Proceedings of the International Conference on Internet-of-Things Design and Implementation最新文献_第3页

FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT 公平物联网:在个性化物联网中利用人类可变性的公平感知人在环强化学习

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-03-30 DOI: 10.1145/3450268.3453525

Salma Elmalaki

{"title":"FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT","authors":"Salma Elmalaki","doi":"10.1145/3450268.3453525","DOIUrl":"https://doi.org/10.1145/3450268.3453525","url":null,"abstract":"Thanks to the rapid growth in wearable technologies, monitoring complex human context becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing such personalized IoT applications arises from human variability. Such variability stems from the fact that different humans exhibit different behaviors when interacting with IoT applications (intra-human variability), the same human may change the behavior over time when interacting with the same IoT application (inter-human variability), and human behavior may be affected by the behaviors of other people in the same environment (multi-human variability). To that end, we propose FaiR-IoT, a general reinforcement learning-based framework for adaptive and fairness-aware human-in-the-loop IoT applications. In FaiR-IoT, three levels of reinforcement learning agents interact to continuously learn human preferences and maximize the system's performance and fairness while taking into account the intra-, inter-, and multi-human variability. We validate the proposed framework on two applications, namely (i) Human-in-the-Loop Automotive Advanced Driver Assistance Systems and (ii) Human-in-the-Loop Smart House. Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%-60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1.5 orders of magnitude.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121522723","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: 17

Black-Box IoT: Authentication and Distributed Storage of IoT Data from Constrained Sensors 黑盒物联网:来自受限传感器的物联网数据的认证和分布式存储

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-03-06 DOI: 10.1145/3450268.3453536

Panagiotis Chatzigiannis, Foteini Baldimtsi, C. Kolias, A. Stavrou

{"title":"Black-Box IoT: Authentication and Distributed Storage of IoT Data from Constrained Sensors","authors":"Panagiotis Chatzigiannis, Foteini Baldimtsi, C. Kolias, A. Stavrou","doi":"10.1145/3450268.3453536","DOIUrl":"https://doi.org/10.1145/3450268.3453536","url":null,"abstract":"We propose Black-Box IoT (BBox-IoT), a new ultra-lightweight black-box system for authenticating and storing IoT data. BBox-IoT is tailored for deployment on IoT devices (including low-Size Weight and Power sensors) which are extremely constrained in terms of computation, storage, and power. By utilizing core Blockchain principles, we ensure that the collected data is immutable and tamper-proof while preserving data provenance and non-repudiation. To realize BBox-IoT, we designed and implemented a novel chain-based hash signature scheme which only requires hashing operations and removes all synchronicity dependencies between signer and verifier. Our approach enables low-SWaP devices to authenticate removing reliance on clock synchronization. Our evaluation results show that BBox-IoT is practical in Industrial Internet of Things (IIoT) environments: even devices equipped with 16MHz microcontrollers and 2KB memory can broadcast their collected data without requiring heavy cryptographic operations or synchronicity assumptions. Finally, when compared to industry standard ECDSA, our approach is two and three orders of magnitude faster for signing and verification operations respectively. Thus, we are able to increase the total number of signing operations by more than 5000% for the same amount of power.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134350011","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

Optimal Distributed Bandwidth Allocation in NB-IoT Networks: Poster Abstract NB-IoT网络中最优分布式带宽分配:海报摘要

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-03-05 DOI: 10.1145/3450268.3453510

Hongde Wu, Zhengyong Chen, N. O’Connor, Mingming Liu

引用次数: 0

Rim Rim

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2018-03-02 DOI: 10.1002/9780470114735.hawley14079

Yitao Hu, Weiwu Pang, Xiaochen Liu, Rajrup Ghosh, Bongjun Ko, Wei-Han Lee, R. Govindan

引用次数: 0

SecDeep

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 1900-01-01 DOI: 10.1145/3450268.3453524

Renju Liu, L. Garcia, Zaoxing Liu, Botong Ou, M. Srivastava

{"title":"SecDeep","authors":"Renju Liu, L. Garcia, Zaoxing Liu, Botong Ou, M. Srivastava","doi":"10.1145/3450268.3453524","DOIUrl":"https://doi.org/10.1145/3450268.3453524","url":null,"abstract":"There is an increasing emphasis on securing deep learning (DL) inference pipelines for mobile and IoT applications with privacy-sensitive data. Prior works have shown that privacy-sensitive data can be secured throughout deep learning inferences on cloud-offloaded models through trusted execution environments such as Intel SGX. However, prior solutions do not address the fundamental challenges of securing the resource-intensive inference tasks on low-power, low-memory devices (e.g., mobile and IoT devices), while achieving high performance. To tackle these challenges, we propose SecDeep, a low-power DL inference framework demonstrating that both security and performance of deep learning inference on edge devices are well within our reach. Leveraging TEEs with limited resources, SecDeep guarantees full confidentiality for input and intermediate data, as well as the integrity of the deep learning model and framework. By enabling and securing neural accelerators, SecDeep is the first of its kind to provide trusted and performant DL model inferencing on IoT and mobile devices. We implement and validate SecDeep by interfacing the ARM NN DL framework with ARM TrustZone. Our evaluation shows that we can securely run inference tasks with 16× to 172× faster performance than no acceleration approaches by leveraging edge-available accelerators.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"20 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":"116632870","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

DeepSQA

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 1900-01-01 DOI: 10.1145/3450268.3453529

Tianwei Xing, L. Garcia, F. Cerutti, L. Kaplan, A. Preece, M. Srivastava

{"title":"DeepSQA","authors":"Tianwei Xing, L. Garcia, F. Cerutti, L. Kaplan, A. Preece, M. Srivastava","doi":"10.1145/3450268.3453529","DOIUrl":"https://doi.org/10.1145/3450268.3453529","url":null,"abstract":"The ubiquity of mobile, wearable, and IoT devices enhances humans with a network of environmental sensors. These devices capture raw, time-series measurements of scalar physical phenomena. To transform the data into human-digestible representations, deep learning methods have enabled high-level interpretations of the opaque raw sensory data. However, interfacing models with humans requires flexibility to support the vast database of human inquiries about sensor data. Deep learning models are usually trained to perform fixed tasks, limiting the inference outputs to a predefined set of high-level labels. To enable flexible inference, we introduce DeepSQA, a generalized Sensory Question Answering (SQA) framework that aims to enable natural language questions about raw sensory data in distributed and heterogeneous IoT networks. Given a sensory data context and a natural language question about the data, the task is to provide an accurate natural language answer. In addition to the DeepSQA, we create SQA-Gen, a software framework for generating SQA datasets using labeled source sensory data, and also generate OppQA with SQA-Gen for benchmarking different SQA models. We evaluate DeepSQA across several state-of-the-art QA models and lay the foundation and challenges for future SQA research. We further provide open-source implementations of the framework, the dataset generation tool, and access to the generated dataset, to help facilitate research on the SQA problem.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"146 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":"116908661","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

SmartParcels

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 1900-01-01 DOI: 10.1145/3450268.3453526

Tung-Chun Chang, Georgios Bouloukakis, C. Hsieh, Cheng-Hsin Hsu, N. Venkatasubramanian

{"title":"SmartParcels","authors":"Tung-Chun Chang, Georgios Bouloukakis, C. Hsieh, Cheng-Hsin Hsu, N. Venkatasubramanian","doi":"10.1145/3450268.3453526","DOIUrl":"https://doi.org/10.1145/3450268.3453526","url":null,"abstract":"The emergence of IoT-aided smart communities has created the need for a new set of urban planning tools. The extra design process includes instrumenting infrastructures (sensing, networking, and computing devices) in smartspaces to generate information units (from data analytics) to realize a range of required services. In this paper, we propose SmartParcels, a framework that generates a comprehensive and cost-effective plan for instrumenting designated regions of smart communities (often called parcels). SmartParcels embeds an approach to solve the cross-layer IoT planning problem (shown to be NP-hard) that must consider applications, information/data, infrastructure, and geophysical layout as interdependent layers in the overall design. We develop a suite of algorithms (optimal, partial optimal, heuristic) for the problem; urban planners can compose these techniques in a plug-and-play manner to achieve performance trade-offs (optimality, timeliness). SmartParcels can be utilized for clean-slate planning (from scratch) or for retrofit of communities with existing smart infrastructure. We evaluate Smart-Parcels in two real-world settings: National Tsing Hua University in Taiwan and Irvine in California, USA, for clean-slate and retrofit. The evaluation results reveal that SmartParcels can enable a 2X -7X improvement in cost/performance metrics as compared to the baseline algorithm in the clean-slate and retrofit cases.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"2 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":"130084889","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: 3

milliEye

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 1900-01-01 DOI: 10.1145/3450268.3453532

Xian Shuai, Yulin Shen, Yi Tang, Shuyao Shi, Luping Ji, Guoliang Xing

{"title":"milliEye","authors":"Xian Shuai, Yulin Shen, Yi Tang, Shuyao Shi, Luping Ji, Guoliang Xing","doi":"10.1145/3450268.3453532","DOIUrl":"https://doi.org/10.1145/3450268.3453532","url":null,"abstract":"A wide range of advanced deep learning algorithms have recently been proposed for image classification and object detection. However, the effectiveness of these methods can be significantly restricted in many real-world scenarios where the visibility or illumination is poor. Compared to RGB cameras, millimeter-wave (mmWave) radars are immune to the above environmental variability and can assist cameras under adverse conditions. To this end, we propose milliEye, a lightweight mmWave radar and camera fusion system for robust object detection on the edge platforms. milliEye has several key advantages over existing sensor fusion approaches. First, while milliEye fuses two sensing modalities in a learning-based fashion, it requires only a small amount of labeled image/radar data of a new scene as it can fully utilize large public image datasets for extensive training. This salient feature enables milliEye to adapt to highly complex real-world environments. Second, based on a novel architecture that decouples the image-based object detector from other modules, milliEye is compatible with different off-the-shelf image-based object detectors. As a result, it can take advantage of the rapid progress of object detection algorithms. Moreover, thanks to the highly compute-efficient fusion approach, milliEye is lightweight and thus suitable for edge-based real-time applications. To evaluate the performance of milliEye, we collect a new radar and camera fusion dataset for object detection, which contains both ordinary-light and low-light illumination conditions. The results show that milliEye can provide substantial performance boosts over state-of-the-art image-based object detectors, including Tiny YOLOv3 and SSD, especially in low-light scenes, while incurring low compute overhead on edge platforms.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"76 2 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":"121917514","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: 1

ObscureNet ObscureNet

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 1900-01-01 DOI: 10.1145/3450268.3453534

Omid Hajihassnai, Omid Ardakanian, Hamzeh Khazaei

{"title":"ObscureNet","authors":"Omid Hajihassnai, Omid Ardakanian, Hamzeh Khazaei","doi":"10.1145/3450268.3453534","DOIUrl":"https://doi.org/10.1145/3450268.3453534","url":null,"abstract":"In this paper, we introduce ObscureNet, an encoder-decoder architecture that effectively conceals private attributes associated with time series data generated by sensors in IoT devices, while preserving the information content of the original time series. Drawing on conditional generative models and adversarial information factorization, ObscureNet learns latent representations that are invariant to the user-specified private attributes. This allows for modifying the private attributes or generating them randomly before using the decoder to synthesize a new version of data. We present three approaches to alter private attributes at anonymization time, and show that non-deterministic approaches can prevent an adversary from re-identifying private attributes. We compare ObscureNet with the autoencoder-based anonymization methods proposed in the literature and other generative models in terms of the accuracy of sensitive and desired inferences. Our experiments on two human activity recognition datasets show that compared to the original data, the sensitive inference accuracy is reduced by 80.38% on average, while the desired inference accuracy is only reduced by 6.82%. Moreover, ObscureNet reduces the sensitive inference accuracy by an additional 13.48% on average compared to the best baseline method. We report the computation overhead of running ObscureNet on a Raspberry Pi, and corroborate that it can be used for real-time anonymization of sensor data.","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":"122642502","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: 12

SoftBLE SoftBLE

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 1900-01-01 DOI: 10.1145/3450268.3453527

Mehdi Jafarizadeh, Xingzhi Liu, Rong Zheng

引用次数: 0