2018 IEEE International Conference on Industrial Internet (ICII)最新文献_第4页

A Modular and Adaptive Architecture for Building Applications with Connected Devices 基于连接设备构建应用的模块化和自适应架构

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00009

P. Pannuto, Wenpeng Wang, P. Dutta, Bradford Campbell

{"title":"A Modular and Adaptive Architecture for Building Applications with Connected Devices","authors":"P. Pannuto, Wenpeng Wang, P. Dutta, Bradford Campbell","doi":"10.1109/ICII.2018.00009","DOIUrl":"https://doi.org/10.1109/ICII.2018.00009","url":null,"abstract":"Smart and connected devices offer enormous potential to enable context-aware, localized, and multi-device orchestrations that could substantially increase the reach and utility of computing. The growth of these applications has been hampered, however, as devices, their data, and their control have been largely sequestered to their own vendor-specific APIs, clouds, and applications-a largely stove-piped state of affairs. Where barriers between devices have been pierced, the connections often occur between vendor clouds, affecting the latency, privacy, and reliability of the original application, while simultaneously increasing complexity. Locally executing applications have not materialized as devices with incompatible communication protocols, inconsistent APIs, and incongruent data models rarely communicate. We claim that what is needed to unlock the application potential is an architecture tailored to facilitating applications composed of networked devices. Our proposed architecture addresses this by providing a port-based abstraction for devices using a small wrapper layer. This device abstraction provides a consistent view of devices, and embeddable runtimes provide existing applications straightforward access to devices. The architecture also supports device discovery, shared interfaces between devices, and an application specification interface that promotes creating device-agnostic applications capable of operating even when devices change. We demonstrate the efficacy of our architecture with two application case studies that highlight the abstraction layers between applications and devices and employ the embeddability of our system to add new functionality to existing systems.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"10 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":"126122681","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: 4

[Title page i] [标题页i]

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/icii.2018.00001

引用次数: 0

Brightics-IoT: Key Attractive Features of Enterprise Targeted IoT Platform 物联网亮点:面向企业的物联网平台的主要吸引力

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00032

Hyokeun Choi

引用次数: 3

Indoor Multi-Sensory Self-Supervised Autonomous Mobile Robotic Navigation 室内多感官自监督自主移动机器人导航

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00021

Juhong Xu, Hanqing Guo, Shaoen Wu

{"title":"Indoor Multi-Sensory Self-Supervised Autonomous Mobile Robotic Navigation","authors":"Juhong Xu, Hanqing Guo, Shaoen Wu","doi":"10.1109/ICII.2018.00021","DOIUrl":"https://doi.org/10.1109/ICII.2018.00021","url":null,"abstract":"Autonomous robotic navigation in indoor environments is fairly challenging and important to industrial environments. Traditional map-based or mapless navigation methods often fail because of the unstructured characteristics of the environments. Recently, imitation learning using DAgger algorithm has been successfully applied to many real-world robotic tasks. However, it needs human operators to give correct control commands without feedback to overcome data distribution mismatch problem, which is always prone to error and expensive. In this paper, we propose a novel solution to eliminate the need of human manual labeling after the initial data collection in the task of imitating to navigate in indoor environments. This solution introduces an imperfect policy based on multi-sensor fusion and a recording policy that only records the data giving the most knowledge to the navigation policy. The recording policy mitigates the affect of learning too much from an imperfect policy. With extensive experiments in indoor environments, we demonstrate that after several iterations of learning, the robot is able to navigate through real-world hallways in both seen and unseen situations safely. In addition, we show that our system achieves near human performance in most of the tasks and even surpasses human performance in one out of three tasks. To the best of our knowledge, this is the first work that utilizes imperfect sensor measurements to perform self-supervised imitation learning in robotic navigation tasks.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"11 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":"114176203","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: 7

Towards Automation of Vulnerability and Exploitation Identification in IIoT Networks 迈向工业物联网网络中漏洞和利用识别的自动化

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-10-01 DOI: 10.1109/ICII.2018.00023

Nour Moustafa, B. Turnbull, Kim-Kwang Raymond Choo

{"title":"Towards Automation of Vulnerability and Exploitation Identification in IIoT Networks","authors":"Nour Moustafa, B. Turnbull, Kim-Kwang Raymond Choo","doi":"10.1109/ICII.2018.00023","DOIUrl":"https://doi.org/10.1109/ICII.2018.00023","url":null,"abstract":"Since Industrial Internet of Things (IIoT) networks are comprised of heterogeneous manufacturing and technological devices and services, discovering previously unknown vulnerabilities and their exploitation vectors (also known as Penetration Testing - PT) is an arduous and risk-prone process. PT across IIoT networks requires system administrators to attempt multiple and often bespoke commercial tools for testing vulnerable network nodes, platforms, and software. In this paper, we propose a new testbed IIoT environment involving multiple vulnerable platforms connected to IIoT sensors and IoT gateways for designing automated vulnerability and exploitation identification techniques based on analyzing network flows. We utilize a particle filter technique for estimating the vulnerability and exploitation behaviors in a term of posterior probabilities. The proposed model is better than using traditional artificial planning algorithms that consume significant computational resources and demand termination criteria. The proposed testbed IIoT environment can be shared with other like-minded researchers to facilitate future evaluations.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"23 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":"122545537","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: 13

Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening 工业物联网协同安全:集冗余、多样性、强化于一体

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-08-28 DOI: 10.1109/ICII.2018.00025

Aron Laszka, W. Abbas, Yevgeniy Vorobeychik, X. Koutsoukos

{"title":"Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening","authors":"Aron Laszka, W. Abbas, Yevgeniy Vorobeychik, X. Koutsoukos","doi":"10.1109/ICII.2018.00025","DOIUrl":"https://doi.org/10.1109/ICII.2018.00025","url":null,"abstract":"As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125187307","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: 20

RT-DAP: A Real-Time Data Analytics Platform for Large-Scale Industrial Process Monitoring and Control RT-DAP:大规模工业过程监测和控制的实时数据分析平台

2018 IEEE International Conference on Industrial Internet (ICII) Pub Date : 2018-02-22 DOI: 10.1109/ICII.2018.00015

Song Han, Tao Gong, M. Nixon, Eric Rotvold, K. Lam, K. Ramamritham

{"title":"RT-DAP: A Real-Time Data Analytics Platform for Large-Scale Industrial Process Monitoring and Control","authors":"Song Han, Tao Gong, M. Nixon, Eric Rotvold, K. Lam, K. Ramamritham","doi":"10.1109/ICII.2018.00015","DOIUrl":"https://doi.org/10.1109/ICII.2018.00015","url":null,"abstract":"In most process control systems nowadays, process measurements are periodically collected and archived in historians. Analytics applications process the data, and provide results offline or in a time period that is considerably slow in comparison to the performance of many manufacturing processes. Along with the proliferation of Internet-of-Things (IoT) and the introduction of \"pervasive sensors\" technology in process industries, increasing number of sensors and actuators are installed in process plants for pervasive sensing and control, and the volume of produced process data is growing exponentially. To digest these data and meet the ever-growing requirements to increase production efficiency and improve product quality, there needs a way to both improve the performance of the analytic system and scale the system to closely monitor a much larger set of plant resources. In this paper, we present a real-time data analytics platform, referred to as RT-DAP, to support large-scale continuous data analytics in process industries. RT-DAP is designed to be able to stream, store, process and visualize a large volume of real-time data flows collected from heterogeneous plant resources, and feedback to the control system and operators in a real-time manner. A prototype of the platform is implemented on Microsoft Azure. Our extensive experiments validate the design methodologies of RT-DAP and demonstrate its efficiency in both component and system levels.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":" 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120829889","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: 6