore the importance of adaptive and intelligent systems in enhancing the quality of life through technology. © 2024 ieee.

关键词： Smart homes

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AFNT: A Secure Data Storage Scheme Based on IOTA Tangle for Wireless sensor Networks

AFNT: A Secure Data Storage Scheme Based on IOTA Tangle for ...

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2023 ieee international conference on Metaverse computing, Networking and Applications, MetaCom 2023

作者： Wang, Shiyun Ye, Qiang Liu, Kai Dalhousie University Faculty of Computer Science Halifax Canada University of Pei School of Math and Cs Charlottetown Canada

ISBN: (纸本)9798350333336

Blockchain is a distributed ledger technology that enables cryptocurrencies, such as Bitcoin and Ethereum. However, it suffers from the scalability problem, which means that not all generated blocks can be added to the chain in a timely manner. For the Internet of Things (IoT), which involves a large number of interconnected devices, blockchain is infeasible due to the scalability issue. Instead, a different distributed ledger technology, IOTA Tangle, is expected to be widely adopted in IoT applications. Nevertheless, it has been found that the tip selection and weight update operation of IOTA Tangle could negatively affect its efficiency. To tackle this problem, Fishing Net Topology (FNT) was proposed to completely eliminate the tip selection and weight update operation. However, in our research, we noticed that the width of the fishing net used in FNT remains unchanged, which has a serious impact on the performance of FNT. In this paper, we focus on Wireless sensor Networks (WSNs), which are often used to implement IoT applications. Specifically, we present a secure data storage scheme based on FNT for WSN, Adaptive FNT (AFNT). Our experimental results indicate that AFNT outperforms FNT in terms of layer number, average fishing net width, percentage of over-approved nodes, and percentage of over-approving nodes. © 2023 ieee.

关键词： Blockchain

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Sending Query Data to Mobile Sinks at High Speed in Wireless sensor Networks 9

Sending Query Data to Mobile Sinks at High Speed in Wireless...

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9th international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Singh, Charanjeet Subba Reddy, V. Vyas, Narayan Krishan Gupta, Mridul Kushwaha, Sumit Prasanna, N.M.Sri DeenbandhuChhotu Ram University of Science And Technology Department of Electronics And Communication Engineering Haryana Murthal131039 India KoneruLakshmaiah Education Foundation Department of Electronics And Communication Engineering Andhra Pradesh Guntur522302 India Government Engineering College Department of Electronics And Communication Engineering Rajasthan Jhalawar326001 India Graphic Era Deemed To Be University Department of Electronics & Communication Engineering Uttarakhand Dehradun248002 India University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Department of English Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350365092

Recently, many strategies to extend the lifespan of wireless sensor networks have made use of sink flexibility (WSNs). Mobile sink-based systems function with different power utilization between hubs, leading to power porting, in contrast to classic WSNs where haptic data from sensor arrays is ultimately communicated by static sinks. decrease the issue of The hub should record the most recent location of mobile sinks for data transfer if flexibility is needed. Containment detection and organizational networks are two of the most crucial elements for ensuring powerful natural detection and high-performance data communication in WSN (Wireless sensor Network) applications. This project aims to set up, test, and enhance a data collecting framework that solves the issue of disconnected hubs, enhances the network between sensor hubs and base stations, and uses less power. Also, this paradigm aids in addressing some problems including unpredictable behavior and conflicts of interest. This study focuses on the potential applications for the suggested approach. Exploratory work that is truly ongoing will aim to provide a detailed analysis of cuttingedge applications, such as transport frameworks for common uses. © 2024 ieee.

关键词： Wireless sensor networks

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Extended Kalman Filter Algorithm to Detect False Data Injection in Wireless sensor Network 2

Extended Kalman Filter Algorithm to Detect False Data Inject...

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2nd international conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Babuji, R. Ganesan, J. Jeya Lekha, D. Karthikeyan, M. Kalai Selvi, D.M. Rmk Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Wireless sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio. © 2023 ieee.

关键词： Extended Kalman filters

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distributed Computation for Centralized MMSE Combining in Cell-Free Massive MIMO systems 23

Distributed Computation for Centralized MMSE Combining in Ce...

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23rd ieee international conference on Communication Technology, ICCT 2023

作者： Liu, Junkai Dai, Weijie Zhang, Jingjing Jiang, Yi School of Information Science and Technology Fudan University Department of Communication Science and Engineering Shanghai China

ISBN: (纸本)9798350325959

Cell-Free (CF) massive multiple-input multiple-output (MIMO) technology is poised to revolutionize future wireless networks by significantly improving network capacity. However, the conventional centralized implementation of CF networks brings about computational complexities in the central processing unit (CPU) and imposes a heavy burden on fronthaul links due to extensive channel state information (CSI) exchange. In this paper, we propose a distributed computing architecture that employs the preconditioned conjugate gradient algorithm to achieve the performance level of centralized minimum mean square error (MMSE) combining. The proposed algorithm offers two key advantages: firstly, it offloads a significant portion of computations from the CPU to distributed access points, thereby alleviating the computational burden;secondly, it significantly reduces the transmission of instantaneous and historical data through fronthaul links. These benefits contribute to the overall efficiency and scalability of CF massive MIMO systems, as demonstrated in the simulation results. © 2023 ieee.

关键词： Cell-Free massive MIMO centralized MMSE combining conjugate gradient algorithm distributed computing

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sensor-Integrated Transformer-RF Model for HAR 30

Sensor-Integrated Transformer-RF Model for HAR

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30th ieee international conference on Parallel and distributed systems, ICPADS 2024

作者： Kang, Yisen Wang, Zheng Sun, Xiaoqi Wang, Huan Lu, Ruiqi Zou, Dengpeng Liao, Mingyuan Shi, Xiaokang Wang, Yanwen Li, Renfa Central South University School of Computer Science and Engineering Hunan Changsha410083 China China Electric Power Research Institute Artificial Intelligence Application Research Center Beijing100192 China Hunan University Hunan Changsha410082 China Langfang Normal University School of Life Sciences Langfang Hebei065000 China State Grid Hunan Electric Power Company Limited Hunan Changsha410004 China

ISBN: (纸本)9798331515966

The precise classification of human activities through sensor data collection and analysis addresses the broad demands in healthcare, security surveillance, and smart home applications amidst the rapid development of IoT technology. However, achieving high efficiency and accuracy remains a significant challenge for HAR algorithms. This paper proposes a HAR algorithm based on Transformer and Random Forest (Transformer-RF). The algorithm extracts and integrates multimodal features in the time domain, frequency domain, and statistical metrics, constructing one-dimensional and two-dimensional feature sets through feature transformation. The Transformer component, leveraging self-attention mechanisms, captures long-range dependencies and extracts global contextual information. Concurrently, the Random Forest component randomly selects features and samples, enhancing model diversity and improving complex human activity recognization capabilities. Experimental results demonstrate that compared with state-of-the-art algorithms, the Transformer-RF model achieves superior performance on both one-dimensional and two-dimensional feature sets, with an accuracy of up to 94.17%. The primary contribution of this paper lies in the introduction of an innovative Transformer-RF human activity recognization method, which not only ensures high accuracy but also exhibits excellent generalization capability and practical application potential. This study provides new insights and technical solutions for the field of human activity recognization, offering significant theoretical and practical value. © 2024 ieee.

关键词： Frequency domain analysis

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Hybrid Parameter Update: Alleviating Imbalance Impacts for distributed Deep Learning 24

Hybrid Parameter Update: Alleviating Imbalance Impacts for D...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Hongliang Xu, Dong Xu, Zhewen Li, Xiang College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China

ISBN: (纸本)9798350319934

Nowadays, data parallelism has been widely applied to train large datasets on distributed deep learning clusters, but it has suffered from costly global parameter updates at batch barriers. Performance imbalance among worker instances, introduced by uneven workload partitioning or biased resource allocation, can cause straggly workers, which can lead to severe impacts on both training speed and result accuracy. This paper studies the issue focusing on the tradeoff between training speed and result accuracy. We propose Cooperate Grouping Parallel (CGP), a hybrid parameter update solution that allows the flexibility of both synchronous and asynchronous update schemes. We introduce a novel Cooperate Worker Grouping Problem (CWGP) that seeks a task grouping configuration that leads to maximum model accuracy and holds customized training speed guarantees. We propose an evolution-based Pareto local searching algorithm to compute efficient worker grouping configurations. Comprehensive evaluation results are presented to demonstrate the effectiveness of CGP under both persistent and fluctuant imbalances. The proposed method alleviates the imbalance impacts without introducing extra adjustment over-heads. © 2022 ieee.

关键词： Large dataset

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Transforming AI and Quantum computing to Streamline Business Supply Chains in Aerospace and Education

Transforming AI and Quantum Computing to Streamline Business...

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2024 international conference on TVET Excellence and Development, ICTeD 2024

作者： Mosaddeque, Ahmed Inan Guria, Zenith Matin Morshed, Niaz Sufian, Mohammad Abu Ahamed, Asif Tamim Hossain Rimon, S.M. International American University Business Administration Department Los Angeles United States Westcliff University College of Business IrvineCA United States Westcliff University College of Engineering and Technology IrvineCA United States Qatar University Management and Marketing Department Doha Qatar

ISBN: (纸本)9798331516413

The article analyzes the effects of artificial intelligence (AI) and quantum computing on improving the business supply chain systems in the aerospace and education industries. With the increase of technology within various sectors, it becomes evident that AI and Quantum computing are complementing logistics advances to bring down the cost of operations and enhance efficiency in the management of the supply chain. The aerospace sector, predictive maintenance, decision support systems, and improved resource allocation are examples of what these technologies make possible. In the education sector, individual students can move through the curriculum at their own pace, the curriculum can be modified based on Big Data on student performance and educational resources can be better distributed. The current paper looks at how AI and Quantum computing as new technologies are fused to enhance creativity and revolutionize agility to adapt to changes in the environment and consequently the business models of the firms involved here. These developments not only improve productivity but also pave the way for the two industries to engage in new forms of inter-discipline collaborations leading to progress in both sectors. © 2024 ieee.

关键词： Students

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