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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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End-to-end Delay Analysis for Coded computing with Multi-Task Arrival 14

End-to-end Delay Analysis for Coded Computing with Multi-Tas...

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14th ieee international conference on Wireless Communications and Signal Processing (WCSP)

作者： Ji, Zhongming Chen, Li Chen, Xiaohui Wei, Guo Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Peoples R China

ISBN: (纸本)9781665450850

Coded computing has received significant attention thanks to its advantage in alleviating the straggler effect in distributed computing systems. For the scenario where multiple tasks randomly arrive, the additional task queueing makes the delay analysis of coded computing more challenging. In this paper, we consider the impacts of task queueing and characterize the end-to-end delay for coded computing systems under the multi-task scenario. To this end, we model the end-to-end coded computing system, which involves the stages of task arrival, queueing, transmission and computation. Although the expected end-to-end delay for coded computing is intractable, we obtain closed-form expressions for its lower and upper bounds. Moreover, we show that the multi-task coded computing has a coding gain of log(n) where n denotes the number of worker nodes, even with task queues considered. Simulation results verify the accuracy of the derived delay bounds and show the effectiveness of coded computing in the multi-task scenario.

关键词： Coded computing distributed computing delay analysis edge computing queueing theory

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Wireless MAC Slot Allocation Using distributed Multi-Armed Bandit Learning and Slot Defragmentation 18

Wireless MAC Slot Allocation Using Distributed Multi-Armed B...

引用

18th ieee international Wireless Communications and Mobile computing conference (IWCMC)

作者： Dutta, Hrishikesh Bhuyan, Amit Kumar Biswas, Subir Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665467490

This paper presents a distributed framework for Medium Access Control (MAC) slot allocation in time-asynchronous wireless networks using Multi-Armed Bandits (MAB) based learning. MAC slot allocation is formulated as an MAB problem where the nodes act as independent learning agents and learn transmission policies that ensure collision free transmissions. A novel concept of Hysteretic MAB has been introduced to speed up learning convergence. In order to reduce the bandwidth overhead while maintaining a desired MAB learning speed, a novel slot defragmentation mechanism is introduced. Not relying on network time synchronization makes the proposed mechanism feasible for low-complexity and low-cost transceivers for wireless sensor and IoT networks. The proposed mechanism is tested and evaluated on both fully connected and arbitrary mesh network topologies and is shown to be scalable with network size and topological degree. It is also shown that in partially connected topologies, the mechanism learns spatial channel reuse, thus leading to better spectral usage efficiency.

关键词： Multi-Armed Bandits (MAB) Time Asynchronous Medium Access Control distributed Learning Hysteretic Learning

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distributed Fault Diagnosis System Based on Machine Learning 3

Distributed Fault Diagnosis System Based on Machine Learning

引用

3rd international conference on Intelligent computing, Communication and Convergence, ICI3C 2023

作者： Guan, Mengru Fu, Yanyou Wang, Zhaoyue Zhang, Jiaqian Sias University Henan China

ISBN: (纸本)9798350343922

This research focuses on the development of a machine learning-based distributed fault diagnosis system that aims to improve the accuracy and real-time detection of faults in large-scale distributed systems. By deeply analysing key information such as log data, performance metrics, and network traffic of distributed systems, we design adaptable machine learning algorithms to handle distributed environments of complexity and large scale. In our research, we pay special attention to real-time fault diagnosis to detect and diagnose faults in a timely manner by monitoring and analysing the data flow of distributed systems in real time, thus minimising the potential impact on the system. The proposed machine learning algorithms are not only able to handle large-scale datasets efficiently, but also have the flexibility to adapt to different system architectures and network topologies, while exhibiting good scalability and robustness. In addition, we attempt to introduce an augmented learning approach to enable the fault diagnosis system to actively learn and optimise in order to maintain an efficient fault diagnosis capability in a dynamic and distributed environment. This innovative approach is expected to further improve the system's adaptability to emerging fault modes, thereby enhancing the system's intelligence and self-optimisation capabilities. Overall, this study introduces advanced techniques in machine learning for fault diagnosis in the field of distributed systems and lays the foundation for a more accurate, real-time and intelligent fault diagnosis system. © 2023 ieee.

关键词： Adversarial machine learning

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Design of distributed Detector and Consensus State Estimator for Nonlinear systems under MITM Attacks 13

Design of Distributed Detector and Consensus State Estimator...

引用

13th ieee international conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Wen, Yanxin Tian, Xingyu Li, Li School of Electrical Engineering Yanshan University Hebei Qinhuangdao China

ISBN: (纸本)9798350315196

Aiming at man-in-the-middle (MITM) attacks in wireless sensor network (WSN) channels of nonlinear systems, a distributed secure consensus state estimator is designed, which is based on RSA encryption and Hash function verification. Firstly, transmitted packets are encrypted to ensure security prevention. Then, an attack detector based on Hash function is designed to reduce the impact of MITM attacks. Moreover, a local double gains estimator is designed. Based on this, a distributed secure consensus state estimator is designed to ensure consensus on state estimation of sensors. Finally, sufficient conditions are given to ensure that information matrix of the designed estimator is bounded, and effectiveness of the estimator is verified by a numerical simulation. © 2023 ieee.

关键词： Hash functions

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An efficient charging scheduling scheme to enhance the wireless rechargeable sensor networks' lifespan

An efficient charging scheduling scheme to enhance the wirel...

引用

2023 international conference on Intelligent systems, Advanced computing and Communication, ISACC 2023

作者： Rahaman, Sk Md Abidar Azharuddin, Md Aliah University Department of Computer Science and Engineering Kolkata700156 India

ISBN: (纸本)9798350399387

The wireless sensor networks' (WSNs') sensor nodes' (SNs') limited energy is a crucial factor to take into account since it can result in data loss or even the entire network failing. In order to address energy shortages or meet energy demands, wireless power transfer (WPT) was introduced as a means to overcome this problem. With WPT, SNs can have their battery power restored by recharging them through one or more chargers, thereby extending their lifespan. Planning a charging schedule for a mobile charger (MC) is an NP-hard issue. As part of our research, we present a priority-based charging scheduling algorithm that can be used on wireless rechargeable sensor networks (WRSNs). The suggested algorithm primarily considers the SNs' remaining energy and the timing of charging requests. To demonstrate the suggested algorithm's higher performance, we simulate it and compare the results with those of other commonly used approaches. © 2023 ieee.

关键词： sensor nodes

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Towards Context Aware Adaptive Deployment in ML Applications Using State Machines 17

Towards Context Aware Adaptive Deployment in ML Applications...

引用

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

作者： Dolui, Koustabh Michiels, Sam Lowet, Dietwig Hughes, Danny Hallez, Hans Katholieke Univ Leuven Imec Distrinet Comp Sci Leuven Belgium Philips Res Eindhoven Netherlands

ISBN: (纸本)9781665439299

This paper starts from the observation that mobile and edge devices are powerful enough to execute Machine Learning (ML) application components, which in turn creates opportunities to keep privacy-sensitive data close to its source. Composing and deploying a distributed ML application is far from trivial because the optimal configuration depends on the application's goals and execution context, both of which may change throughout its lifetime. Prior research on context-aware reconfigurations in ML based applications offer limited capabilities for dynamically migrating software components between mobile, edge and cloud devices. In this paper, we propose a context-aware middleware that enables automated optimizations of the application deployment in order to satisfy the application's functional goals while the execution context changes in terms of available computation, memory and network resources. We use finite state machines to model the reconfiguration of the application based on contextual triggers and facilitate system design through the abstraction of system states. We illustrate the benefits of our approach with an image recognition application with well-defined performance goals that is deployed in a three-tier mobile-edge-cloud architecture.

关键词： Context-aware services Image recognition Machine learning Computer architecture sensor systems Middleware distributed computing

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TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment 23

TeeSwap: Private Data Exchange using Smart Contract and Trus...

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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： Chen, Peng Shi, Peichang Xu, Jie Fu, Xiang Li, Linhui Zhong, Tao Xiang, Liangliang Kong, Jinzhu College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China Zhejiang Lab Hangzhou311100 China Kylin Software Co. Ltd Changsha410073 China

ISBN: (纸本)9781665494571

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively. © 2021 ieee.

关键词： Data privacy Smart contracts Data communication Cryptography

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Collision Aware Energy Efficient UAV-assisted Heterogeneous sensor Networks

Collision Aware Energy Efficient UAV-assisted Heterogeneous ...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Sethukarasi, T. Dhaarani, T.G. Parivazhagan, A. Madhurikkha, S. Jayakumar, T. R.M.K. Engineering College Department of CSE Chennai India Nandha Engineering College Department of ECE Erode India Kalasalingam Academy of Research and Education Department of CSE Krishnankoil India Jaya Sakthi Engineering College Department of Artificial Intelligence & Data Science India KCG College of Technology Department of Mechatronics Engineering Chennai India

ISBN: (纸本)9798350319200

Heterogeneous sensor Networks (HSN) is the most trending technology which is used in lots of intelligent communication systems so that it greatly helps to increase the quality of life of people. Due to high speed and unpredictable mobility certain drawbacks are occurred in the network that reduces the communication quality. For that purpose and Unmanned Aerial Vehicles (UAVs) are created in the network that can able to improve the device monitoring and its mobility so that it increases the sustainability of the devices. The process of UAV deployment is highly flexible but proper UAV placement is the challenging issue in the network. For that purpose in this article Collision Aware Energy Efficient UAV (CEEUH) is concentrated which can able to increase the effectiveness of UAV utilization. The experimental demonstration is conducted using NS3, and the parameters analyzed in this study include energy efficiency and packet delivery ratio. The final output is compared to the findings of existing research, and the results demonstrate that the proposed model outperforms previous techniques © 2023 ieee.

关键词： Energy efficiency

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PiP-MColl: Process-in-Process-based Multi-object MPI Collectives 25

PiP-MColl: Process-in-Process-based Multi-object MPI Collect...

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25th ieee international conference on Cluster computing (CLUSTER)

作者： Huang, Jiajun Ouyang, Kaiming Zhai, Yujia Liu, Jinyang Si, Min Raffenetti, Ken Zhou, Hui Hori, Atsushi Chen, Zizhong Guo, Yanfei Thakur, Rajeev Univ Calif Riverside Riverside CA 92521 USA NVIDIA Corp Santa Clara CA USA Meta Platforms Inc Cambridge MA USA Argonne Natl Lab Argonne IL USA Natl Inst Informat Chiyoda City Tokyo Japan

ISBN: (纸本)9798350307924

In the era of exascale computing, the adoption of a large number of CPU cores and nodes by high-performance computing (HPC) applications has made MPI collective performance increasingly crucial. As the number of cores and nodes increases, the importance of optimizing MPI collective performance becomes more evident. Current collective algorithms, including kernel-assisted inter-process data exchange techniques and data sharing based shared-memory approaches, are prone to significant performance degradation due to the overhead of system calls and page faults or the cost of extra data-copy latency. These issues can negatively impact the efficiency and scalability of HPC applications. To address these issues, we propose PiP-MColl, a Process-in-Process-based Multi-object Interprocess MPI Collective design that maximizes small message MPI collective performance at scale. We also present specific designs to boost the performance for larger messages, such that we observe a comprehensive improvement for a series of message sizes beyond small messages. PiP-MColl features efficient multiple sender and receiver collective algorithms and leverages Processin-Process shared memory techniques to eliminate unnecessary system call, page fault overhead and extra data copy, which results in improved intra- and inter-node message rate and throughput. Experimental results demonstrate that PiP-MColl significantly outperforms popular MPI libraries, including OpenMPI, MVAPICH2, and Intel MPI, by up to 4.6X for the MPI collectives MPI Scatter, MPI Allgather, and MPI Allreduce.

关键词： MPI Collective Message Passing Interface Process-in-Process Parallel Algorithms distributed systems

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sensor fault detection in photovoltaic systems using ensemble learning-based statistical monitoring chart 11

Sensor fault detection in photovoltaic systems using ensembl...

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11th international conference on Smart Grid (IcSmartGrid)

作者： Harrou, Fouzi Sun, Ying Dorbane, Abdelhakim Bouyeddou, Benamar King Abdullah Univ Sci & Technol CEMSE Div Thuwal Saudi Arabia Univ Ain Temouchent Dept Mech Engn SSL Lab Ain Temouchent Algeria Univ Saida Dr Moulay Tahar Fac Technol LESM Lab Saida Algeria

ISBN: (纸本)9798350310665

Photovoltaic systems have become increasingly popular as a source of renewable energy due to their environmental benefits and cost-effectiveness. However, sensor faults can significantly impact the performance of photovoltaic systems, resulting in reduced energy output and increased maintenance costs. This paper presents an effective approach for detecting sensor faults in photovoltaic (PV) systems using ensemble learning and the exponentially weighted moving average (EWMA) chart with nonparametric threshold estimation. The proposed approach trains the ensemble models using data collected during normal operating conditions of the PV system and detects any sensor faults by analyzing the residuals generated from the ensemble models. The EWMA chart is then applied to track changes in the residuals over time and detect any abnormalities. The flexibility of the chart is enhanced by computing the detection threshold using kernel density estimation (KDE). This approach improves the accuracy and reliability of the fault detection process. The proposed approach is assessed based on simulated data from a PV system using PVGIS. The results of the study demonstrate that the proposed method effectively detects sensor faults in photovoltaic systems, and the baggeed trees-based EWMA scheme outperforms the Boosted trees-based scheme in detecting faults in the pyranometer.

关键词： Anomaly detection Photovoltaic system sensor faults machine learning monitoring charts

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