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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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6th ieee international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, ieee CloudTech 2023

6th IEEE International Conference on Cloud Computing and Art...

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6th ieee international conference on Cloud computing and Artificial Intelligence: Technologies and Applications, ieee CloudTech 2023

ISBN: (纸本)9783031786976

The proceedings contain 15 papers. The special focus in this conference is on Cloud computing and Artificial Intelligence: Technologies and Applications. The topics include: CAReNet: A Promising AI Architecture for Low Data Regime Mixing Convolutions and Attention;Designing Converged Middleware for HPC, AI, and Big Data: Challenges and Opportunities;key Mechanisms and Emerging Issues in Cloud Identity systems;Power Consumption in HPC-AI systems;integrated Architecture for Cloud and IoT with Logical sensors and Actuators - Logical IoT Cloud;The Need for HPC in AI Solutions;facts and Issues of Neural Networks for Numerical Simulation;scalable Deep Learning for Industry 4.0: Speedup with distributed Deep Learning and Environmental Sustainability Considerations;multi-domain Dataset for Moroccan Arabic Dialect Sentiment Analysis in Social Networks;on the Challenges of Migrating to Microservices Architectures for Better Cloud Solutions;missing Data Imputation Approach for IoT Using Machine Learning;holistic Approach for Enhanced Object Recognition in Complex Environments;analyzing Sentiment in Arabic Tweets: A Study Using Machine Learning and Deep Learning Techniques.

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DiCent: A distributed Credit Incentive Mechanism for Opportunistic Networks 19

DiCent: A Distributed Credit Incentive Mechanism for Opportu...

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19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Christiani, Daniel de M. C. Simoes, Jefferson E. Rocha, Antonio A. de A. Campos, Carlos Alberto V. Fed Univ State Rio De Janeiro Rio De Janeiro RJ Brazil Univ Fed Fluminense Niteroi RJ Brazil

ISBN: (纸本)9798350333398

Incentive mechanisms are increasingly needed in opportunistic networks that contain nodes with selfish behavior. For this, credit-based mechanisms need a virtual bank (central entity) to promote the incentive. However, the existence of this central entity in an opportunistic network may be challenging. Therefore, we propose a credit incentive mechanism in this paper. The main contribution is that the mechanism does not use a virtual bank to distribute credits (reward for forwarding messages) but rather a decentralized approach. In addition, we propose mathematical modeling to represent the collection and distribution of credits to avoid Edge Insertion attacks in some instances. Finally, the proposed mechanism was evaluated through simulation using real mobility traces and different routing protocols and compared its performance with the RELICS incentive mechanism. The obtained results show that the proposed mechanism is promising in diminishing the occurrence of selfish nodes.

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A Novel distributed Task Scheduling Framework for Supporting Vehicular Edge Intelligence 42

A Novel Distributed Task Scheduling Framework for Supporting...

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Yang, Kun Sun, Peng Lin, Jieyu Boukerche, Azzedine Song, Liang Fudan Univ Acad Engn & Amp Technol Shanghai Peoples R China Duke Kunshan Univ Suzhou Peoples R China Univ Toronto Toronto ON Canada Univ Ottawa Ottawa ON Canada

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

In recent years, data-driven intelligent transportation systems (ITS) have developed rapidly and brought various AI-assisted applications to improve traffic efficiency. However, these applications are constrained by their inherent high computing demand and the limitation of vehicular computing power. Vehicular edge computing (VEC) has shown great potential to support these applications by providing computing and storage capacity in close proximity. For facing the heterogeneous nature of in-vehicle applications and the highly dynamic network topology in the Internet-of-Vehicle (IoV) environment, how to achieve efficient scheduling of computational tasks is a critical problem. Accordingly, we design a two-layer distributed online task scheduling framework to maximize the task acceptance ratio (TAR) under various QoS requirements when facing unbalanced task distribution. Briefly, we implement the computation offloading and transmission scheduling policies for the vehicles to optimize the onboard computational task scheduling. Meanwhile, in the edge computing layer, a new distributed task dispatching policy is developed to maximize the utilization of system computing power and minimize the data transmission delay caused by vehicle motion. Through single-vehicle and multi-vehicle simulations, we evaluate the performance of our framework, and the experimental results show that our method outperforms the state-of-the-art algorithms. Moreover, we conduct ablation experiments to validate the effectiveness of our core algorithms.

关键词： Vehicular edge computing mobility-aware distributed scheduling in-vehicle application resource allocation

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Scalable Big Spatial Data Processing with SQL Query Compilation and distributed Morsel-driven Parallelism

Scalable Big Spatial Data Processing with SQL Query Compilat...

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2024 ieee international conference on Big Data, BigData 2024

作者： Sahni, Rahul Zhang, Xiaozheng Chatterjee, Sudip Ray, Suprio University of New Brunswick Fredericton Canada

ISBN: (纸本)9798350362480

The rapid rise in spatial data volumes from diverse sources necessitate efficient spatial data processing capability. Although most relational databases support spatial extensions of SQL query features, they offer limited scalability. Traditional relational database query processing follows a pull-based (or tuple-at-a-time) model of query processing. This is not efficient for processing large volumes of data. A number of specialized spatial data processing systems were developed that extend cluster computing frameworks, such as Spark and Hadoop. However, these systems are characterized by limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based *** present a compilation-based distributed SQL query processing system. It follows a data-centric query compilation approach that takes a SQL query and generates distributed C++ (UPC++) based physical query plans. The generated code is compiled and executed on a distributed in-memory high performance framework based on the Partitioned Global Address Space (PGAS) paradigm. We also introduce morsel-driven parallelism for scalable spatial query execution in a distributed runtime. We conduct experimental evaluation of our system with two real-world datasets on a number of spatial query workloads. Experimental results demonstrate that our system performs significantly better than a leading spatial big data system Apache Sedona and distributed parallel relational database Citus. © 2024 ieee.

关键词： Structured Query Language

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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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distributed Reconstruction of Noisy Pooled Data 42

Distributed Reconstruction of Noisy Pooled Data

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Hahn-Klimroth, Max Kaaser, Dominik TU Dortmund Univ Dortmund Germany TU Hamburg Hamburg Germany

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

In the pooled data problem we are given a set of n agents, each of which holds a hidden state bit, either 0 or 1. A querying procedure returns for a query set the sum of the states of the queried agents. The goal is to reconstruct the states using as few queries as possible. In this paper we consider two noise models for the pooled data problem. In the noisy channel model, the result for each agent flips with a certain probability. In the noisy query model, each query result is subject to random Gaussian noise. Our results are twofold. First, we present and analyze for both error models a simple and efficient distributed algorithm that reconstructs the initial states in a greedy fashion. Our novel analysis pins down the range of error probabilities and distributions for which our algorithm reconstructs the exact initial states with high probability. Secondly, we present simulation results of our algorithm and compare its performance with approximate message passing (AMP) algorithms that are conjectured to be optimal in a number of related problems.

关键词： Reconstruction Pooled Data Random Noise Greedy Algorithm Approximate Message Passing

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A Comparative Study of Datasets for Cyber-attacks Detection in Wireless sensor Networks 3

A Comparative Study of Datasets for Cyber-attacks Detection ...

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3rd international conference on computing and Machine Intelligence (ICMI)

作者： Ismail, Shereen Dawoud, Diana W. Reza, Hassan Univ N Dakota Sch Elect Engn & Comp Sci Grand Forks ND 58202 USA Univ Dubai Coll Engn & Informat Technol Dubai U Arab Emirates

ISBN: (纸本)9798350372977;9798350372984

Machine Learning (ML) is one of the effective security approaches to build cyber-attacks detection systems in Wireless sensor Networks (WSNs). ML leverages the power of data analysis and pattern recognition to detect and classify various types of cyber-attacks to enhance the security of WSNs. A well-constructed dataset is one of the key factors that significantly impact the performance and generalization capabilities of any ML classifier trained on it. In this paper, we evaluate the effectiveness of two datasets: WSN-DS and WSN-BFSF which are specialized for Denial-of-Service (DoS) attacks targeting WSNs. We compare the two datasets in terms of their key characteristics, dataset quality, and ML classification performance. Mutual Information (MI) and Recursive Feature Elimination (RFE) are used for feature selection. The dataset quality is measured using statistical information calculation. The ML classification performance is investigated for three supervised ensemble techniques: LightGBM, bagging, and stacking using evaluation metrics including probability of detection, probability of false alarm, probability of misdetection, classification accuracy, model size, and processing time.

关键词： Machine Learning Security cyber-attacks Detection Dataset Quality Classification Performance

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Querying distributed sensor Streams in the Edge-to-Cloud Continuum 6

Querying Distributed Sensor Streams in the Edge-to-Cloud Con...

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

作者： Karlstetter, Roman Widhopf-Fenk, Robert Schulz, Martin IfTA GmbH Puchheim Germany Tech Univ Munich Garching Germany

ISBN: (数字)9781665481403

ISBN: (纸本)9781665481403

sensor data is of crucial importance in many IoT scenarios. It is used for online monitoring as well as long term data analytics, enabling countless use cases from damage prevention to predictive maintenance. Multivariate sensor time series data is acquired and initially stored close to the sensor, at the edge. It is also beneficial to summarize this data in windowed aggregations at different resolutions. A subset of the resulting aggregation hierarchy is typically sent to a cloud infrastructure, often via intermittent or low bandwidth connections. Consequently, different views on the data exist on different nodes in the edge-to-cloud continuum. However, when querying this data, users are interested in a fast response and a complete, unified view on the data, regardless of which part in the infrastructure continuum they send the query to and where the data is physically stored. In this paper, we present a loosely coupled approach that enables fast range queries on a distributed and hierarchical sensor database. Our system only assumes the possibility of fast local range queries on a hierarchical sensor database. It does not require any shared state between nodes and thus degrades gracefully in case certain parts of the hierarchy are unreachable. We show that our system is suitable for driving interactive data exploration sessions on terabytes of data while unifying the different views on the data. Thus, our system can improve the data analysis experience in many geo-distributed scenarios.

关键词： edge cloud multivariate sensor data stream distributed query

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distributed Runtime Verification of Metric Temporal Properties for Cross-Chain Protocols 42

Distributed Runtime Verification of Metric Temporal Properti...

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Ganguly, Ritam Xue, Yingjie Jonckheere, Aaron Ljung, Parker Schornstein, Benjamin Bonakdarpour, Borzoo Herlihy, Maurice Michigan State Univ E Lansing MI 48824 USA Brown Univ Providence RI 02912 USA

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Transactions involving multiple blockchains are implemented by cross-chain protocols. These protocols are based on smart contracts, programs that run on blockchains, executed by a network of computers. Verifying the runtime correctness of smart contracts is a problem of compelling practical interest since, smart contracts can automatically transfer ownership of cryptocurrencies, electronic securities, and other valuable assets among untrusting parties. Such verification is challenging since smart contract execution is time sensitive, and the clocks on different blockchains may not be perfectly synchronized. This paper describes a method for runtime monitoring of blockchain executions. First, we propose a generalized runtime verification technique for verifying partially synchronous distributed computations for the metric temporal logic (MTL) by exploiting bounded-skew clock synchronization. Second, we introduce a progression-based formula rewriting scheme for monitoring MTL specifications which employs SMT solving techniques and report experimental results.

关键词： Metric Temporal Logic Partial Synchrony distributed systems Runtime Verification Blockchain Cross-Chain Protocols

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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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