Proceedings of the 9th International Conference on the Internet of Things最新文献_第3页

A Novel Semantic Complex Event Processing Framework for Streaming Processing 一种面向流处理的语义复杂事件处理框架

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365906

Rose Yemson, Savas Konur, D. Thakker

引用次数: 3

Using BPM Technology to Deploy and Manage Distributed Analytics in Collaborative IoT-Driven Business Scenarios 使用BPM技术在协作物联网驱动的业务场景中部署和管理分布式分析

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365890

T. D'Hondt, A. Wilbik, P. Grefen, Heiko Ludwig, N. Baracaldo, Ali Anwar

{"title":"Using BPM Technology to Deploy and Manage Distributed Analytics in Collaborative IoT-Driven Business Scenarios","authors":"T. D'Hondt, A. Wilbik, P. Grefen, Heiko Ludwig, N. Baracaldo, Ali Anwar","doi":"10.1145/3365871.3365890","DOIUrl":"https://doi.org/10.1145/3365871.3365890","url":null,"abstract":"Increasing competition forces business organizations to improve the efficiency of their operational business processes, certainly where costly physical resources are involved. By integrating real-time, IoT-based information from these resources into business processes, advanced real-time decision making can be realized to enable the required efficiency increase. There are various challenges though. Firstly, the resources can be large in number, heterogeneous in nature and owned by different business parties. Secondly, the data is typically heterogeneous in format and large in volume. Thirdly, business scenarios are diverse and evolve over time. Consequently, converting IoT data into usable information to drive business processes is not a trivial task. To address this, we propose the use of a novel combination of existing technologies in distributed analytics (DA) and business process management (BPM). To deal with the size, heterogeneity and ownership of data, we don't bring the data to the analytics, but bring the analytics in a distributed format to the data. We use parameterized micro-services that are packed into software containers to make them dynamically deployable from a service repository into the IoT edge. To deal with the number of IoT resources and the diversity of scenarios, we automate the deployment and management processes of the containerized microservices using a BPM engine. This engine interprets graphically specified process models that define the data flow between the DA modules and business decision making. Our approach leaves large amounts of raw data at its origin and is highly flexible in its data processing scheme. We show the feasibility of our approach in a proof-of-concept prototype implementation.","PeriodicalId":350460,"journal":{"name":"Proceedings of the 9th International Conference on the Internet of Things","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114170629","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

Towards Identification of Packaged Products via Computer Vision: Convolutional Neural Networks for Object Detection and Image Classification in Retail Environments 面向包装产品的计算机视觉识别:零售环境中用于目标检测和图像分类的卷积神经网络

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365899

K. Fuchs, T. Grundmann, E. Fleisch

{"title":"Towards Identification of Packaged Products via Computer Vision: Convolutional Neural Networks for Object Detection and Image Classification in Retail Environments","authors":"K. Fuchs, T. Grundmann, E. Fleisch","doi":"10.1145/3365871.3365899","DOIUrl":"https://doi.org/10.1145/3365871.3365899","url":null,"abstract":"Identification of packaged products in retail environments still relies on barcodes, requiring active user input and limited to one product at a time. Computer vision (CV) has already enabled many applications, but has so far been under-discussed in the retail domain, albeit allowing for faster, hands-free, more natural human-object interaction (e.g. via mixed reality headsets). To assess the potential of current convolutional neural network (CNN) architectures to reliably identify packaged products within a retail environment, we created and open-source a dataset of 300 images of vending machines with 15k labeled instances of 90 products. We assessed observed accuracies from transfer learning for image-based product classification (IC) and multi-product object detection (OD) on multiple CNN architectures, and the number of images instances required per product to achieve meaningful predictions. Results show that as little as six images are enough for 90% IC accuracy, but around 30 images are needed for 95% IC accuracy. For simultaneous OD, 42 instances per product are necessary and far more than 100 instances to produce robust results. Thus, this study demonstrates that even in realistic, fast-paced retail environments, image-based product identification provides an alternative to barcodes, especially for use-cases that do not require perfect 100% accuracy.","PeriodicalId":350460,"journal":{"name":"Proceedings of the 9th International Conference on the Internet of Things","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121696694","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: 26

Asset-Oriented Access Control: Towards a New IoT Framework 面向资产的访问控制:迈向新的物联网框架

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365905

T. Cattermole, Simon Docherty, D. Pym, M. Sasse

引用次数: 1

Achieving Accurate Room-Level Indoor Location Estimation with Emerging IoT Networks 利用新兴物联网网络实现精确的房间级室内位置估计

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365875

Jacob T. Biehl, Andreas Girgensohn, Mitesh Patel

引用次数: 6

Achieving Robust and Reliable Wireless Communications in Hostile In-Car Environments 在恶劣的车内环境中实现稳健可靠的无线通信

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365904

Daniel Kraus, P. Priller, K. Diwold, E. Leitgeb

引用次数: 3

Energy-Accuracy Tradeoff for Efficient Noise Monitoring and Prediction in Working Environments 工作环境中高效噪声监测与预测的能量-精度权衡

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365885

F. Kraemer, Faiga Alawad, Ida Marie Vestgøte Bosch

引用次数: 6

Retrofit 改造

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1007/1-4020-0613-6_16306

Richard Bjetak, K. Diwold, Amer Kajmakovic

引用次数: 0

Interactive Machine Learning for the Internet of Things: A Case Study on Activity Detection 物联网的交互式机器学习:活动检测的案例研究

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365881

Agnes Tegen, P. Davidsson, J. Persson

{"title":"Interactive Machine Learning for the Internet of Things: A Case Study on Activity Detection","authors":"Agnes Tegen, P. Davidsson, J. Persson","doi":"10.1145/3365871.3365881","DOIUrl":"https://doi.org/10.1145/3365871.3365881","url":null,"abstract":"The advances in Internet of Things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for Activity Recognition by using Machine Learning. However, as the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures, the feature space might also change over time. Moreover, the labelled data necessary for the training is often costly to acquire. Active Learning is a type of Interactive Machine Learning where the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data points to label. It is generally assumed that a query always gets a correct response, but in many real-world scenarios this is not a realistic assumption. In this work we investigate different Proactive Learning strategies, which explore the human factors of the oracle and aspects that might influence a user to provide or withhold labels. We implemented four proactive strategies and hybrid versions of them. They were evaluated on two datasets to examine how a more proactive, or reluctant, user affects performance. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.","PeriodicalId":350460,"journal":{"name":"Proceedings of the 9th International Conference on the Internet of Things","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128640510","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: 5

Non-interactive Certificate Update Protocol for Efficient Authentication in IoT 面向物联网高效认证的非交互式证书更新协议

Proceedings of the 9th International Conference on the Internet of Things Pub Date : 2019-10-22 DOI: 10.1145/3365871.3365879

Li Duan, Yong Li, L. Liao

引用次数: 3