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检索条件"任意字段=6th IEEE International Conference on Distributed Computing in Sensor Systems"

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Augmented Selection of Cluster Heads and Enlightening Network Lifetime in Underwater Wireless sensor Networks Using Grey Wolf Optimization (GWO) 6

Augmented Selection of Cluster Heads and Enlightening Networ...

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

作者： Hole, Snehal Zaboun, Abdul Razzaq T. Al-Ghanimi, Hayder Hussen, Omad Aldeen Alchilibi, Haider Abdul Kadeem, Sahar R. S.N.G Institute of Management and Research Pune India Al-Esraa University College Forensic Sciences Department Baghdad Iraq Techniques Engineering Hilla University College Department of Medical Instrumentation Babylon Iraq Al-Kunooze University College Computer Engineer Department Basra Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad10022 Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350304480

the Underwater Wireless sensor Network (UWSN) is a collection of sensor nodes deployed beneath the seabed to monitor and record challenging applications in offshore exploration and oceanic environments. In order to ensure proper energy consumption in a dynamic environment, the effective forwarding of data packets through efficient paths is crucial. Routing, therefore, plays a significant role in reducing power consumption by making informed decisions on packet destinations. In this study, we propose and analyze an efficient routing technique for underwater wireless sensor networks based on Grey-Wolf Optimization. the proposed technique is evaluated under various conditions, and the experimental results demonstrate a reduced number of iterations for selecting a cluster head, leading to a longer network lifetime compared to existing protocols such as Energy Efficient Routing Protocol (EERP) and Directional Flooding based Routing (DFR). © 2023 ieee.

关键词： DFR EERP Energy routing Grey wolf lifetime

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A distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems 48

A Distributed Adaptive Algorithm for Non-Smooth Spatial Filt...

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48th ieee international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Hovine, Charles Bertrand, Alexander Leuven Belgium Signal Processing and Data Analytics Stadius Center for Dynamical Systems Leuven Belgium Leuven Belgium

ISBN: (纸本)9781728163277

computing the optimal solution to a spatial filtering problems in a Wireless sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. the so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of the original optimization problem, relying solely on the exchange of compressed views of the sensor data between the nodes. However, the DASF algorithm has only been shown to converge for filtering problems that can be expressed as smooth optimization problems. In this paper, we explore an extension of the DASF algorithm to a family of non-smooth spatial filtering problems, allowing the addition of non-smooth regularizers to the optimization problem, which could for example be used to perform node selection, and eliminate nodes not contributing to the filter objective, therefore further reducing communication costs. We provide a convergence proof of the non-smooth DASF algorithm and validate its convergence via simulations in both a static and adaptive setting. © 2023 ieee.

关键词： Adaptive algorithms

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the Role of Blockchain Integration in Edge computing for 6G IoT: Challenges and Applications 7

The Role of Blockchain Integration in Edge Computing for 6G ...

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7th international conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Shtayt, Bushra Abdullah Barraood, Samera Obaid Adawy, Mohammad Ibrahim Al-Jamili, Omar InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Parit Raja Malaysia Information Technologies Management Management Technical College of Basra Southern Technical University Basra Iraq Department of Computer Science College of Computers and Information Technology Hadhramout University Hadhramout Yemen Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia

ISBN: (纸本)9798331519513

Blockchain is the technology on which cryptocurrencies operate within the 6G Internet of things. Due to its flexibility, it has received significant adoption in numerous applications as a possible distributed data management solution. Nonetheless, a significant obstacle that impedes blockchain's potential is its constrained scalability, which limits its capacity to facilitate applications requiring frequent transactions. Conversely, edge computing, an innovative technology, addresses the limitations of conventional cloud-based systems by decentralizing cloud functionalities and alleviating problems such as elevated latency and insufficient security. Nonetheless, edge computing presently encounters challenges related to decentralized governance and security. As a result, the integration of blockchain and edge computing within a cohesive framework offers substantial benefits, such as dependable network access and governance, decentralized storage, and computing at the peripheries. this integration effectively mitigates many security problems and guarantees data integrity in edge computing contexts. this article offers a comprehensive analysis of blockchain technology, its attributes, and its relevance in the context of 6G. Additionally, it investigates the amalgamation of blockchain and edge computing, analyzes diverse application cases, and addresses the related issues. Finally, the report delineates contemporary research trajectories and draws conclusions based on the findings. © 2024 ieee.

关键词： Mobile edge computing

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A Proposed Intensity Modulated Optical Fiber Based sensor for Partial Discharge Detection in High Voltage Power Transformer 6

A Proposed Intensity Modulated Optical Fiber Based Sensor fo...

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ieee 6th international conference on Condition Assessment Techniques in Electrical systems (ieee CATCON)

作者： Adhikari, Krishanlal Koley, Chiranjib Roy, Nirmal Kumar NIT Durgapur Elect Engn Durgapur India

ISBN: (纸本)9781665473804

Power transformer is recognized as the heart of the power system. Insulation failure is the prime cause behind the failure of transformers. Partial Discharge (PD) is considered as the best indicator of insulation deterioration. So PD detection successively helps to prevent impending failures in capital equipment like transformers. In this paper, an intensity-modulated, optical fiber-based, invasive PD sensor is presented. the proposed sensor is designed to detect the high-frequency acoustic waves generated due to PD activity inside the transformers. An elastic fixed-fixed beam structure with predefined dimensions is designed as the primary sensing element. the structure is made of suitably chosen material having high dielectric strength. the optical fiber sensor is pasted on the primary sensing element using epoxy adhesive. the intensity-modulated optical fiber sensor consists of a fiber-to-fiber coupling having a high coupling coefficient. the designed sensor assembly modulates the intensity of the light when the primary sensing element vibrates due to PD-generated acoustic waves. therefore, such an arrangement can be a cost-effective alternative to the high-cost fiber Bragg grating analyzers(FBGA) or tunable laser-based conventional acoustic PD detection methods. the fundamental design equations and the simulated results obtained using Ansys engineering simulation software are presented in this paper. From the study, it is profoundly observed that the proposed sensor is a progressive and efficient alternative in the field of acoustic PD detection of power transformers.

关键词： power transformer intensity modulation fiber-to-fiber coupling partial discharge acoustic sensor

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A Layer-Based Sparsification Method For distributed DNN Training 24

A Layer-Based Sparsification Method For Distributed DNN Trai...

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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： Hu, Yanqing Ye, Qing Zhang, Zhongyu Lv, Jiancheng Sichuan University Chengdu China

ISBN: (纸本)9798350319934

With the increasing size of Deep Neural Networks (DNNs) and datasets, DNN training will consume a lot of time. Various distributed strategies have been utilized to speed up the training process. Nevertheless, the frequent communication between different computational nodes dramatically limits the scale of the cluster. Currently, many gradient compression methods (e.g., Sparsification, Quantization, Low-Rank) have been proposed to optimize the transmission process. Since each layer needs to be processed and compressed respectively before transmission, a rash of time-consuming tensor operations is demanded. To alleviate this issue, we propose a layer-based sparsification method, which stems from the observation that the learning efficiency and convergence speed of different layers of DNN is different during the training process. Instead of compressing all gradients in an iteration, some layers are preferentially selected using a well-designed sliding window, which largely avoids unnecessary tensor operations. the sliding window is adjusted according to the dynamic characteristics of the parameter during the DNN training. Extensive experiments on one Text task and two Image Classification tasks are conducted to validate the efficiency of the proposed method in the same environment configuration, where the gradient compression adopts the layer-based sparsification and the other three comparative state-of-the-art methods. the total training time, communication time, and model accuracy are collected and analyzed. Sufficient experimental results demonstrate that both training and communication time can be greatly reduced, which is especially up to 60% in some cases. Additionally, despite slightly decreased within 1% model quality, the results indicate that the improvement in throughput can be as high as 50%. © 2022 ieee.

关键词： Deep neural networks

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Gray Wolf Optimization Algorithm to Enhance Data Aggregation in Wireless sensor Network 5

Gray Wolf Optimization Algorithm to Enhance Data Aggregation...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Loganathan, Sathyapriya Victoria Jancee, B. Senkamalavalli, R. Subbu Lakshmi, T.C. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India East Point College of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Francis Xavier Engineering College Department of Information Technology Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350321425

Wireless sensor networks (WSNs) have become increasingly popular as distributed systems using small, lightweight nodes with limited resources for monitoring environmental conditions or measuring physical parameters. To minimize energy utilization, data aggregation can be employed to reduce the data quantity forward to the base station (BS). To solve these issues, a Gray wolf optimization algorithm (GWOA) is proposed to enhance data aggregation in WSN. Increasing the network lifetime, node reliability, and throughput with the selection of best cluster heads (CHs) utilizing the Gray Wolf Optimization (GWO) algorithm is the major objective of this research. this approach selects the CH by applying the GWO algorithm established on the node remaining energy, available bandwidth, and distance. the GWO-based clustering protocol minimizes dead nodes and energy utilization and raises the clustering rounds and throughput. the simulation results demonstrate that the increases the node reliability and network throughput. © 2023 ieee.

关键词： Energy efficiency

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Benefits of Using distributed Ledger Technology in Managing Assets of Firm 6

Benefits of Using Distributed Ledger Technology in Managing ...

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

作者： Khan, Kahkashan Kumar, Bhupendra Singh, Rekha Sood, Shagun Kandasamy, Umamaheswari Siddharth University MBA Department U.P Siddharthnagar India College of Business & Economics Debre Tabor University Debre Tabor Ethiopia Thakur Institute of Management Studies and Research Mumbai India Mittal School of Business Lovely Professional University Jalandhar India School of Management KPR College of Arts Science and Research Coimbatore India

ISBN: (纸本)9798350304480

this research paper has explained the scenario of understanding the DLT process where it has accumulated different knowledge and ideas. Objectives have been prepared on getting the entire system verified along with nurturing the factors thoroughly. However, it has again incorporated its aspects of understanding sustainability along with making DLT as an efficient way of data storage. It has again explained the scenario of accelerating the ways to meet the research requirements and the process of developing cloud storage. Issues have been explained along with understanding the problems and issues with DLT and the proper recommended strategies as well. © 2023 ieee.

关键词： block chain data assimilation data sort data storage DLT

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Resource-Efficient DNN Training and Inference for Heterogeneous Edge Intelligence in 6G 23

Resource-Efficient DNN Training and Inference for Heterogene...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Cui, Enfang Zhang, Weiting Yang, Dong Wu, Wen Lyu, Feng School of Electronic and Information Engineering Beijing Jiaotong University Beijing China University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665494571

Edge intelligence is expected to be a key enabler of the future sixth generation (6G) mobile network. However, the heterogeneous characteristics of edge intelligence, such as heterogeneous edge data, resources, and service requirements, pose challenges to deep neural network (DNN) training and inference at the edge. To fully tap the potential of DNN for 6G heterogeneous edge intelligence, a variety of solutions have been proposed to tackle the above challenges, such as federated learning and transfer learning, for DNN training;and DNN partitioning and early exiting, for DNN inference. In this paper, we provide a comprehensive survey about resource-efficient DNN training and inference in heterogeneous edge intelligence. We first discuss the challenges of DNN training and inference in heterogeneous edge intelligence. then, we give a detailed review of the recent advances of DNN training and inference technologies in heterogeneous edge intelligence, including the principles and state-of-the-art solutions of these technologies. We further conduct a taxonomy and summary of the reviewed solutions to clarify their applicable scenarios and the limitations. Finally, we point out some potential future research opportunities on heterogeneous edge intelligence. © 2021 ieee.

关键词： Training 6G mobile communication Deep learning Smart cities Taxonomy Transfer learning Neural networks

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Multi-Modal Microfluidic Bend sensor for Soft and Robotic Sensing Applications∗ 6

Multi-Modal Microfluidic Bend Sensor for Soft and Robotic Se...

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6th international conference on Manipulation, Automation, and Robotics at Small Scales, MARSS 2023

作者： Othman, Wael Qasaimeh, Mohammad A. Mechanical and Biomedical Engineering New York University Abu Dhabi United Arab Emirates Mechanical and Aerospace Engineering Tandon School of Engineering New York University United States

ISBN: (纸本)9798350330397

Bend sensors play a pivotal role in gaining insights into the dynamic behavior of flexible mechanical systems and bolstering safety considerations. this paper introduces a novel microfluidic-based multi-modal bend sensor that offers tremendous potential for a wide range of applications, especially in robotics and microsystems. Our bend sensor features a flexible substrate embedded with a liquid metal-filled microchannel filled. the fabrication process utilizes simple and cost-effective 3D printing and soft-lithography techniques. As the microchannel deforms, the sensor measures changes in electrical resistance across its terminals, providing reliable indications of bending intensity and direction. By incorporating rectangular through-voids in close proximity underneath the serpentine microchannel, the bending-induced change in the length and cross-sectional area of the microchannel is magnified. this resulted in an enhanced bending sensitivity. Experimental characterizations demonstrate the sensor's effectiveness and versatility under various bending conditions. Furthermore, the sensor is capable of detecting the amount of applied pressure as well as the degree of twisting, expanding its functionality. With its soft and flexible nature, the novel multi-modal bend sensor can be seamlessly integrated into wearable technology and soft robotics applications, offering real-time insights into bending, pressure, and twisting for improved safety and control. © 2023 ieee.

关键词： Microchannels

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CONTINUER: Maintaining distributed DNN Services During Edge Failures 6

CONTINUER: Maintaining Distributed DNN Services During Edge ...

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6th ieee international conference on Edge computing and Communications (ieee EDGE)

作者： Majeed, Ayesha Abdul Kilpatrick, Peter Spence, Ivor Varghese, Blesson Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Univ St Andrews Sch Comp Sci St Andrews Fife Scotland

ISBN: (数字)9781665481403

ISBN: (纸本)9781665481403

Partitioning and deploying Deep Neural Networks (DNNs) across edge nodes may be used to meet performance objectives of applications. However, the failure of a single node may result in cascading failures that will adversely impact the delivery of the service and will result in failure to meet specific objectives. the impact of these failures needs to be minimised at runtime. three techniques are explored in this paper, namely repartitioning, early-exit and skip-connection. When an edge node fails, the repartitioning technique will repartition and redeploy the DNN thus avoiding the failed nodes. the earlyexit technique makes provision for a request to exit (early) before the failed node. the skip connection technique dynamically routes the request by skipping the failed nodes. this paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives (accuracy, latency and downtime thresholds) when an edge node fails. To this end, CONTINUER is developed. Two key activities of the framework are estimating the accuracy and latency when using the techniques for distributed DNNs and selecting the best technique. It is demonstrated on a lab-based experimental testbed that CONTINUER estimates accuracy and latency when using the techniques with no more than an average error of 0.28% and 13.06%, respectively, and selects the suitable technique with a low overhead of no more than 16.82 milliseconds and an accuracy of up to 99.86%.

关键词： distributed DNNs Edge computing Failures

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