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

共 30822 条记录，以下是1661-1670 订阅

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Develop Improved Weighed Quantum Particle Swarm Optimization for distributed MIMO and NOMA systems in Energy Harvesting Mobile-Edge computing

Develop Improved Weighed Quantum Particle Swarm Optimization...

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2024 international conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Balasupramani, A. Tamilselvan, S. Puducherry Technological University Department of Electronics and Communication Engineering Puducherry India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Due to the complex nature of the goal function in meeting demands that ensure sufficient Quality of Service (QoS), such as minimizing the number of determined bits, limiting the total power consumption by users, maximizing CPU speed, ensuring minimum harvested power, and managing computation times, the problem is non-convex and difficult to solve. Mobile Edge computing (MEC) represents a significant digital transformation trend that addresses issues associated with cloud computing. However, implementing Industrial Internet of Things (IIoT) MEC networks is challenging due to underlying power and bandwidth constraints. This article proposes a new approach to optimize dynamic resource planning and energy efficiency in MEC environments. Specifically, it focuses on power distribution in Multiple-Input Multiple-Output (MIMO) and Non-Orthogonal Multiple Access (NOMA) systems using the Improved Weighted Quantum Particle Swarm Optimization (IWQPSO) method. The proposed approach leverages the unique capabilities of IWQPSO to tackle the intricate, non- linear optimization problems inherent in these systems. By incorporating quantum computing principles, the IWQPSO method enhances convergence speed and solution accuracy, leading to more efficient use of energy harvesting. The research demonstrates significant improvements in resource planning and power allocation efficiency, enhancing overall system performance. Simulation results validate the success of the proposed strategy, highlighting substantial energy savings and improved resource management in future wireless networks. © 2024 ieee.

关键词： distributed MIMO Dynamic Resource Scheduling Energy Harvesting Energy-Efficient Power Allocation Improved Weighted Quantum Particle Swarm Optimization Mobile-Edge computing NOMA systems Quantum computing Wireless Networks

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A distributed Message Queuing System Based On Hbase-like K-v Databases

A Distributed Message Queuing System Based On Hbase-like K-v...

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2023 international conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Yaxian, Wang Ran, Li Guangdong Mechanical & Electrical Polytechnic School of Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350312492

Message queues, as a low-latency, high-concurrency, and easily accessible data processing middleware, can improve the reliability and efficiency of data processing, and have become an indispensable part of large data and computing systems. For some users, the demand for data security is more prominent, and this article provides a message queue implementation method based on an hbase-like key-value database, which uses the database as a foundation to implement a distributed message queue that can freely control consumer progress and ensure temporal consistency. The safety and performance of this method are equivalent to that of the db components used, and different security levels of databases can be flexibly used as the basic component. In terms of the read-write mechanism of the message, this article has also designed it in detail to achieve optimal performance. © 2023 ieee.

关键词： Queueing theory

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Performance Analysis of a Data Stream Processing System for Online Activity Classification via Wearable sensor Data

Performance Analysis of a Data Stream Processing System for ...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Pour, Hawzhin Hozhabr Ciortuz, Gabriela Lüers, André Fudickar, Sebastian Institute of Medical Informatics University of Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Department of Informatics University of Oldenburg Carl von Ossietzky Universität Oldenburg Ammerländer Heerstraße 114-118 Oldenburg26129 Germany

Online activity recognition based on wearable sensors is commonly used in sports and medicine applications. The question of whether cloud or edge computing approaches are more suitable is not easy to answer and depends on several factors. To address this issue, the influence the resource availability, batch sizes and number of considered users on the throughput and latency of central data stream processing architectures has yet to be answered. This article conducts a performance analysis, identifying relevant factors for a corresponding cloud-based online data stream processing platform for online human activity recognition, using the Apache Spark data processing framework and the Apache Kafka distributed messaging system. The platform focuses on quantitative performance criteria to evaluate its effectiveness in terms of latency (turnaround time) and throughput (number of users). Both metrics, throughput and latency (dependent variables), depend on the batch interval, number of users, and hardware availability (independent variables). In addition to identifying clear advantages of larger batch intervals, we also found significant benefits in applying vertical scaling. The results indicate a monthly cost of 1e per user for compute resources in online activity recognition, a price that could potentially be reduced by combining edge and cloud computing. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Data streams

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Learning-based Co-Design of distributed Edge Sensing and Transmission for Industrial Cyber-Physical systems 19

Learning-based Co-Design of Distributed Edge Sensing and Tra...

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ieee 19th international conference on Industrial Informatics (INDIN)

作者： Jin, Tiankai Ji, Zhiduo Zhu, Shanying Chen, Cailian Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781728143958

Industrial cyber-physical systems (ICPS) refer to an emerging generation of intelligent systems, where distributed data acquisition is of great importance and is influenced by data transmission. In the improvement of the overall performance of sensing accuracy and energy efficiency, sensing and transmission are tightly coupled. Due to the unknown transmission channel states in the harsh industrial field environment, intelligently performing sensor scheduling for distributed sensing is challenging. In this paper, edge computing technology is utilized to enhance the level of intelligence at the edge side and deploy advanced scheduling algorithms. We propose a learning-based distributed edge sensing-transmission co-design (LEST) algorithm under the coordination of the sensors and the edge computing unit (ECU). Deep reinforcement learning is applied to perform real-time sensor scheduling under unknown channel states. The conditions for the existence of feasible scheduling policies are analyzed. The proposed algorithm is applied to estimate the slab temperature in the hot rolling process, which is a typical ICPS. The simulation results demonstrate that the overall performance of LEST is better than other suboptimal algorithms.

关键词： Industrial cyber-physical systems (ICPS) distributed edge sensing sensing-transmission co-design deep reinforcement learning

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Enhanced Accuracy in Underwater Communication Prediction Utilizing LSTM Over RNN for Improved Efficiency 2

Enhanced Accuracy in Underwater Communication Prediction Uti...

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2nd international conference on Automation, computing and Renewable systems, ICACRS 2023

作者： Sandhiya, V. Sandhiya, J. Hema Malini, A. Archana, T. Department of ECE Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

Underwater communication systems experience obstacles like confined bandwidth, elevated energy consumption, propagation lags, end-to-end delay (E-ED), complex media accessibility control, resource allocation, and power constraints. These challenges are resolved using energy-efficient protocols, which fall into two categories: constraint-basedand constraint-free. The study conducts a comprehensive assessment of constraint-free protocols in our project, considering environmental impact, data transmission rates, efficiency, energy consumption, E-ED, and propagation delay. This analysis provides insights into the pros and cons of these protocols, guiding future research in Underwater Wireless sensor Networks (UWSNs). The study also presents routing strategies and their strengths and weaknesses, identifying areas for further investigation. In this research work, algorithms such as Long-short Term memory algorithm (LSTM), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), and Artificial Neural Network (ANN), are employed and analyzed in terms of accuracy. The results demonstrate that Long-short Term memory algorithm (LSTM) outperforms better than other existing algorithms in terms of accuracy. © 2023 ieee.

关键词： Data Transmission Rate End to End delay (E-ED) Long-short term memory algorithm (LSTM) Recurrent Neural Network (RNN) Underwater wireless sensor network (UWSN)

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Managing Smart Urban Transportation with the integration of Big Data Analytic Platform 6

Managing Smart Urban Transportation with the integration of ...

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6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Kunjir, Sanika Nitin Patil, Sakshi Sanjay Hingane, Bhakti Sunil Pagariya, Janvi Anand Rashid, Mamoon Vishwakarma University Pune India

ISBN: (纸本)9798350304480

Urban traffic management is a major challenge for cities worldwide, but with the help of technology, such as big data analytics and intelligent transport systems, cities are working to improve their situations. An integrated big data analytics platform has the potential to change transportation networks for the better, providing real-time insights that can be used for decisions. By collecting and analyzing real-time data from various sources, such as traffic sensors and GPS, we can predict traffic flow and create plans more efficiently. This paper will explore how an integrated big data analytics platform can be used to improve urban transportation management. It discusses some of the challenges that urban transportation systems face and provides insights from government officials and industry experts. The importance of this paper reviews fundamental concepts such as big data analytics and smart cities so that you have a better understanding of what this technology can do. Afterwards, it identifies opportunities for improving urban transportation networks using big data analytics. © 2023 ieee.

关键词： Big data data analytics data quality distributed environment public transport management smart city

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Proceedings of the 2023 ieee/ACM international conference on Advances in Social Networks Analysis and Mining, ASONAM 2023

Proceedings of the 2023 IEEE/ACM International Conference on...

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15th ieee/ACM international conference on Advances in Social Networks Analysis and Mining, ASONAM 2023

ISBN: (纸本)9798400704093

The proceedings contain 122 papers. The topics discuss include: centralization problem for opinion convergence in decentralized networks;annotators’ perspectives: exploring the influence of identity on interpreting misogynoir;maximizing influence with graph neural networks;structure and dynamics of a charitable donor co-attendance network;how popularity shapes user interactions in tech-related online communities;together apart: decoding support dynamics in online COVID-19 communities;classifying severe weather events by utilizing social sensor data and social network analysis;the art of active listening;analyzing bias in recommender systems: a comprehensive evaluation of YouTube’s recommendation algorithm;exploring inequity in park usage amidst the COVID-19 pandemic;and understanding online attitudes with pre-trained language models.

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Symmetric and Asymmetric Algorithms for Susceptible Harmful in Wireless sensor Network on FPGA Platform 9

Symmetric and Asymmetric Algorithms for Susceptible Harmful ...

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9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Saranya, L. Arvind, C. Zhu Tang, Seng Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India University of Huaguoshan Department of Intelligent Robotics Huaguoshan Jileshijie Province China

ISBN: (纸本)9798350397376

WSNs are made up of a network of sensor nodes that are used to track environmental characteristics like pressure, temperature, etc. and communicate the data to the intended location. Networks must overcome obstacles including security, routing, problems with energy conservation, scalability, dependability, secrecy, integrity, authentication, and others when transmitting information. Different routing systems, including data-centric, location- and mobility-based, Quality of Service (QoS)-based, and multipath routing protocols, have been employed to address these issues. Even while these routing methods guarantee a successful transaction, the network still has to manage intermediary assaults that can completely alter the content of messages being carried. Therefore, a number of cryptographic approaches have been used to minimise the network. The newly developed symmetric and asymmetric algorithms are susceptible to problems with route latency, area overhead, and on-chip power consumption. Thus, to reduce these harmful actions in WSNs, an efficient reduced key size and high security based cryptographic method must be used. Utilizing the limited network resources, the main goal of the study is to increase network security while reducing network overhead and path delay-related problems. © 2023 ieee.

关键词： Network security

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Smart Agriculture: Leveraging IoT and Machine Learning in Wireless sensor Networks for Precision Farming

Smart Agriculture: Leveraging IoT and Machine Learning in Wi...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Sheela, M. Sahaya Leonid, T. Thomas Aravindhraj, N. Ravi, S. Giri, Pallavi Amer, Ayman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Kcg College of Technology Department of Cse Chennai India Kongu Engineering College Department of Cse Erode India Department of Vlsi Microelectronics Chennai India Laxminarayan Innovation Technological University Department of Civil Engineering Nagpur India Zarqa University Department of Energy Engineer Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

This research explores the challenge of incorporating IoT with Machine Learning (ML) applications to Precision Agriculture, particularly the use of wireless sensors for irrigation control. Information from sensors that monitor key parameters including temperature, humidity, moisture, and water levels are used to forecast water requirements and control pumps. Support vector machine (SVM), Decision tree (DT), Random Forest (RF) and Naïve Bayes (NB) techniques are adopted for the analysis to find out their accuracy levels. It is observed from all the above-performed models that among all, the SVM has the maximum prediction rate of the given dataset and is slightly outperformed by only the DT and RF. The confusion matrix which points to the misclassification patterns of each model gives an insight into identifying the best ML approach for use. The convergence of IoT and ML guarantees flexibility and responsiveness when controlling and designing agricultural systems. It is this approach that has the potential to enhance water resources management as well as address issues precipitated by climate change and growing worldwide food demand. © 2024 ieee.

关键词： Smart agriculture

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ASLPP-RR Routing Based on Improved IDAF-FIT Clustering with Secure Data Aggregation in Wireless sensor Network 3

ASLPP-RR Routing Based on Improved IDAF-FIT Clustering with ...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Garg, Megha Dogra, Yakesh Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

Wireless sensor Network (WSN) technology has recently emerged as a critical component for monitoring and tracking tasks across a variety of application domains. The most difficult problem now is developing a power-efficient data collection technique. This is due to the fact that each sensor node in the network only has a certain amount of power resources. Clustering is used to improve power efficiency, network connectivity, and delay minimization. Therefore, because of data aggregation and cluster head selection, approaches base on clustering for information collection assume an essential part as far as power-saving and expanding the lifetime of the network. The IDAF- FIT (Fuzzy and distributed Autonomous Fashion Integration The algorithm (If-then Rules) is suggested for clustering in this suggested approach, and in the interim, the Cluster kind of Head is also chosen. The routing concept is then started in order to transfer the packet from the source to the target node by selecting the best route. ASLPP-RR, Routing is suggested using an adaptive source location privacy preservation method. in this context. Additionally, Using Secure Data Aggregation based on Principle Component Analysis and integrity are preserved (SDA-PCA) technique. Finally, a thorough analysis of the security of sensitive data yields results that are superior to those of the current methods. The total performance of the suggested approach is described as a 20% increase in packet delivery ratio, a 15% decrease in packet dropping ratio, an 18% increase in residual power, a 22% increase in network lifespan, and a 16% decrease in power consumption when compared to existing. © 2023 ieee.

关键词： sensor nodes

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