2018 IEEE International Conference on Industrial Internet (ICII)最新文献

Cybersecurity Issues in Internet of Things and Countermeasures 物联网中的网络安全问题及对策

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00037

Hoda Ghadeer

引用次数: 15

A Flexible Retransmission Policy for Industrial Wireless Sensor Actuator Networks 工业无线传感器执行器网络的灵活重传策略

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00017

Ryan Brummet, Dolvara Gunatilaka, Dhruv Vyas, O. Chipara, Chenyang Lu

{"title":"A Flexible Retransmission Policy for Industrial Wireless Sensor Actuator Networks","authors":"Ryan Brummet, Dolvara Gunatilaka, Dhruv Vyas, O. Chipara, Chenyang Lu","doi":"10.1109/ICII.2018.00017","DOIUrl":"https://doi.org/10.1109/ICII.2018.00017","url":null,"abstract":"Real-time and reliable communication is essential for industrial wireless sensor-actuator networks. To this end, researchers have proposed a wide range of transmission scheduling techniques. However, these methods usually employ a link-centric policy which allocates a fixed number of retransmissions for each link of a flow. The lack of flexibility of this approach is problematic because failures do not occur uniformly across links and link quality changes over time. In this paper, we propose a flow-centric policy to flexibly and dynamically reallocate retransmissions among the links of a multi-hop flow at runtime. This contribution is complemented by a method for determining the number of retransmissions necessary to achieve a user-specified reliability level under two failures models that capture the common wireless properties of industrial environments. We demonstrate the effectiveness of flow centric policies using empirical evaluations and trace-driven simulations. Testbed experiments indicate a flow-centric policy can provide higher reliability than a link-centric policy because of its flexibility. Trace-driven experiments compare link-centric and flow-centric policies under the two reliability models. Results indicate that when the two approaches are configured to achieve the same reliability level, a flow-centric approach increases the median real-time capacity by as much as 1.42 times and reduces the end-to-end response times by as much as 2.63 times.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121805002","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: 23

Message from the ICII 2018 Program Co-Chairs ICII 2018项目联合主席致辞

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/icii.2018.00006

引用次数: 0

A Focused Crawler Model Based on Mutation Improving Particle Swarm Optimization Algorithm 基于变异改进粒子群优化算法的聚焦爬虫模型

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00031

Guangxia Xu, Peng Jiang, Chuang Ma, M. Daneshmand

引用次数: 1

Blockchain Enabled Internet-of-Things Service Platform for Industrial Domain 基于区块链的工业领域物联网服务平台

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00033

Chanhyung Lee, Nak-Myoung Sung, L. Nkenyereye, Jaeseung Song

引用次数: 3

IoT Based Positioning Service Platform 物联网定位服务平台

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00027

S. K. Datta, J. Haerri, C. Bonnet

引用次数: 0

ICII 2018 Organizing Committee ICII 2018组委会

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/icii.2018.00007

引用次数: 0

Integrated Analytics for IIoT Predictive Maintenance Using IoT Big Data Cloud Systems 使用物联网大数据云系统的工业物联网预测性维护集成分析

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00020

Hong Linh Truong

{"title":"Integrated Analytics for IIoT Predictive Maintenance Using IoT Big Data Cloud Systems","authors":"Hong Linh Truong","doi":"10.1109/ICII.2018.00020","DOIUrl":"https://doi.org/10.1109/ICII.2018.00020","url":null,"abstract":"For predictive maintenance of equipment with Industrial Internet of Things (IIoT) technologies, existing IoT Cloud systems provide strong monitoring and data analysis capabilities for detecting and predicting status of equipment. However, we need to support complex interactions among different software components and human activities to provide an integrated analytics, as software algorithms alone cannot deal with the complexity and scale of data collection and analysis and the diversity of equipment, due to the difficulties of capturing and modeling uncertainties and domain knowledge in predictive maintenance. In this paper, we describe how we design and augment complex IoT big data cloud systems for integrated analytics of IIoT predictive maintenance. Our approach is to identify various complex interactions for solving system incidents together with relevant critical analytics results about equipment. We incorporate humans into various parts of complex IoT Cloud systems to enable situational data collection, services management, and data analytics. We leverage serverless functions, cloud services, and domain knowledge to support dynamic interactions between human and software for maintaining equipment. We use a real-world maintenance of Base Transceiver Stations to illustrate our engineering approach which we have prototyped with state-of-the art cloud and IoT technologies, such as Apache Nifi, Hadoop, Spark and Google Cloud Functions.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927868","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: 14

A New Efficient Scheme for Securely Growing WBAN Nodes 一种新的高效WBAN节点安全增长方案

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00026

Zhouzhou Li, Hua Fang, Honggang Wang, Shaoen Wu, M. Daneshmand

引用次数: 1

An Edge Computing Framework for Real-Time Monitoring in Smart Grid 面向智能电网实时监控的边缘计算框架

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00019

Yutao Huang, Yuhe Lu, Feng Wang, Xiaoyi Fan, Jiangchuan Liu, Victor C. M. Leung

{"title":"An Edge Computing Framework for Real-Time Monitoring in Smart Grid","authors":"Yutao Huang, Yuhe Lu, Feng Wang, Xiaoyi Fan, Jiangchuan Liu, Victor C. M. Leung","doi":"10.1109/ICII.2018.00019","DOIUrl":"https://doi.org/10.1109/ICII.2018.00019","url":null,"abstract":"Due to the ever-growing demands in modern cities, unreliable and inefficient power transportation becomes one critical issue in nowadays power grid. This makes power grid monitoring one of the key modules in power grid system and play an important role in preventing severe safety accidents. However, the traditional manual inspection cannot efficiently achieve this goal due to its low efficiency and high cost. Smart grid as a new generation of the power grid, sheds new light to construct an intelligent, reliable and efficient power grid with advanced information technology. In smart grid, automated monitoring can be realized by applying advanced deep learning algorithms on powerful cloud computing platform together with such IoT (Internet of Things) devices as smart cameras. The performance of cloud monitoring, however, can still be unsatisfactory since a large amount of data transmission over the Internet will lead to high delay and low frame rate. In this paper, we note that the edge computing paradigm can well complement the cloud and significantly reduce the delay to improve the overall performance. To this end, we propose an edge computing framework for real-time monitoring, which moves the computation away from the centralized cloud to the near-device edge servers. To maximize the benefits, we formulate a scheduling problem to further optimize the framework and propose an efficient heuristic algorithm based on the simulated annealing strategy. Both real-world experiments and simulation results show that our framework can increase the monitoring frame rate up to 10 times and reduce the detection delay up to 85% comparing to the cloud monitoring solution.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122797954","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: 45