2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)最新文献_第2页

Message from the IoTDI 2022 General Chair IoTDI 2022总主席致辞

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00005

引用次数: 0

To Share or Not to Share: On Location Privacy in IoT Sensor Data 分享还是不分享:关于物联网传感器数据中的位置隐私

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00015

F. Papst, Naomi Stricker, R. Entezari, O. Saukh

{"title":"To Share or Not to Share: On Location Privacy in IoT Sensor Data","authors":"F. Papst, Naomi Stricker, R. Entezari, O. Saukh","doi":"10.1109/iotdi54339.2022.00015","DOIUrl":"https://doi.org/10.1109/iotdi54339.2022.00015","url":null,"abstract":"Data sharing is crucial for building large datasets which in return are essential for developing and training accurate models in many contexts including smart cities, agriculture, and medical applications. However, shared data may leak private information, such as personal identifiers or location. Past research provides evidence that solely removing these identifiers through pseudonymization is not enough to ensure data privacy protection, since even the pseudonymized data may still contain information about the data provider. In this paper, we show that sensor data may leak a sensor's location even if the latter is not explicitly shared. Sensors are localized by linking sensor data with publicly available environmental data such as local weather. The proposed localization method relies on a machine learning model to predict weather data from sensor observations. Subsequently, the localization algorithm determines the sensor's location from the predicted weather trace using Bayesian filtering. We apply our approach to three real-world datasets where we (1) localize an ozone sensor given its readings, (2) localize a cow from activity parameters recorded with a tracker in the cow's reticulum, (3) localize solar panels based on their solar generation data. The achieved average localization accuracy of 5.68 km, 19.91 km, and 13.68 km on the above tasks, respectively, using data traces with a length of 365 days is remarkable. In addition, we introduce a mechanism, referred to as teleport, to protect location information in sensor data. The mechanism is based on deep models and masks the location by replacing the weather dependency with a different weather signature.","PeriodicalId":314074,"journal":{"name":"2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122970440","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

Demonstrating Virtual IO For Internet Of Things Devices Secured By TLS Server In Secure Element 在安全单元中演示由TLS服务器保护的物联网设备的虚拟IO

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00025

P. Urien

引用次数: 1

Let's Grab a Drink: Teacher-Student Learning for Fluid Intake Monitoring using Smart Earphones 让我们喝一杯:使用智能耳机进行液体摄入监测的师生学习

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00014

Shijia Zhang, Yilin Liu, Mahanth K. Gowda

{"title":"Let's Grab a Drink: Teacher-Student Learning for Fluid Intake Monitoring using Smart Earphones","authors":"Shijia Zhang, Yilin Liu, Mahanth K. Gowda","doi":"10.1109/iotdi54339.2022.00014","DOIUrl":"https://doi.org/10.1109/iotdi54339.2022.00014","url":null,"abstract":"This paper shows the feasibility of fluid intake estimation using earphone sensors, which are gaining in popularity. Fluid consumption estimation has a number of healthcare-related applications in tracking dehydration and overhydration which can be connected to issues in fatigue, irritability, high blood pressure, kidney stones, etc. Therefore, accurate tracking of hydration levels not only has direct benefits to users in preventing such disorders but also offers diagnostic information to healthcare providers. Towards this end, this paper employs a voice pickup microphone that captures body vibrations during fluid consumption directly from skin contact and body conduction. This results in the extraction of stronger signals while being immune to ambient environmental noise. However, the main challenge for accurate estimation is the lack of availability of large-scale training datasets to train machine learning models (ML). To address the challenge, this paper designs robust ML models based on techniques in data augmentation and semi-supervised learning. Extensive user study with 12 users shows a per-swallow volume estimation accuracy of 3.35 mL (≈ 19.17% error) and a cumulative error of 3.26% over an entire bottle, while being robust to body motion, container type, liquid temperature, sensor position, etc. The ML models are implemented on smartphones with low power consumption and latency.","PeriodicalId":314074,"journal":{"name":"2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114610457","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

IoTDI 2022 Committee IoTDI 2022委员会

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00007

引用次数: 0

5G-Slicer: An emulator for mobile IoT applications deployed over 5G network slices 5G切片器:用于部署在5G网络切片上的移动物联网应用的模拟器

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00008

Moysis Symeonides, Demetris Trihinas, G. Pallis, M. Dikaiakos, C. Psomas, I. Krikidis

{"title":"5G-Slicer: An emulator for mobile IoT applications deployed over 5G network slices","authors":"Moysis Symeonides, Demetris Trihinas, G. Pallis, M. Dikaiakos, C. Psomas, I. Krikidis","doi":"10.1109/iotdi54339.2022.00008","DOIUrl":"https://doi.org/10.1109/iotdi54339.2022.00008","url":null,"abstract":"5G is emerging as a key mobile network technology offering Gbps transmission rates, lower communication latency, and support for 10-100x more connected devices. The full exploitation of 5G relies on network slicing, a network virtualization technique where operators split a physical network among a wide number and variety of services, in accordance to their individual needs. However, experimentation with 5G-enabled services and measurement of key performance indicators (KPIs) over network slices is extremely challenging as it requires the deployment and coordination of numerous physical devices, including edge and cloud resources. In this paper, we introduce 5G-Slicer; an open and extensible framework for modeling and rapid experimentation of 5G-enabled services via a scalable network slicing emulator. Through modeling abstractions, our solution eases the definition of 5G network slices, virtual and physical fog resources, and the mobility of involved entities. With the blueprint of an emulated testbed at hand, users can create reproducible experiments to evaluate application functionality and KPIs by injecting load, faults and even changing runtime configurations. To show the wide applicability of 5G-Slicer, we introduce a proof-of-concept use-case that encompasses different scenarios for capacity management in a city-scale intelligent transportation service. Evaluation results exploiting real 5G data show that 5G-slicer presents, at most, an 11.7% deviation when comparing actual and emulated network Quality of Service (QoS).","PeriodicalId":314074,"journal":{"name":"2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131266966","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

$pi$-Configurator: Enabling Efficient Configuration of Pipelined Applications on the Edge $pi$-Configurator:启用边缘上的流水线应用程序的有效配置

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00009

M. Rafiuzzaman, S. Gopalakrishnan, K. Pattabiraman

{"title":"$pi$-Configurator: Enabling Efficient Configuration of Pipelined Applications on the Edge","authors":"M. Rafiuzzaman, S. Gopalakrishnan, K. Pattabiraman","doi":"10.1109/iotdi54339.2022.00009","DOIUrl":"https://doi.org/10.1109/iotdi54339.2022.00009","url":null,"abstract":"Modern edge computing applications involve a computational pipeline of multiple stages. Each stage typically involves many configuration options that affect application-level quality of service. Identifying an optimal configuration is challenging but important when the applications run under resource constraints. The main challenge is that when pipelines have many stages and each stage has many settings, the overall configuration state space is exceedingly large. We propose $pi$-Configurator, a system for sampling application-level quality of service (QoS) metrics, constructing an approximation of the configuration state space, and finally identifying an optimal configuration for the application. We demonstrate the accuracy and effectiveness of $pi$ - Configurator with four multi-stage data processing applications on resource-limited edge computing platforms. $pi$-Configurator incurs low approximation error, and is one to two orders of magnitude faster than complete sampling approaches. The configurations identified by $pi$-Configurator outperform those identified by existing local adaptation approaches by 99%.","PeriodicalId":314074,"journal":{"name":"2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115354226","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

Demo Abstract: PrivacyCube: A Tangible Device for Improving Privacy Awareness in IoT 摘要:PrivacyCube:提高物联网中隐私意识的有形设备

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-05-01 DOI: 10.1109/iotdi54339.2022.00024

Bayan Al Muhander, O. Rana, N. Arachchilage, Charith Perera

引用次数: 3

IOTA: A Framework for Analyzing System-Level Security of IoTs IOTA:分析物联网系统级安全性的框架

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2022-02-05 DOI: 10.1109/iotdi54339.2022.00017

Zheng Fang, Hao Fu, Tianbo Gu, Pengfei Hu, Jinyue Song, T. Jaeger, P. Mohapatra

{"title":"IOTA: A Framework for Analyzing System-Level Security of IoTs","authors":"Zheng Fang, Hao Fu, Tianbo Gu, Pengfei Hu, Jinyue Song, T. Jaeger, P. Mohapatra","doi":"10.1109/iotdi54339.2022.00017","DOIUrl":"https://doi.org/10.1109/iotdi54339.2022.00017","url":null,"abstract":"Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose Iota, a logic programming-based framework to perform system-level security analysis for IoT systems. Iota generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building Iota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. Iota automatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose two metrics based on the attack graph, which provide guidance on fortifying IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that IOTA's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply Iota to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.","PeriodicalId":314074,"journal":{"name":"2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128525909","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

Multimodal Federated Learning on IoT Data 物联网数据的多模态联邦学习

2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI) Pub Date : 2021-09-10 DOI: 10.1109/iotdi54339.2022.00011

Yuchen Zhao, P. Barnaghi, H. Haddadi

{"title":"Multimodal Federated Learning on IoT Data","authors":"Yuchen Zhao, P. Barnaghi, H. Haddadi","doi":"10.1109/iotdi54339.2022.00011","DOIUrl":"https://doi.org/10.1109/iotdi54339.2022.00011","url":null,"abstract":"Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. In many applications, such as smart homes with Internet-of-Things (IoT) devices, local data on clients are generated from different modalities such as sensory, visual, and audio data. Existing federated learning systems only work on local data from a single modality, which limits the scalability of the systems. In this paper, we propose a multimodal and semi-supervised federated learning framework that trains autoencoders to extract shared or correlated representations from different local data modalities on clients. In addition, we propose a multimodal FedAvg algorithm to aggregate local autoencoders trained on different data modalities. We use the learned global autoencoder for a downstream classification task with the help of auxiliary labelled data on the server. We empirically evaluate our framework on different modalities including sensory data, depth camera videos, and RGB camera videos. Our experimental results demonstrate that introducing data from multiple modalities into federated learning can improve its classification performance. In addition, we can use labelled data from only one modality for supervised learning on the server and apply the learned model to testing data from other modalities to achieve decent $F_{1}$ scores (e.g., with the best performance being higher than 60%), especially when combining contributions from both unimodal clients and multimodal clients.","PeriodicalId":314074,"journal":{"name":"2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124261394","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