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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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A Study on Machine Learning Based Anomaly Detection Approaches in Wireless sensor Network 10

A Study on Machine Learning Based Anomaly Detection Approach...

引用

10th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Dwivedi, Rajendra Kumar Rai, Arun Kumar Kumar, Rakesh Madan Mohan Malviya Univ Technol Dept IT&CA Gorakhpur Uttar Pradesh India Madan Mohan Malviya Univ Technol Dept CSE Gorakhpur Uttar Pradesh India

ISBN: (纸本)9781728127910

Wireless sensor networks (WSN) became very popular in last few years. They are deployed in distributed manner for collecting variety of data. There are a lot of research issues and challenges in WSN viz;energy efficiency, security, localization etc. Outlier or anomaly detection is one of such area to prevent malicious attacks or reducing the errors and noisy data in millions of wireless sensor networks. Outlier detection models should not compromise with quality of data. We have to identify the anomalies in offline mode or online mode with accuracy, better performance and intake of minimal resources in the network. There are various machine learning techniques which have been used by several researchers these days to detect outliers. This paper presents a survey on outlier detection in WSN data using various machine learning techniques.

关键词： Machine Learning Anomaly Detection Outlier Detection Wireless sensor Network Internet of Things

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Real-time adaptable resource allocation for distributed data-intensive applications over cloud and edge environments 9

Real-time adaptable resource allocation for distributed data...

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9th ieee international conference on Cloud computing in Emerging Markets, CCEM 2020

作者： Tom-Ata, Jean-Didier Totow Kyriazis, Dimosthenis University of Piraeus Department of Digital Systems Piraeus Greece

ISBN: (纸本)9781728195766

Applications performance is strongly linked with the total load, the application deployment architecture and the amount of resources allocated by the cloud or edge computing environments. Considering that the majority of the applications tends to be data intensive, the load becomes quite dynamic and depends on the data aspects, such as the data sources locations, their distribution and the data processing aspects within an application that consists of micro-services. In this paper we introduce an analysis and prediction model that takes into account the characteristics of an application in terms of data aspects and the edge computing resources attributes, such as utilization and concurrency, in order to propose optimized resources allocation during runtime. © 2020 ieee.

关键词： Resource allocation

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DSANA: A distributed machine learning acceleration solution based on dynamic scheduling and network acceleration 22

DSANA: A distributed machine learning acceleration solution ...

引用

22nd ieee international conference on High Performance computing and Communications, 18th ieee international conference on Smart City and 6th ieee international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

作者： Zhang, Runhua Shen, Guowei Gong, Liangyi Guo, Chun Guizhou University China Guiyang Beihang University China Beijing China Guizhou University China Guiyang China Tsinghua University China Beijing China

ISBN: (纸本)9781728176499

distributed machine learning(DML) has become a feasible solution to deal with the growing training data and models. Reviewing the existing architecture of DML, Parametric server(PS) architecture stands out in iterative convergence algorithms and widely deployed in practice, thanks to flexible expansion and so on. Under this architecture, the parameter synchronization mode based on Bulk Synchronous Parallel(BSP) has become one of the research hotspots. As for the BSP mode, each iteration efficiency is determined by the slowest node in the cluster, therefore, the straggler problem becomes the main reason for reducing the efficiency of DML training, which is even more prominent in the heterogeneous cloud services. Existing works mainly focus on the straggler problem, and the importance of communication is usually ignored. However, inefficient communication is also one of the reasons for the inefficiency of DML iterations. In this paper, we propose DSANA, which first alleviates certain straggler problems by dynamically scheduling computation tasks. Secondly, DSANA improves the overlap of computation/communication by dividing larger transmission parameters, thus further improving the iteration efficiency of DML training. We conduct comparison experiments with the classic iterative algorithm PageRank on four different-scale data sets in two cloud service scenarios. The experimental results show that DSANA can improve the training efficiency to 36.6%\sim 56.4% compared with the baseline solution. © 2020 ieee.

关键词： Efficiency

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A Lightweight Deep Learning Network for sensor-based Human Activity Recognition using IMU sensors of a Low-Power Wearable Device 25

A Lightweight Deep Learning Network for Sensor-based Human A...

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25th international Computer Science and Engineering conference, ICSEC 2021

作者： Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

Human Activity Recognition (HAR) is an intriguing approach to healthcare monitoring that necessitates the ongoing utilization of wearable sensors to capture everyday activities. The most advanced studies using wearable devices have used a cloud computing paradigm that categorized data at remote systems. This method has drawbacks in terms of energy consumption, latency, and privacy. As a result, we adhere to a low-power computing structure in which wearable device solutions achieve an acceptable level of performance while being energy and memory-efficient. This study proposes a lightweight deep learning model termed a gate recurrent unit (GRU) network for energy-efficient HAR appropriate for low-power wearable devices. The introduced GRU network is evaluated against a benchmark dataset named the w-HAR dataset, including essential deep learning networks. The proposed GRU surpasses baseline deep learning networks in terms of overall accuracy, with a score of 95.160%. © 2021 ieee.

关键词： Energy efficiency

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Proceedings - 2020 international conference on Space-Air-Ground computing, SAGC 2020

Proceedings - 2020 International Conference on Space-Air-Gro...

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1st international conference on Space-Air-Ground computing, SAGC 2020

ISBN: (纸本)9780738124384

The proceedings contain 19 papers. The topics discussed include: a cloud storage framework for massive meteorological and oceanographic data and the application of virtualization technology;on link performance according to flight route in drone-based wide area wireless sensor network;multi-authority attribute based encryption with policy-hidden and accountability;distributed task offloading and resource allocation in vehicular edge computing;Stackelberg game based computation offloading and resource allocation in mobile edge computing;and research and backoff algorithm improvement of statistical priority-based multiple access protocol.

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Decentralized Deep Reinforcement Learning for a distributed and Adaptive Locomotion Controller of a Hexapod Robot

Decentralized Deep Reinforcement Learning for a Distributed ...

引用

ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Schilling, Malte Konen, Kai Ohl, Frank W. Korthals, Timo Bielefeld Univ Neuroinformat Grp D-33501 Bielefeld Germany Otto von Guericke Univ Dept Syst Physiol Learning Leibniz Inst Neurobiol Magdeburg Germany Otto von Guericke Univ Inst Biol Magdeburg Germany Bielefeld Univ Kognitron & Sensor Grp D-33501 Bielefeld Germany

ISBN: (纸本)9781728162126

Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots. While machine learning has been successfully applied to many tasks in recent years, Deep Reinforcement Learning approaches still appear to struggle when applied to real world robots in continuous control tasks and in particular do not appear as robust solutions that can handle uncertainties well. Therefore, there is a new interest in incorporating biological principles into such learning architectures. While inducing a hierarchical organization as found in motor control has shown already some success, we here propose a decentralized organization as found in insect motor control for coordination of different legs. A decentralized and distributed architecture is introduced on a simulated hexapod robot and the details of the controller are learned through Deep Reinforcement Learning. We first show that such a concurrent local structure is able to learn better walking behavior. Secondly, that the simpler organization is learned faster compared to holistic approaches.

关键词： Reinforcement learning

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Deep Learning for Surface Wave Identification in distributed Acoustic Sensing Data 8

Deep Learning for Surface Wave Identification in Distributed...

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8th ieee international conference on Big Data (Big Data)

作者： Dumont, Vincent Tribaldos, Veronica Rodriguez Ajo-Franklin, Jonathan Wu, Kesheng Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Rice Univ Houston TX USA

ISBN: (纸本)9781728162515

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130 Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170 GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.

关键词： Big Data Deep Learning distributed Acoustic Sensing distributed Training sensor Network Technology

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Proceedings - ieee international conference on distributed computing in sensor systems, DCOSS 2012

Proceedings - IEEE International Conference on Distributed C...

引用

8th ieee international conference on distributed computing in sensor systems, DCOSS 2012

ISBN: (纸本)9780769547077

The proceedings contain 56 papers. The topics discussed include: should I stay or should I go? maximizing lifetime with relays;network lifetime maximization in delay-tolerant sensor networks with a mobile sink;achieving high lifetime and low delay in very large sensors networks using mobile sinks;efficient mobile data collection with mobile collect;throughput maximization in mobile WSN scheduling with power control and rate selection;coverage estimation in heterogeneous visual sensor networks;adaptive synchronization control with multi-level buffer in wireless multimedia sensor networks;timely report delivery in social swarming applications;personal marks and community certificates: detecting clones in wireless mobile social networks;a ubiquitous publish/subscribe platform for wireless sensor networks with mobile mules;and distributed subspace projection in wireless sensor networks using computational codes.

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DCache: A distributed Cache Mechanism for HDFS based on RDMA 22

DCache: A Distributed Cache Mechanism for HDFS based on RDMA

引用

作者： Zhang, Xiao Liu, Binbin Gou, Zian Shi, Jia Zhao, Xiaonan Northwestern Polytechnical University School of Computer Science 127 Youyi xi Road Xi'an Shaanxi710129 China

ISBN: (纸本)9781728176499

Hadoop distributed File System (HDFS) is an important component of Hadoop, which provides data storage service. The performance of the IO subsystem has a great influence on data processing efficiency. In the Hadoop system, computing jobs are scheduled to the nodes where the data is located to reduce IO time. The job scheduler and data distribution have a great impact on the efficiency of data processing. HDFS provides a mechanism for users to specify files or directories to be cached. But the cached data is only accessible for the jobs running in the same data nodes. In this paper, we present a distributed cache mechanism for HDFS with RDMA-capable network. We separate the caching function from the original data nodes. We design and implement a new kind of component for the caching function. The cached data in these nodes can be accessed directly by any nodes in the cluster through Remote Direct Memory Access (RDMA). Compared with the existing caching mechanism, our approach improves the performance and stability of the IO process. We also accelerate the data writing process using our caching mechanism. The experiments show that compared with the existing caching mechanism of HDFS, our scheme improves the reading latency up to 3 times, improves the throughput by 30% \sim 95% for large files, and the writing performance by 64%. When the task runs on different nodes, the IO fluctuation caused by different data distribution is dropped from 20.71 to 0.50. © 2020 ieee.

关键词： File organization

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Smart Cities: A Taxonomy for the Efficient Management of Lighting in Unpredicted Environments 16th

Smart Cities: A Taxonomy for the Efficient Management of Lig...

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16th international Symposium on distributed computing and Artificial Intelligence (DCAI)

作者： Saenz-Penafiel, Juan-Jose Poza-Lujan, Jose-Luis Posadas-Yague, Juan-Luis Univ Politecn Valencia Doctoral Sch Camino Vera S-N E-46022 Valencia Spain Univ Politecn Valencia Univ Inst Control Syst & Ind Comp Ai2 Camino Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9783030238872;9783030238865

In recent years there has been a substantial increase in the number of outdoor lighting installations, the energy management of this has not been greatly improved and electricity consumption has skyrocketed. Most of it does not come from renewable energies with all the negative effects that this entails. With all this, public lighting can represent up to a total of 54% of the energy consumption of a municipality and up to 61% of its electricity consumption. This work focuses on the analysis of the factors to consider in the implementation and application of a lighting control system in a real environment for energy saving. The system should be based on the collection of data by the different sensors installed in the luminaries of the route oriented to the environment of the Smart Cities and the Intelligent Transport systems (ITS). The main objective is to try to reduce the consumption of electrical energy as much as possible while maintaining the comfort that the road user feels in it. For this, the weak points of these systems will be searched and their elimination will be sought. A study will be made of the situation of the systems available today. The characteristics of these systems will be analysed. Based on the characteristics of the systems analysed, the necessary requirements of the system presented will be determined. The characteristics that will make this project different from the rest will be established. An architecture proposal that seeks to optimise the parameters analysed will be presented.

关键词： Smart Cities distributed systems distributed architectures Taxonomy Power Consumption

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