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ISFD: Efficient and Fault-tolerant In-System-Failure-Detection for LP FPGA-based Smart-sensors in Space Expeditions

ISFD: Efficient and Fault-tolerant In-System-Failure-Detecti...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： Kazi Mohammad Abidur Rahman Timo Dirkes Bjoern Delfs Vanessa Wyrwoll Ulf Kulau Smart Sensors Group Hamburg University of Technology Hamburg Germany Department of Engineering DSI Aerospace GmbH Bremen Germany Department for Medical Radiation Physics Carl Von Ossietzky University Oldenburg Oldenburg Germany

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

The health of astronauts is a top priority in manned space missions, and space conditions can have a major impact on the physiology of the human body. This is why extensive health monitoring is essential, especially for future missions beyond low Earth orbit (LEO), where astronauts will be increasingly exposed to higher radiation doses. To address these challenges, the AuRelia project has developed space-capable smart sensors that are designed to be failure-tolerant and autonomous. These sensors employ an innovative In-System-Failure-Detection (ISFD) technique that caters to the increasing need for Single Event Effect (SEE)-induced failure tolerance in resource-constrained and ultra-low power SRAM-based FPGAs used in miniaturized smart sensors. The ISFD technique integrates a hardware-based radiation-tolerant Test Core (using only 77 LUTs) with a software-based Test Controller, resulting in a lightweight yet efficient solution for failure detection. What’s more, the adaptability of ISFD facilitates seamless integration into existing firmware and software, offering developers a swift implementation process. ISFD complements traditional blind scrubbing methods, enhancing sensor data reliability, and has been validated with radiation up to 70Krad.

关键词： Space vehicles Space missions Low power electronics sensor systems Software Table lookup Intelligent sensors

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A fog and blockchain-based distributed Virtual Private Networks (VPN)

A fog and blockchain-based distributed Virtual Private Netwo...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： Rahma Trabelsi Ghofrane Fersi Mohamed Jmaiel ReDCAD Laboratory National School of Engineers of Sfax Sfax Tunisia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

The Internet of Things (IoT) is a breakthrough technology that interconnects and empowers numerous smart devices allowing them to communicate, collect, and exchange data. The most challenging issue in IoT networks is securing exchanged data. Virtual Private Networks (VPNs) have a significant impact on ensuring security within IoT systems. So implementing a VPN-based solution can bring multiple security values to IoT systems, especially for exchanged data. However, most VPN-based solutions rely on a single central server responsible for managing all VPN connections. This presents a major problem when the number of managed VPNs increases because it will lead to the performance deterioration of the VPN server and can even lead to a single point of failure problem. We propose in this paper a distributed VPN-based solution that relies on distributed fog nodes playing the role of VPN servers. To determine the VPN server responsible for a specific communication, a fully decentralized and efficient search is guaranteed within the fog nodes through the utilization of the DHT-based Chord protocol. To enforce security and immutability, blockchain is used to verify the different criteria of a new requester to join a specific VPN. The performance evaluations have shown that our solution is efficient in terms of cost, time, and complexity.

关键词： Performance evaluation Protocols Costs Virtual private networks Complexity theory Servers Security

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Exploring Unsupervised One-Class Classifiers for Lightweight Intrusion Detection in IoT systems

Exploring Unsupervised One-Class Classifiers for Lightweight...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： Shahrzad Golestani Dwight Makaroff Department of Computer Science University of Saskatchewan Saskatoon Canada

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

The Internet of Things (IoT) has revolutionized numerous domains, but security and privacy remain significant concerns. Massive amounts of IoT data poses challenges for a centralized IDS. distributed solutions, particularly using Machine Learning (ML) and Deep Learning (DL) methods, demand substantial data and processing power, thus impractical for resource-constrained IoT *** study employs three ML, one DL, and five One-Class Classification (OCC) methods for anomaly detection across five IoT datasets. While supervised ML and DL methods demonstrate superior performance, only some OCC methods exhibit comparable performance. All ML and DL methods show consistent performance on all different datasets, but this seamless consistency is not observed among OCCs. DT stands out as the most accurate algorithm among all, and DROCC and LOF stand out as the most accurate and consistent OCCs. In terms of execution time, DT is the fastest among all methods, and DROCC and LOF are the second and third fastest among OCCs. Conversely, among the most time-consuming algorithms, OCSVM is the most time-intensive, followed by DeepSVDD and DNN.

关键词： Performance evaluation Deep learning Privacy Accuracy Intrusion detection distributed databases Classification algorithms

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PipePrune: Pipeline Parallel Based on Convolutional Layer Pruning for distributed Deep Learning 23

PipePrune: Pipeline Parallel Based on Convolutional Layer Pr...

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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： Tan, Daojun Jiang, Wenbin Qin, Shang Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Natl. Eng. Res. Ctr. for Big Data Technol. and Syst. Serv. Comp. Technol. and Syst. Lab Cluster and Grid Comp. Lab Wuhan430074 China

ISBN: (纸本)9781665494571

Benefitting from the combination of the idea of pipeline with model parallelism and data parallelism, pipeline parallelism improves the efficiency of distributed deep learning systems significantly. However, suffering from the bubbles and gaps caused by the imbalance of different stages in pipeline, it can not output ideal performance yet. To explore the potential of pipeline parallelism further, we propose a novel approach called PipePrune, which adds a convolutional layer pruning strategy to pipeline creatively to reduce the bubbles and gaps. For the convolutional layers with heavy overheads, some unimportant kernels are pruned by the measurement of the L1-norm. This approach makes the processing overheads of different pipeline stages more balanced. The experimental results show that, compared with state-of-the-art pipeline methods, PipePrune can improve the training speeds obviously (e.g. for ResNet50 on ImageNet, about 30% speed improvement is realized with only 1.1% loss of training accuracies). © 2021 ieee.

关键词： Training Deep learning Smart cities Pipelines Neural networks distributed databases Parallel processing

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An Efficient Approach for Merging Multidimensional Blockchains in Mobile IoT

An Efficient Approach for Merging Multidimensional Blockchai...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： Hussein Zangoti Niki Pissinou Charles A Kamhoua School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia US Army Research Laboratory Network Security Branch Adelphi MD USA

Graph-based or multidimensional blockchains have been proposed to improve the scalability and efficiency of existing blockchain applications. However, when implemented in mobile Internet of Things (mIoT) networks, these blockchain systems can frequently split and merge and cause the merging algorithm to process a large number of similar blocks (a block is similar or identical when it exists once in multiple blockchains). The presence of similar blocks hinders the merging process, as it consumes time and computational resources to scan, validate, and potentially merge similar blocks. This paper presents an efficient approach for merging graph-based or multidimensional blockchains in mIoT networks by avoiding similar blocks and effectively detecting and merging new blocks that were created after the split. Our proposed merging algorithm employs depth-first search and Merkle tree techniques to minimize the time and computational resources spent on identical blocks. Finally, we evaluate the performance of our method in highly mobile networks and demonstrate that it can execute the merge with a more than 72% reduction in time in comparison to merging algorithms without block similarity handling.

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Development and Comparative Study of Advanced distributed Electronic Control System for Robotic Applications

Development and Comparative Study of Advanced Distributed El...

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2023 international conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Nair, Shashank Babu Patil, Sushma Mathurkar, Piyush Chippalkatti, Srinivas S. Vishwakarma Institute of Information Technology Electronics and Telecommunication Engineering Maharashtra Pune India Sharnbasva University Electrical and Electronics Engg Kalaburagi India Vishwakarma University Pune Engineering Sciences Maharashtra Pune India

ISBN: (纸本)9798350315455

This paper aims to thoroughly analyze and compare a project report on an advanced electronic control system for robotics with two related scholarly works. The primary focus is on identifying the commonalities and divergences, as well as the strengths and weaknesses of these methods. The paper being reviewed proposes a novel architecture for a control system that employs distributed electronics to enhance robot performance and functionality, detailing the design, implementation, and real-world application effectiveness. It covers key aspects like an electronic board designed for stepper motor control in a closed-loop system, communication protocols, sensor integration, and distributed computing. The review delves into the comparative analysis of control system architectures, communication methods, and implementation techniques across the three papers, shedding light on their potential impact in the field of robotics, and suggests areas for future research and improvements. © 2023 ieee.

关键词： distributed architecture Electronic control system Robotic applications

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Analysis of a sensor Switching Approach for State Estimation with Applications to Electrochemical Power Converters and Energy Storage systems 11

Analysis of a Sensor Switching Approach for State Estimation...

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11th ieee international conference on systems and Control, ICSC 2023

作者： Rauh, Andreas Lahme, Marit Carl von Ossietzky Universität Oldenburg Group: Distributed Control in Interconnected Systems Department of Computing Science OldenburgD-26111 Germany

ISBN: (纸本)9798350304886

Equivalent circuit models for fuel cell stacks as well as for the series connection of battery cells are characterized by multiple series-connected RC sub-networks. In practical applications, for example, when aiming at the aging detection and monitoring of the individual cells in a fuel cell stack, it is desired to estimate the individual state variables of each of these series-connected cells. The same holds true for battery management systems that are dedicated to the state of charge (resp., voltage) equalization in a series connection of multiple battery cells. However, the pure knowledge of the current through this series connection as well as the sum over all terminal voltages is insufficient to make the overall system model fully observable. To efficiently observe the state variables in each of the series-connected subsystems, we aim at avoiding the measurement of the voltages of each individual fuel (resp., battery) cell. Instead, this paper presents a systematic sensor switching strategy which allows for estimating the individual state variables of all series-connected subsystems in an Unscented Kalman Filter framework. Simulation results are presented to compare the achievable estimation accuracy with a scenario in which all cell (resp., terminal) voltages were measured simultaneously. © 2023 ieee.

关键词： Battery management systems

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Research of Seismic Streaming Storage Based on Druid 23

Research of Seismic Streaming Storage Based on Druid

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作者： Gao, Bing Chen, Jie Zou, Qijie Qin, Jing School of Information Engineering Dalian University Dalian Key Laboratory of Smart Medical and Health Dalian China School of Software Engineerin Dalian University Dalian Key Laboratory of Smart Medical and Health Dalian China

ISBN: (纸本)9781665494571

Along with the growth of seismic observation networks, the amount of seismic observation data is also increasing rapidly with the form of fast arrival stream, and it is an important and challenging task to store seismic data in time. However, two bottlenecks (slow read/write speed, poor user concurrency) still make the practical applications difficult. We proposes an Apache Druid-based storage solution for seismic observation data streams. The solution is more adaptable to the read/write requirement of large-scale seismic data streams. Specifically, we achieve real-time observation for two aspects: (I) using Kafka as a distributed message exchange system;and (II) implementing accesses and exchange with massive seismic stream data onto the Kafka indexing service provided by Druid. The experimental results show that the Druid-based temporal data storage solution has better stream input and real-time query performance compared with the existed Hbase storage method. © 2021 ieee.

关键词： Concurrent computing Smart cities Time series analysis Memory distributed databases Real-time systems Task analysis

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distributed Artificial Intelligence and Adaptive Fish Swarm Optimization based Resource Allocation in Wireless sensor Network

Distributed Artificial Intelligence and Adaptive Fish Swarm ...

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2023 international conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Reddy, Y. Geetha Priyanka, K. Kumar, K. Sudheer Habelalmateen, Mohammed I. Hussein, A.H.A. Malla Reddy Engineering College for Women Hyderabad India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India SR University School of Computer Science & Artificial Intelligence Warangal India The Islamic University Najaf Iraq Ahl Al Bayt University College of Pharmacy Karbala Iraq

ISBN: (纸本)9798350315455

The Wireless sensor Network (WSN) is a decentralized and distributed ad-hoc network that comprises the powerful computing, sensing and processing nodes. In WSN, the sensor nodes are limited in terms of computational latency, communication range, bandwidth, storage and battery power. The efficient utilization of WSN resource is a difficult task for improve network lifetime, throughput, minimize the computational delay and overhead. Various intelligent strategies are developed through adopting intelligent resource management approaches. Due to the sensor heterogeneity, the inter and intra-cluster cooperative communication among sensor node in terms of response time and energy consumption. In this paper, a distributed Artificial Intel ligence (DAI) with a hierarchical resource allocation for address the resource allocation issues. For inter-cluster power allocation, this paper considers the Quality of Service (QoS) and energy consumption with DAI. Additionally, for intra-cluster resource allocation, the Adaptive Fish Swarm Optimization (AFSO) is utilized which utilizes its objective functions as node distance and corresponding energy loads. The proposed DAI and AFSO less energy consumption of 7.86%, packet delivery ratio of 99.4% which is better than other existing methods. © 2023 ieee.

关键词： Adaptive Fish Swarm Optimization distributed Artificial Intelligence Energy Consumption Quality of Service Resource Allocation Wireless sensor Network

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PSeer: Performance Prediction for Partially Co-located distributed Deep Learning 23

PSeer: Performance Prediction for Partially Co-located Distr...

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作者： Ding, Weijia Ding, Ziwei Zhao, Laiping Qiu, Tie College of Intelligence and Computing Tianjin University Tianjin Key Laboratory of Advanced Networking China

ISBN: (纸本)9781665494571

This paper studies the problem of predicting the deep learning (DL) jobs' training time which is the fundamental guide for training resources allocating and job scheduling. The existing prediction approaches, however, are greatly challenged for distributed deep learning (DDL) jobs due to the partial interference, namely, the resource contention that only occurs at partial time or locations of a job. We observe that the maximum difference in training time under different combinations of partial interference DDL can reach 2.2×. Thus partial interference is not a negligible factor for predicting the training time of DDL jobs. In this paper, we present PSeer, a partial interference-aware training time predictor for DDL workloads. It adopts a combined dual-submodel (the step number submodel and the step speed submodel), which is highly accurate in predicting by encoding the spatially and temporally varied partial interference into the model design. It is also lightweight: the step number submodel only requires the initial training loss value, and the step speed submodel can be constructed using the CPU and GPU resource utilization information of a few pre-trained steps. Empirical evaluation shows the prediction error generated by PSeer could achieve as low as 3.93% when employing RFR in the training speed submodel, and the prediction overhead is © 2021 ieee.

关键词： Training Deep learning Graphics processing units Interference Predictive models Encoding Resource management

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