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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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Travel Path Planning for UAV as a Data Collector for a Sparse WSN 17

Travel Path Planning for UAV as a Data Collector for a Spars...

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17th Annual ieee international conference on distributed computing in sensor systems (ieee DCOSS)

作者： Karegar, Pejman A. Al-Anbuky, Adnan Auckland Univ Technol Elect & Elect Engn Auckland New Zealand

ISBN: (纸本)9781665439299

Recent advances in Unmanned Aerial Vehicle (UAV) technologies make their use as data collection sinks feasible for practical applications of Wireless sensor Networks (WSN). Using UAVs as mobile data collection agents can improve network performance by reducing the horizontal radio pollution in animal sanctuaries or conservations, for example. This may also positively improve energy consumption of both UAV and sensor nodes (SN) on the ground. Designing an efficient UAV trajectory which has the capability of data gathering from randomly distributed large number of SNs has been one of the challenges for the UAV-aided WSN data gathering solutions. This paper aims at conceptualizing a solution for UAV path planning in gathering data of distributed wireless sensors over a large space. Both efficient flight path and dynamic orchestration of sensor grouping are considered as two key factors for improving the flight performance in data collection. Herein, a classical Travel Salesman Problem (TSP) solution supported by the ground group offerings are used for manipulating a simplified centroid-based trajectory and turning it into a more relaxed fuzzy route. This in turn would offer a ground for smoother and more efficient flight paths. While preliminary modelling of the approach offers encouraging results, the demand for fluidity in dynamically re-orchestrating the ground sensor networks must be carefully considered. Successful implementation of this concept would support a wide range of applications such as the farming and conservation used cases.

关键词： Unmanned Aerial Vehicle (UAV) Wireless sensor Networks (WSN) Fuzzy route

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ARIES: Accelerating distributed Training in Chiplet-based systems via Flexible Interconnects 42

ARIES: Accelerating Distributed Training in Chiplet-based Sy...

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42nd ieee/ACM international conference on Computer-Aided Design (ICCAD)

作者： Yin, Lingxiang Ghazizadeh, Amir Louri, Ahmed Zheng, Hao Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA George Washington Univ Dept Elect & Comp Engn Washington DC USA

ISBN: (纸本)9798350322255

Large-scale deep learning models are widely deployed in many application domains with remarkable performance improvements. However, training these models with immense parameters calls for unprecedented computing and communication capabilities. Recently, chiplet-based architectures have shown much promise in scaling Deep Neural Network (DNN) inference, but their applications in the training phase remain unexplored and challenging. In this paper, we posit, beyond scaling computing capability, chiplet-based architectures could also be leveraged to enable new optimization opportunities for existing parallel training algorithms (e.g., Ring and Tree-based all-reduce). Specifically, we aim to explore a variety of topological characteristics, along with the interposer technology, to sustain the performance scaling of parallel training in chiplet-based systems. We propose ARIES, a versatile chiplet-based communication architecture supporting various parallel training algorithms using a flexible interconnect design. The proposed design can adapt to various collective operations such as reduce and gather across a wide diversity of training algorithms. Moreover, such flexibility is also leveraged to further enhance existing all-reduce algorithms depending on the latency and bandwidth requirements of the DNN model and dataset size. Simulation results show that the proposed ARIES can achieve up to 3.92x speedup in execution time and 38.8% reduction in Network-on-Chip (NoC) energy consumption when compared to prior work.

关键词： DNNs parallel training chiplets collective operations

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Divide and Conquer for Combinatorial Optimization and distributed Quantum Computation 4

Divide and Conquer for Combinatorial Optimization and Distri...

引用

4th ieee international conference on Quantum computing and Engineering, QCE 2023

作者： Tomesh, Teague Saleem, Zain H. Perlin, Michael A. Gokhale, Pranav Suchara, Martin Martonosi, Margaret Infleqtion ChicagoIL United States Princeton University Department of Computer Science PrincetonNJ United States Argonne National Laboratory Mathematics and Computer Science Division LemontIL United States Amazon Amazon Web Services SeattleWA United States

ISBN: (纸本)9798350343236

Scaling the size of monolithic quantum computer systems is a difficult task. As the number of qubits within a device increases, a number of factors contribute to decreases in yield and performance. To meet this challenge, distributed architectures composed of many networked quantum computers have been proposed as a viable path to scalability. Such systems will need algorithms and compilers that are tailored to their distributed architectures. In this work we introduce the Quantum Divide and Conquer Algorithm (QDCA), a hybrid variational approach to mapping large combinatorial optimization problems onto distributed quantum architectures. This is achieved through the combined use of graph partitioning and quantum circuit cutting. The QDCA, an example of application-compiler co-design, alters the structure of the variational ansatz to tame the exponential compilation overhead incurred by quantum circuit cutting. The result of this cross-layer co-design is a highly flexible algorithm which can be tuned to the amount of classical or quantum computational resources that are available, and can be applied to both near- and long-term distributed quantum ar-chitectures. We simulate the QDCA on instances of the Maximum Independent Set problem and find that it is able to outperform similar classical algorithms. We also evaluate an 8-qubit QDCA ansatz on a superconducting quantum computer and show that circuit cutting can help to mitigate the effects of noise. Our work demonstrates how many small-scale quantum computers canwork together to solve problems 85 % larger than their own qubit count, motivating the development and potential of large-scale distributed quantum computing. © 2023 ieee.

关键词： Quantum computers

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Enhanced Slicing based Deep Learning architecture for 5G networks in distributed Learning 15

Enhanced Slicing based Deep Learning architecture for 5G net...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dadhich, Atul Shankar, Amar Kumari, Aarsi Department of Electrical Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science & IT ARKA JAIN University Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper gives a distributed deep getting-to-know (DDL) architecture for 5G community cutting. DDL is an allotted method which has been a success in schooling massive-scale deep mastering fashions. It applies the ideas of disbursed computing to reduce the dimensions of the statistics being trained at the same time as education samples in parallel on different computing assets. The proposed solution in this paper exploits the improvements in 5G networks' virtualization skills, which include community cutting, to provide a green architecture which allows DDL fashions to be taught with dispensed resources to limit the training time. The proposed structure consists of three predominant modules: a useful resource-cutting manager, a dispensed training cluster, and a dispensed inference cluster. The resource-reducing manager allows dynamic aid cutting throughout numerous assets (e.g. GPU, CPU). The distributed schooling cluster includes workers distributing the training duties across the sources. The allotted inference cluster is used to perform inference on the trained fashions throughout more than one allotted computing source. The advantages of the proposed structure are that it allows green disbursed schooling, green disbursed inference, and value-green education of complex deep gaining knowledge of models. As such, it may be used to educate huge and complex models with decreased prices and also boost the schooling throughput. Additionally, 5G community cutting presents flexibility in useful resource provision. ©2024 ieee.

关键词： 5G mobile communication systems

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Challenges and Solutions for distributed File System 2

Challenges and Solutions for Distributed File System

引用

2nd international conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Richa Gupta, Amit Bhatla, Shruti Tripathi, Vikas Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350371055

A distributed File System (DFS) is a file system that allows multiple users to access and share files and data across a network. In a DFS, files are stored on multiple servers, and users can access them from any location on the network as if they were stored on their local computer. DFS provides a way to organize and manage files and folders across multiple servers in a unified way, making it easier for users to access and share data without worrying about the physical location of the files. This can help to improve data availability, reliability, and scalability in a networked environment. DFS also provides a way to replicate data across multiple servers for redundancy and fault tolerance, so that if one server fails, the data can still be accessed from other servers. Some examples of popular distributed file systems include Hadoop distributed File System (HDFS), Google File System (GFS), and Windows distributed File system (DFS). LCCFS (light weight cloud computing file system) which help to reduce the overhead and increase the performance of the distributed system RADOS (Reliable autonomic distributed object storage) which utilized for implementing distributed file system for cloud computing Pangea is a new system which allow different units to combine into a single unit and PLSF (Virtual Parallel log structured file system) which pick up the performance by improving the bandwidth are some new terminologies introduced by some previous research are discussed in this research. © 2024 ieee.

关键词： Ceph Cloud file system distributed File System Gluster HDFS

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CDQKL: Consensus-Based distributed Quantum Kernel Learning 1

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Ma, Wenxuan Liu, Mengxiang Deng, Ruilong State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350366778

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL. © 2024 ieee.

关键词： Quantum computers

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Research on Anti-Burst Technology of Urban Underground Cable Based on distributed Vibration Fiber Sensing and Digital Twin 6

Research on Anti-Burst Technology of Urban Underground Cable...

引用

2023 ieee 6th international conference on Information systems and Computer Aided Education, ICISCAE 2023

作者： Li, Kaipeng Chen, Zhou Wang, Yunge Wu, Xingxiao Wang, Tao Xiangshan Electric Power Industry Co. Ltd Ningbo China State Grid Zhejiang Electric Power Co. Ltd. Xiangshan County Power Supply Company Ningbo China

ISBN: (纸本)9798350313444

This project plans to develop a distributed fiber optic vibration sensor and digital twin technology based on the external rupture detection technology of optical cable. In this method, a high voltage pulse is applied to the cable to cause partial discharge in the defective part of the cable and then generate the vibration signal of the cable. In this way, the real-time monitoring and early warning of external fracture damage in the common trench structure can be realized. The distributed fiber optic vibration sensor is used to detect the current along the cable direction to locate the damaged location. The proposed method is used to detect the fault condition on the line. The test proves that the device can monitor the fault situation of multiple optical cable lines in real time, and determine the fault point. © 2023 ieee.

关键词： cable digital twins external broken fault monitoring system Vibration fiber sensing

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2023 19th international conference on Wireless and Mobile computing, Networking and Communications, WiMob 2023

2023 19th International Conference on Wireless and Mobile Co...

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19th ieee international conference on Wireless and Mobile computing, Networking and Communications, WiMob 2023

ISBN: (纸本)9798350336672

The proceedings contain 81 papers. The topics discussed include: outage performance of IRS-assisted underwater optical wireless communication systems over combined channel model;sensor-based wastewater monitoring framework to detect COVID-19;deep learning assisted channel estimation for cell-free distributed MIMO networks;multi-agent Q-learning for drone base stations;RF fingerprint-based identity verification in the presence of an sei mimicking adversary;analyzing the feasibility of privacy-respecting automated tracking of devices fleeing a burglary;security implications of a satellite communication device on wireless networks using Pentesting;deep learning assisted quality ranking for list decoding of videos subject to transmission errors;robotaxis as computing clusters: a stochastic modeling approach;chained estimation for data reduction-driven routing in wireless sensor networks;lightweight group pre-handover authentication scheme for aviation 5g air-to-ground networks;learning to detect wireless spectrum occupancy using clustering approaches;smartphone camera as a diagnostic tool for detection of pulse rate and blood oxygen concentration;density-aware reinforcement learning to optimize energy efficiency in UAV-assisted networks;area under time series transformation for home appliance classification;and a novel single grant-based uplink scheme for high throughput and reliable low latency communication.

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A Learning Based Technique for sensor Fault Detection and Fault Tolerant Control

A Learning Based Technique for Sensor Fault Detection and Fa...

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2024 MIT Art, Design and Technology School of computing international conference, MITADTSoCiCon 2024

作者： Patil, Maheshkumar S. Malwatkar, Gajanan M. Government College of Engineering Department of Instrumentation Engineering Maharashtra Jalgaon425002 India

ISBN: (纸本)9798350362879

In this paper, a learning-based methodology is introduced to detect sensor fault and FTC in a level system. The methodology includes reinforcement learning skills to attain optimal performance and the computational time is also as compatible with the systems tracking. The method is deliberately designed to offer thoughtful yet workable solutions that support sustainability, safety, and dependability in presented attempt to strike a balance between system performance and system complexity. By providing a thorough structure that maximizes performance while lowering complexity and costs, the work provides intelligent ways of industrial solutions. A novel proportional integral(PI) controller augmented by reinforcement learning, especially for accurate fault detection (FD) as well as (FTC) in level system. The results are demonstrate the superiority of the presented controller over conventional methods of control system design in terms of accuracy, effectiveness, and usability. In this, a model-based environment approach is used to ensure robust fault detection, resulting in a quick and precise diagnostic technique for sensor faults. A model-based FTC method is used in the case that a sensor malfunctions, enabling the system to continue operating effectively in spite of disturbances. The efficiency of presented method is vividly demonstrated by simulation results using a level system subjected to sensor failures in the MATLAB environment. The results clearly demonstrated the benefits of proposed method, which qualify it for industrial use due to its enhanced accuracy, quicker operation, affordability, and simplified simplicity. © 2024 ieee.

关键词： Reinforcement learning

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Improving the Performance of Bully Algorithm for Leader Election in distributed systems 1

Improving the Performance of Bully Algorithm for Leader Elec...

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1st international conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Alkohali, Ebrahim A. Algabri, Malek Abdullah, Monir Faculty of Computer & Information Technology Sana’a University Sana’a Yemen College of Computing and Information Technology University of Bisha Bisha Saudi Arabia

ISBN: (纸本)9798331533557

Leader election is a classical problem in distributed systems in which the selection a coordinator node to perform tasks is necessary. However, coordinator can fail and this requires performing a leader election operation to choose a new coordinator. This study focuses on the Bully algorithm which uses a lot of messages and higher network traffic which are considered the main drawback. This paper provides a simple and adaptable adaptive bully algorithm that lowers the messages passing used and is simple and flexible. The algorithm utilizes only one node, called responsibility for the election, and a time span, where a time span is set for each node. These two procedures minimize leader election operations especially discovering failures from multiple processes in the system and messages passing. According to the results, the enhanced algorithm is better than the algorithm bully. It reduces the complexity of message passing during leader election operations. © 2024 ieee.

关键词： Message passing

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