2022 IEEE International Conference on Edge Computing and Communications (EDGE)最新文献_第2页

Resilience-Focused Monitoring Framework for Edge Systems 边缘系统的弹性监测框架

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00030

Alex Skov Klitgaard, Anders Alexander Sønderby, Henrik Stensgaard Jørgensen, Kristian Walstrøm Petersen, Junior Dongo, M. Albano

{"title":"Resilience-Focused Monitoring Framework for Edge Systems","authors":"Alex Skov Klitgaard, Anders Alexander Sønderby, Henrik Stensgaard Jørgensen, Kristian Walstrøm Petersen, Junior Dongo, M. Albano","doi":"10.1109/EDGE55608.2022.00030","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00030","url":null,"abstract":"Developing applications on the edge is a difficult task, as issues arise when multiple nodes have to operate in unison. Common challenges comprise correctness, performance, and forensics analysis after a system crash. The challenges are aggravated when both edge and fog devices get in the picture, since the limited resources of some of them exacerbate any possible issue, and create the need for a robust mechanism to collect log data to make sense of the system's behavior. The envisioned monitoring layer should collect log data regarding performance and errors, protect the data until they are delivered to an external data processing facility, and be easy to integrate into the deployed system. This paper proposes C-Mon, a resilience-focused monitoring framework for service-oriented applications that tests timing constraints against live log data. The monitoring framework is integrated with OpenAPI Generator, the mainstream ReST code generator we used to generate client and server interfaces and monitoring mechanisms. Log data are cached locally on the disk of the fog/edge devices and transferred to the monitoring server only when enough CPU cycles and network bandwidth are available, at the same time enabling forensics analysis.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117051978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Robust Latent Factor Analysis Model for Incomplete Data Recovery in Wireless Sensor Networks 无线传感器网络中不完全数据恢复的鲁棒潜在因素分析模型

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00033

Zhikai Yu, Di Wu, Yi He

{"title":"A Robust Latent Factor Analysis Model for Incomplete Data Recovery in Wireless Sensor Networks","authors":"Zhikai Yu, Di Wu, Yi He","doi":"10.1109/EDGE55608.2022.00033","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00033","url":null,"abstract":"Wireless sensor network (WSN) is an emerging and promising development direction in recent years. Due to the sudden breakdown of the sensor nodes or saving energy by deliberately shutting down partial sensors, there are always massive missing entries in the collected sensing data from WSNs. Low-rank matrix approximation (LRMA) is a typical and effective approach to achieving data recovery for WSNs. However, existing LRMA-based approaches ignore the adverse effects of noises inevitably mixed with collected data, which may dramatically degrade their recovery accuracy. In this paper, we aim at recovering missing data based on partially observed data mixed with noises in WSNs. To this end, we propose a latent factor analysis (LFA) based spatio-temporal signal recovery (STSR) model, named LFA-STSR. Its main idea is to incorporate the spatio-temporal correlation into a robust LFA model as the regularization constraint, making it possess the merits of both high accuracy and robustness during data recovery. To evaluate the proposed LFA-STSR model, we conduct extensive experiments on two real-world WSNs datasets. The results demonstrate that LFASTSR has significantly higher recovery accuracy than the related state-of-the-art models.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122498998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Skill Model Supporting Complex and Reliable Interactions between Users, VPA and External Services 一种支持用户、VPA和外部服务之间复杂可靠交互的新技能模型

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00028

Xizhe Zhang, Demin Yu, Min Liu, Zhongjie Wang

{"title":"A Novel Skill Model Supporting Complex and Reliable Interactions between Users, VPA and External Services","authors":"Xizhe Zhang, Demin Yu, Min Liu, Zhongjie Wang","doi":"10.1109/EDGE55608.2022.00028","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00028","url":null,"abstract":"With the development of AI and dialog technologies, Virtual Personal Assistants (VPAs) have been used on various types of mobile and edge devices for convenient man-machine interaction. VPA's capabilities are commonly called \"skills\". VPA can use skills to get users intent and provide corresponding services such as playing music. However, the skills supported by existing VPAs have many shortcomings. First of all, skills cannot support complex interactions between users and VPAs. The interaction process is often just a simple process. Secondly, many skills are unreliable because they frequently terminate execution when they receive unexpected input. Moreover, the services supported by current skills are very limited that cannot meet the diverse needs of users. In order to solve these problems, this paper proposes a novel skill model named CRSModel to support complex and reliable interactions between users, VPA and external services. In order to improve the complexity of interactions between users and VPA, CRSModel adds many configurations that are helpful to support complex interactions, and appends the dialogue flow processing mechanism to the execution process that can let users change the dialog flow. In terms of reliability, CRSModel's execution process can capture unexpected input and guide the user to input an expected value. Finally, in order to meet the diverse needs of users, CRSModel can support external services that satisfy the REST protocol, greatly increasing the variety of services.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122911412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring of IoT Systems at the Edges with Transformer-based Graph Convolutional Neural Networks 基于变压器的图卷积神经网络边缘物联网系统监测

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00018

Amadou Ba, Fabio Lorenzi, J. Ploennigs

{"title":"Monitoring of IoT Systems at the Edges with Transformer-based Graph Convolutional Neural Networks","authors":"Amadou Ba, Fabio Lorenzi, J. Ploennigs","doi":"10.1109/EDGE55608.2022.00018","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00018","url":null,"abstract":"Internet of Things (IoT) systems are complex, and consist of distributed and interdependent components, making the modeling, prediction and monitoring of their behavior a critical challenge. This challenge is specifically exacerbated by the need to develop precise models that inherently consider the domain knowledge about the underlying physical structure, and the causal interdependencies between the various components of IoT systems. In this paper, we present an approach that is capable of leading to improved modeling and monitoring at the edges. To this end, we consider an IoT system as a network that can be represented by a graph. Then, we develop an approach that combines a semantic knowledge graph with a Transformer-based neural networks, under a Graph Convolutional Neural Networks (GCNN). Our GCNN produces features that are subsequently used with the Transformer to learn the parameters of an IoT model. We exploit the parameters of the model for monitoring and anomaly detection. We validate our approach using a real robot workcell for anomaly detection during a pick and place process, and we demonstrate that our approach outperforms other competitive techniques.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131685898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

uDiscover: User-Driven Service Discovery in Pervasive Edge Computing using NDN uDiscover:使用NDN的普及边缘计算中用户驱动的服务发现

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00022

George Torres, R. Tourani, Abderrahmen Mtibaa, D. Stelmakh, S. Misra, S. Srikanteswara, Yan Zhang, S. Hoque

{"title":"uDiscover: User-Driven Service Discovery in Pervasive Edge Computing using NDN","authors":"George Torres, R. Tourani, Abderrahmen Mtibaa, D. Stelmakh, S. Misra, S. Srikanteswara, Yan Zhang, S. Hoque","doi":"10.1109/EDGE55608.2022.00022","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00022","url":null,"abstract":"New breed of applications, such as autonomous driving and their need for computation-aided quick decision making has motivated the delegation of compute-intensive services (e.g., video analytic) to the more powerful surrogate machines at the network edge–edge computing (EC). Recently, the notion of pervasive edge computing (PEC) has emerged, in which users’ devices can join the pool of the computing resources that perform edge computing. Inclusion of users’ devices increases the computing capability at the edge (adding to the infrastructure servers), but in comparison to the conventional edge ecosystems, it also introduces new challenges, such as service orchestration (i.e., service placement, discovery, and migration). We propose uDiscover, a novel user-driven service discovery and utilization framework for the PEC ecosystem. In designing uDiscover, we considered the Named-Data Networking architecture for balancing users workloads and reducing user-perceived latency. We propose proactive and reactive service discovery approaches and assess their performance in PEC and infrastructure-only ecosystems. Our simulation results show that (i) the PEC ecosystem reduces the user-perceived delays by up to 70%, and (ii) uDiscover selects the most suitable server–\"accurate\" delay estimates with less than 10% error–to execute any given task.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129124891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Spatial Goal Refinement Patterns for IoT Applications 物联网应用的空间目标细化模式

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00019

Yazeed AlZahrani, Jun Shen, Jun Yan

{"title":"Spatial Goal Refinement Patterns for IoT Applications","authors":"Yazeed AlZahrani, Jun Shen, Jun Yan","doi":"10.1109/EDGE55608.2022.00019","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00019","url":null,"abstract":"The development of the Internet of Things (IoT) is gaining more and more strength. Over the years, IoT has offered different solutions in many areas and contexts. The diversity of these challenges makes it hard to grasp the different solutions’ underlying principles and design an appropriate custom implementation for the IoT space. This paper aims to study numerous production-ready IoT offerings, extract recurring proven solution principles, and classify them into spatial patterns. The method of refinement of the goals is employed so that complex challenges can be solved by breaking them down into achievable and straightforward sub-goals. The present work deals with the major sub-goals, e.g. efficient coverage of the field, connectivity of the IoT devices, spatio-temporal aggregation of the data, and estimation of spatially connected regions of event detection. We have proposed methods to achieve each sub-goal for all different types of spatial patterns. The spatial patterns developed can be used in ongoing and future research on the IoT to understand the principles of the IoT, which will, in turn, promote the better development of existing and new IoT devices.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115463828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Message from the EDGE Chairs 来自EDGE椅子的信息

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/edge55608.2022.00011

引用次数: 0

Role of Fog Computing in Smart Spaces 雾计算在智能空间中的作用

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00021

Fatema Elwy, Raafat Aburukba, A. Al-Ali

{"title":"Role of Fog Computing in Smart Spaces","authors":"Fatema Elwy, Raafat Aburukba, A. Al-Ali","doi":"10.1109/EDGE55608.2022.00021","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00021","url":null,"abstract":"As the prevalence of smart city applications in the era of the digital economy is significantly growing, the Internet of Things (IoT) has evolved to be a part of our everyday lives. Most IoT applications and smart spaces are implemented in the context of a two-tier approach, with the lower tier being the IoT edge devices and the upper tier being the back-end servers in cloud data centers. However, the growth of IoT data and devices at the edge of smart applications challenges the efficiency and time-sensitivity of the two-tier approach. Hence, it is critical to comprehend the significance of integrating a middle tier between the cloud and edge tiers, known as the fog tier. The fog tier’s main purpose is to decentralize the IoT workload to reduce latency and increase efficiency. This paper provides a review and presents a 3-Tier, 3-Dimensions reference model for smart spaces. The 3-tier represent the edge, fog, and cloud tiers, where each tier is coupled with the 3-dimensions that include computing, network, and application. Each dimension within each tier is presented along with the existing technologies that deliver on each dimension. The work presents a case study within a smart healthcare application that utilizes the presented reference model.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132725734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Novel Heterogeneous Computing Middleware for Mobile AI Services 一种新的移动人工智能服务异构计算中间件

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00034

Zihao Shao, Tonghua Su, Manyang Xu, Qinglin Liu, Ruipeng Han, ZhongJie Wang

{"title":"A Novel Heterogeneous Computing Middleware for Mobile AI Services","authors":"Zihao Shao, Tonghua Su, Manyang Xu, Qinglin Liu, Ruipeng Han, ZhongJie Wang","doi":"10.1109/EDGE55608.2022.00034","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00034","url":null,"abstract":"Mobile applications play an important role in edge computing. Recently, with the advancement of more powerful mobile devices, deep models have been widely deployed in mobile applications to provide more intelligent and convenient services to users. However, mobile devices still face critical challenges, such as restricted power, sensitive latency, limited memory. As emerging AI chips are integrated into mobile devices, mobile AI solutions are proposed to accelerate model execution and improve mobile service experience. Unfortunately, they only focus on the acceleration of single chip and ignore the heterogeneity among different chips. This paper proposes a mobile heterogeneous computing AI middleware, which provides developers of AI applications with the ability to accelerate model execution. This proposed middleware utilizes three heterogeneous chips, CPU, GPU, DSP for model scheduling at a layer-wise granularity. It is designed in five layers from top to bottom: User Interface Layer, Heterogeneous Scheduling Layer, Performance Collection Layer, Hardware Information Collection Layer, AI Framework Layer. We verified it in the environment of typical mobile device through a series of experiments. The results prove the high accuracy of data collection algorithm and the effectiveness of heterogeneous scheduling services. For AlexNet and Inception V3, our schedule plan achieves at least 36.39% improvement in inference time and at least 24.4% reduction in energy consumption.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126731493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ECBA-MLI: Edge Computing Benchmark Architecture for Machine Learning Inference ECBA-MLI:机器学习推理的边缘计算基准架构

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00016

Mathias Schneider, Ruben Prokscha, Seifeddine Saadani, A. Höß

引用次数: 1