Proceedings of the International Conference on Internet-of-Things Design and Implementation最新文献

{"title":"CurrentSense","authors":"Sumukh Marathe, A. Nambi, Manohar Swaminathan, R. Sutaria","doi":"10.1145/3450268.3453535","DOIUrl":"https://doi.org/10.1145/3450268.3453535","url":null,"abstract":"Sensor data quality plays a fundamental role in increasing the adoption of IoT devices for environmental data collection. Due to the nature of the deployment, i.e., in-the-wild and in harsh environments, coupled with limitations of low-cost components, sensors are prone to failures. A significant fraction of faults result from drift and catastrophic faults in sensors' sensing components leading to serious data inaccuracies. However, it is challenging to detect faults by analyzing just the sensor data as a faulty sensor data can mimic non-faulty data and an anomalous sensor reading need not represent a faulty data. Existing data-centric approaches rely on additional contextual information or sensor redundancy to detect such faults. This paper presents a systematic approach to detect faults and drifts, by devising a novel sensor fingerprint called CurrentSense. CurrentSense captures the electrical characteristics of the hardware components in a sensor, with working, drifted, and faulty sensors having distinct fingerprints. This fingerprint is used to determine the sensors' health, and compensate for drift or diagnose catastrophic faults without any contextual information. The CurrentSense approach is non-intrusive, and can be applied to a wide variety of environmental sensors. We show the working of the proposed approach with the help of air pollution sensors. We perform an extensive evaluation in both controlled setup and real-world deployments with 51 sensors across multiple cities for 8 months period. Our approach outperforms existing anomaly detectors and can detect and isolate faults with an F1 score of 98% and compensate for sensor drift errors by 86%.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121059631","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}

{"title":"DeepObfuscator","authors":"Ang Li, Jiayi Guo, Huanrui Yang, F. Salim, Yiran Chen","doi":"10.1145/3450268.3453519","DOIUrl":"https://doi.org/10.1145/3450268.3453519","url":null,"abstract":"Deep learning has been widely applied in many computer vision applications, with remarkable success. However, running deep learning models on mobile devices is generally challenging due to the limitation of computing resources. A popular alternative is to use cloud services to run deep learning models to process raw data. This, however, imposes privacy risks. Some prior arts proposed sending the features extracted from raw data (e.g., images) to the cloud. Unfortunately, these extracted features can still be exploited by attackers to recover raw images and to infer embedded private attributes (e.g., age, gender, etc.). In this paper, we propose an adversarial training framework, DeepObfuscator, which prevents the usage of the features for reconstruction of the raw images and inference of private attributes. This is done while retaining useful information for the intended cloud service (i.e., image classification). DeepObfuscator includes a learnable encoder, namely, obfuscator that is designed to hide privacy-related sensitive information from the features by performing our proposed adversarial training algorithm. The proposed algorithm is designed by simulating the game between an attacker who makes efforts to reconstruct raw image and infer private attributes from the extracted features and a defender who aims to protect user privacy. By deploying the trained obfuscator on the smartphone, features can be locally extracted and then sent to the cloud. Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0.9458 to 0.3175 in terms of multi-scale structural similarity (MS-SSIM). The person in the reconstructed image, hence, becomes hardly to be re-identified. The classification accuracy of the inferred private attributes that can be achieved by the attacker is significantly reduced to a random-guessing level, e.g., the accuracy of gender is reduced from 97.36% to 58.85%. As a comparison, the accuracy of the intended classification tasks performed via the cloud service is only reduced by 2%. We also demonstrate the efficiency of DeepObfuscator, showcasing real-time performance of the deployed models on smartphones.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122369238","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}

{"title":"MLIoT","authors":"Sudershan Boovaraghavan, Anurag Maravi, Prahaladha Mallela, Yuvraj Agarwal","doi":"10.1145/3450268.3453522","DOIUrl":"https://doi.org/10.1145/3450268.3453522","url":null,"abstract":"Modern Internet of Things (IoT) applications, from contextual sensing to voice assistants, rely on ML-based training and serving systems using pre-trained models to render predictions. However, real-world IoT environments are diverse, with rich IoT sensors and need ML models to be personalized for each setting using relatively less training data. Most existing general-purpose ML systems are optimized for specific and dedicated hardware resources and do not adapt to changing resources and different IoT application requirements. To address this gap, we propose MLIoT, an end-to-end Machine Learning System tailored towards supporting the entire lifecycle of IoT applications. MLIoT adapts to different IoT data sources, IoT tasks, and compute resources by automatically training, optimizing, and serving models based on expressive application-specific policies. MLIoT also adapts to changes in IoT environments or compute resources by enabling re-training, and updating models served on the fly while maintaining accuracy and performance. Our evaluation across a set of benchmarks show that MLIoT can handle multiple IoT tasks, each with individual requirements, in a scalable manner while maintaining high accuracy and performance. We compare MLIoT with two state-of-the-art hand-tuned systems and a commercial ML system showing that MLIoT improves accuracy from 50% - 75% while reducing or maintaining latency.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129002840","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}

{"title":"Sentinel","authors":"A. Cosson, A. Sikder, Leonardo Babun, Z. Berkay Celik, P. McDaniel, A. Uluagac","doi":"10.1145/3450268.3453533","DOIUrl":"https://doi.org/10.1145/3450268.3453533","url":null,"abstract":"The concept of Internet of Things (IoT) has changed the way we live by integrating commodity devices with cyberspace to automate our everyday tasks. Nowadays, IoT devices in the home environment are becoming ubiquitous with seamless connectivity and diverse application domains. Modern IoT devices have adopted a many-to-many connectivity model to enhance user experience and device functionalities compared to early IoT devices with standalone device setup and limited functionalities. However, the continuous connection between devices and cyberspace has introduced new cyber attacks targeting IoT devices and networks. Due to the resource-constrained nature of IoT devices as well as the opacity of the IoT framework, traditional intrusion detection systems cannot be applied here. In this paper, we introduce Sentinel, a novel intrusion detection system that uses kernel-level information to detect malicious attacks. Specifically, Sentinel collects low-level system information (CPU usage, RAM usage, total load, available swap, etc.) of each IoT device in a network and learns the pattern of device behavior to differentiate between benign and malicious events. We evaluated the efficacy and performance of Sentinel in different IoT platforms with multiple devices and settings. We also measured the performance of Sentinel against five types of real-life attacks. Our evaluation shows that Sentinel can detect different attacks to IoT devices and networks with high accuracy (over 95%) and secure the devices in different IoT platforms and configurations. Also, Sentinel achieves minimum overhead in power consumption, ensuring high compatibility in resource-constraint IoT devices.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121129537","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}

{"title":"EdgeML","authors":"Zhihe Zhao, Kai Wang, Neiwen Ling, Guoliang Xing","doi":"10.1145/3450268.3453520","DOIUrl":"https://doi.org/10.1145/3450268.3453520","url":null,"abstract":"In recent years, deep learning algorithms are increasingly adopted by a wide range of data-intensive and time-critical Internet of Things (IoT) applications. As a result, several new approaches, including model partition/offloading and progressive neural architecture, have been proposed to address the challenge of deploying the computation-intensive deep neural network (DNN) models on resource-constrained edge devices. However, the performance of existing approaches is highly affected by runtime dynamics. For example, offloading workload from edge to cloud suffers from communication delays and the efficiency of progressive neural architecture supporting early-exit DNN executions relies on input characteristics. In this paper, we introduce EdgeML, an AutoML framework that provides flexible and fine-grained DNN model execution control by combining workload offloading mechanism and dynamic progressive neural architecture. To achieve desirable latency-accuracy-energy system performance on edge platforms, EdgeML adopts reinforcement learning to automatically update model execution policy in response to runtime dynamics in real-time. We implement EdgeML for several widely used DNN models on the latest edge devices. Comparing to existing approaches, our experiments show that EdgeML achieves up to 8× performance improvement under dynamic environments.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115874458","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}