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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30727 条记录，以下是221-230 订阅

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Economical high-speed inspection line for Tin-capped glass vials 16

Economical high-speed inspection line for Tin-capped glass v...

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16th international conference on Computer and Automation Engineering (ICCAE)

作者： Nanduri, Chanakya Hegde, Prarthana Hukkeri, Amey Rawashdeh, Nathir A. Michigan Technol Univ Dept Appl Comp Houghton MI 49931 USA

ISBN: (纸本)9798350370058;9798350370164

Vial cap inspection includes checking for missing caps, vial height or any other damage. Possible failures could be seemingly minor cosmetic problems, such as scratches or tiny punctures which could indicate potential seal problems and inappropriate height. The paper and the respective project target the over-priced setup used for high-speed inspection of glass vials in the pharma industry and offer a more economical layout/setup that can prove the same efficiency as any high sophisticated setup costing thousands of dollars. We replace the camera vision normally used in these systems by infrared radiation sensors which are easily integrated with Programmable Logic Controllers and are cheaper than the camera vision systems. We used ladder logic programming to compute for bad and good parts on the conveyer system.

关键词： Industrial automation PLC Inspection line Glass vial production High speed Inspection Economical automation solution Inductive sensor Infrared sensor Conveyor Pneumatic system Vial sorting

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Stash: A comprehensive stall-centric characterization of public cloud VMs for distributed deep learning 43

Stash: A comprehensive stall-centric characterization of pub...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Sharma, Aakash Bhasi, Vivek M. Singh, Sonali Jain, Rishabh Gunasekaran, Jashwant Raj Mitra, Subrata Kandemir, Mahmut Taylan Kesidis, George Das, Chita R. Penn State Univ Comp Sci & Engn University Pk PA 16802 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350339864

Deep neural networks (DNNs) are increasingly popular owing to their ability to solve complex problems such as image recognition, autonomous driving, and natural language processing. Their growing complexity coupled with the use of larger volumes of training data (to achieve acceptable accuracy) has warranted the use of GPUs and other accelerators. Such accelerators are typically expensive, with users having to pay a high upfront cost to acquire them. For infrequent use, users can, instead, leverage the public cloud to mitigate the high acquisition cost. However, with the wide diversity of hardware instances (particularly GPU instances) available in public cloud, it becomes challenging for a user to make an appropriate choice from a cost/performance standpoint. In this work, we try to address this problem by (i) introducing a comprehensive distributed deep learning (DDL) profiler Stash, which determines the various execution stalls that DDL suffers from, and (ii) using Stash to extensively characterize various public cloud GPU instances by running popular DNN models on them. Specifically, it estimates two types of communication stalls, namely, interconnect and network stalls, that play a dominant role in DDL execution time. Stash is implemented on top of prior work, DS-analyzer, that computes only the CPU and disk stalls. Using our detailed stall characterization, we list the advantages and shortcomings of public cloud GPU instances for users to help them make an informed decision(s). Our characterization results indicate that the more expensive GPU instances may not be the most performant for all DNN models and that AWS can sometimes sub-optimally allocate hardware interconnect resources. Specifically, the intra-machine interconnect can introduce communication overheads of up to 90% of DNN training time and the network-connected instances can suffer from up to 5x slowdown compared to training on a single instance. Furthermore, (iii) we also model the impact of DNN m

关键词： Characterization Cloud computing distributed deep learning Public cloud GPU

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An MDP-Based Approach for Distribution System Control with PV Generation and Battery Storage

An MDP-Based Approach for Distribution System Control with P...

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2024 international conference on Communications Control and computing Technologies for Smart Grids

作者： Sosnowskit, Robert Baszyrnski, Marcin Konstantinou, Charalambos AGH Univ Sci & Technol AGH UST Dept Power Elect & Energy Control Syst Krakow Poland King Abdullah Univ Sci & Technol KAUST CEMSE Div King WI USA

ISBN: (纸本)9798350318562;9798350318555

This paper proposes a decision-making approach for the control of distribution systems with distributed energy resources (DERs) equipped with photovoltaic (PV) units and battery energy storage systems (BESS). The objective is to minimize the total operational cost of the distribution system while satisfying the system operating constraints. The method is based on the discrete-time finite-horizon Markov Decision Process (MDP) framework. Different aspects of the distribution system operation are considered, such as the possibilities of curtailment of PV generation, managing battery storage, reactive power injection, load shedding, and providing a flexibility service for the transmission system. The model is tested for the ieee 33-bus system with two added DERs and the study cases involve various unexpected events. The experimental results show that this method enables the attainment of relatively low total cost values compared to the reference deterministic approach. The benefits of applying this approach are particularly evident when there is a significant difference between the predicted and actual PV power generation.

关键词： Distribution systems Markov Decision Process (MDP) distributed energy resources (DERs) PV battery energy storage systems (BESS)

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TopoCommit: A Topological Commit Protocol for Cross-Ledger Transactions in Scientific computing 25

TopoCommit: A Topological Commit Protocol for Cross-Ledger T...

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25th ieee international conference on Cluster computing (CLUSTER)

作者： Tawose, Olamide Timothy Yang, Lei Zhao, Dongfang Lincoln Univ Lincoln Univ PA 19352 USA Univ Nevada Reno NV 89557 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350307924

While increasingly more applications are tempted to manage their data in decentralized systems, such as blockchains or distributed ledgers, the data exchange across multiple, potentially heterogeneous, decentralized systems remains an open problem: State-of-the-art protocols cannot meet one or more of the core requirements, such as atomicity, liveness, and scalability. Specifically, in the field of scientific computing, although a blockchain service was recently developed for scientific computing environments, the data exchanges and transactions among distinct ledgers are not supported. Observing that many modern scientific applications are collaborated on by multiple teams and the increasingly complicated (in-situ) workflows thereof, we argue that there is a pressing need to realize an efficient and scalable protocol for distinct ledgers to exchange data in scientific computing. This paper proposes a topological approach to enabling atomic, nonblocking, and scalable data exchanges among an arbitrary number of scientific ledgers in the context of collaborative scientific computing. Specifically, we construct a topological space formed by these ledgers-abstracting those nodes in a cross-ledger transaction as topological objects such as abstract simplex and simplicial complex. These topological objects, in turn, serve as the building blocks of a topological protocol, namely TopoCommit, under practical assumptions. We implement TopoCommit and integrate it into SciChain, a recently published distributed ledger for tracking scientific data provenance. The extensive evaluation of up to 1,008 nodes and 144 distinct ledgers on CloudLab shows that TopoCommit outperforms state-of-the-art protocols by up to 70x.

关键词： Scientific data management distributed ledgers applied topology

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K-means ++ and Reinforcement Learning-Based Clustering Method to Achieve Energy Efficiency in Underwater Acoustic sensor Networks 21

K-means ++ and Reinforcement Learning-Based Clustering Metho...

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21st ieee international Symposium on Parallel and distributed Processing with Applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xiong, Tao Hou, Rui Zhou, Lingyun Xing, Guanglin Zeng, Deze South-Central Minzu University College of Computer Science Wuhan China China University of Geosciences Computer School Wuhan China

ISBN: (纸本)9798350329223

In recent years, underwater acoustic sensor networks (UASNs) have had a wide range of applications in the fields of underwater environment sensing, marine resource exploitation and water quality analysis. sensor nodes operate in an underwater environment and are powered by embedded batteries, making it very difficult to replenish power. It is thus important to efficiently improve sensor energy efficiency in UASNs and to extend the network lifetime. Clustering can effectively reduce UASN energy consumption by dividing sensor nodes into clusters;however, energy efficiency is always affected by the imbalance in energy consumption due to the unreasonable selection of cluster heads (CHs) and routes. In this paper, a K-means ++ and reinforcement learning-based clustering (KRC) method is proposed to improve energy efficiency. First, the UASN clusters are uniformly distributed using the K-means ++ algorithm. Then, a combination of clustering and reinforcement learning is applied to CH selection, which calculates the energy of intercluster data transmission when each node in the cluster is a CH based on the node's residual energy and location and selects the appropriate CHs to equalize the energy consumption of each network node. The optimal transmission route constructed by reinforcement learning is used to select suitable nodes for relaying data for sensor nodes. The simulation results show that the KRC method effectively balances UASN node energy consumption and improves energy efficiency, thereby extending network survival. © 2023 ieee.

关键词： Energy efficiency

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A Service for Resilient Manufacturing 9

A Service for Resilient Manufacturing

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ieee international conference on Smart computing (SMARTCOMP)

作者： Soderi, Mirco Breslin, John Gerard Univ Galway Data Sci Inst Galway Ireland

ISBN: (纸本)9798350322811

In modern industry, adaptation to market changes, as well as prompt reaction to a variety of predictable and unpredictable events, is a key requirement. Ubiquitous computing, real-time analytics, reconfigurable hardware/software components, often coexist in the complex, internally variegated, and often proprietary systems that are traditionally deployed to meet such requirement. However, such tailor-made systems meet only in part the requirements of openness, security, monitorability, geographical distribution, and most of all, remote extendability and changeability, which are crucial for prompt reaction to unforeseen circumstances. In this work, a containerized service application named Network Factory is presented. It enables the remote construction, configuration and operation of resilient computation systems that meet the above-mentioned requirements, and distinguish for their logical simplicity and for the uniform addressing of elaborations and human-computer interfaces, which are achieved through few reconfigurable components and communication mechanisms that are used from the production line up to the Cloud. Source code, documentation, and step-by-step introductory guides are publicly available in a dedicated GitHub repository, and distributed under the CC-BY-4.0 license.

关键词： Resiliency Reconfigurable manufacturing Ubiquitous computing Real-time analytics Open architecture Security Monitorability Changeable software Geo-distributed system Containerization

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NebulaStream - Data Stream Processing in Massively distributed, Heterogeneous, Volatile Environments 18

NebulaStream - Data Stream Processing in Massively Distribut...

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18th international conference on distributed and Event-Based systems (DEBS)

作者： Markl, Volker BIFOLD Berlin Germany TU Berlin Berlin Germany DFKI Berlin Germany

ISBN: (纸本)9798400704437

Modem data-driven applications arising in such domains as smart manufacturing, healthcare, and the Internet of Things, pose new challenges to data processing systems. Traditional stream processing systems, such as Flink, Spark, and Kafka Streams are ill-suited to cope with the massive scale of distribution, the heterogeneous computing landscape, and requirements, such as timely processing and actuation. Classical approaches like managed runtimes, interpretation-based query processing, and the optimization of single queries that neglect interactions, greatly limit throughput, latency, energy-efficiency, and the general usability of these systems for emerging applications involving distributed data processing at scale in a sensor-edge-cloud-environment. To overcome these limitations, we are researching and building NebulaStream, a novel data stream processing system for massively distributed, heterogeneous environments. NebulaStream supports (potentially resource-constrained) heterogeneous devices, a hierarchical topology (with the distribution of computation and data flow in a cloud-edge-continuum), and the sharing of computations and data across multiple concurrent queries.

关键词： Stream Processing Engines Query Compilation Query Execution distributed Processing Open Source NebulaStream

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Defense against Black Hole Attacks in Wireless sensor Network with Anomaly Report Cycling 20

Defense against Black Hole Attacks in Wireless Sensor Networ...

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20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and Intelligent Communications

作者： Vieira, Marcel A. Liu, Hong Naval Undersea Warfare Ctr Informat Syst Secur Div Newport RI 02841 USA Univ Massachusetts Dartmouth Dept Elect & Comp Engn N Dartmouth MA USA

ISBN: (纸本)9798350361261;9798350361278

Wireless sensor Network (WSN) becomes the dominate last-mile connection to cyber-physical systems and Internet-of-Things. However, WSN opens new attack surfaces such as black holes, where sensing information gets lost during relay towards base stations. Current defense mechanisms against black hole attacks require substantial energy consumption, reducing the system's lifetime. This paper proposes a novel approach to detect and recover from black hole attacks using an improved version of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. LEACH is an energy-efficient routing protocol for groups of battery-operated sensor nodes in hierarchy. A round of selection for cluster heads is scheduled in a set time. We propose to improve LEACH with Anomaly Report Cycling (ARC-LEACH), tradeoff between security strength and energy cost. ARC-LEACH absorbs an attack when it occurs by rotating cluster heads to reestablish communication and then sending a message from the base station to coordinate all nodes against the malicious nodes. ARC-LEACH actively blocks malicious nodes while leveraging the resilience of LEACH for stronger resistance to blackhole attacks. ARC-LEACH can provide more defense capability when under attack from multiple malicious nodes that would otherwise be defenseless by LEACH, with only minor increase in energy consumption.

关键词： network security wireless local area network (WLAN) wireless sensor network (WSN) cyber-physical system (CPS) Internet of Things (IoT)

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iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition 22

iMove: Exploring Bio-impedance Sensing for Fitness Activity ...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Liu, Mengxi Rey, Vitor Fortes Zhang, Yu Ray, Lala Shakti Swarup Zhou, Bo Lukowicz, Paul German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci Kaiserslautern Germany

ISBN: (纸本)9798350326031;9798350326048

Automatic and precise fitness activity recognition can be beneficial in aspects from promoting a healthy lifestyle to personalized preventative healthcare. While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive *** evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left *** contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3.22 % reaching 84.71 % compared to the 81.49 % of the IMU baseline model. We have also shown how bio-impedance can improve human activity recognition (HAR) directly through sensor fusion, reaching an average Macro F1 score of 89.57 % (two modalities required for both training and inference) even if Bio-impedance alone has an average macro F1 score of 75.36 %, which is outperformed by IMU alone. In addition, similar results were obtained in an extended study on lower body fitness activity classification, demonstrating the generalisability of our *** findings underscore the potential of sensor fusion and contrastive learning as valuable tools for advancing fitness activity recognition, with bio-impedance playing a pivotal role in augmenting the capabilities of IMU-based systems.

关键词： bio-impedance sensing human activity recognition contrastive learning sensor fusion

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Building a First Prototype of a Multi-scale Modular distributed Display 17

Building a First Prototype of a Multi-scale Modular Distribu...

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ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Lassabe, Frédéric Dhoutaut, Dominique Piranda, Benoît Kouchnarenko, Olga Bourgeois, Julien Cnrs Utbm FEMTO-ST Institute Belfort France Cnrs Univ. Franche-Comté FEMTO-ST Institute Montbéliard France Cnrs Univ. Franche-Comté FEMTO-ST Institute Besançon France

ISBN: (纸本)9798350351637

Blinky Blocks are cubic modular robots, which communicate with their neighbors through their faces, and change color using LEDs. We previously used sets of Blinky Blocks to display images on the basis of one Blinky Block being one pixel. In this paper, we build a multi-resolution screen with modular robots. This screen benefits from its distributed architecture: being able to work if some nodes fail, and being completely customizable. We propose a hardware architecture and related protocols to attach a 8 × 8 LED matrix to a Blinky Block. The architecture allows us to build distributed multi-resolution screens by mixing regular and enhanced Blinky Blocks. We demonstrate the usage of our integrated screen through experiments with continuously fed Blinky Blocks as well as an autonomous scrolling. © 2024 ieee.

关键词： Modular robots

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