Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation最新文献_第6页

adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential Decision Making Human-in-the-Loop Systems 用于顺序决策的人在环系统的自适应隐私感知强化学习

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-03-07 DOI: 10.1145/3576842.3582325

Mojtaba Taherisadr, S. Stavroulakis, Salma Elmalaki

{"title":"adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential Decision Making Human-in-the-Loop Systems","authors":"Mojtaba Taherisadr, S. Stavroulakis, Salma Elmalaki","doi":"10.1145/3576842.3582325","DOIUrl":"https://doi.org/10.1145/3576842.3582325","url":null,"abstract":"Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy-sensitive data in IoT devices, especially for human-in-the-loop systems. On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans. On the other hand, trained policies can leak the user’s private information. Recent attention has been drawn to designing privacy-aware RL algorithms while maintaining an acceptable system utility. A central challenge in designing privacy-aware RL, especially for human-in-the-loop systems, is that humans have intrinsic variability, and their preferences and behavior evolve. The effect of one privacy leak mitigation can differ for the same human or across different humans over time. Hence, we can not design one fixed model for privacy-aware RL that fits all. To that end, we propose adaPARL, an adaptive approach for privacy-aware RL, especially for human-in-the-loop IoT systems. adaPARL provides a personalized privacy-utility trade-off depending on human behavior and preference. We validate the proposed adaPARL on two IoT applications, namely (i) Human-in-the-Loop Smart Home and (ii) Human-in-the-Loop Virtual Reality (VR) Smart Classroom. Results obtained on these two applications validate the generality of adaPARL and its ability to provide a personalized privacy-utility trade-off. On average, adaPARL improves the utility by while reducing the privacy leak by on average.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133914642","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

VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild 基于IMU传感器的野外人类活动识别的不变特征学习

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-03-03 DOI: 10.1145/3576842.3582390

Yujiao Hao, Boyu Wang, Rong Zheng

{"title":"VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild","authors":"Yujiao Hao, Boyu Wang, Rong Zheng","doi":"10.1145/3576842.3582390","DOIUrl":"https://doi.org/10.1145/3576842.3582390","url":null,"abstract":"Deep neural network models for IMU sensor-based human activity recognition (HAR) that are trained from controlled, well-curated datasets suffer from poor generalizability in practical deployments. However, data collected from naturalistic settings often contains significant label noise. In this work, we examine two in-the-wild HAR datasets and DivideMix, a state-of-the-art learning with noise labels (LNL) method to understand the extent and impacts of noisy labels in training data. Our empirical analysis reveals that the substantial domain gaps among diverse subjects cause LNL methods to violate a key underlying assumption, namely, neural networks tend to fit simpler (and thus clean) data in early training epochs. Motivated by the insights, we design VALERIAN, an invariant feature learning method for in-the-wild wearable sensor-based HAR. By training a multi-task model with separate task-specific layers for each subject, VALERIAN allows noisy labels to be dealt with individually while benefiting from shared feature representation across subjects. We evaluated VALERIAN on four datasets, two collected in a controlled environment and two in the wild. Experimental results show that VALERIAN significantly outperforms baseline approaches. VALERIAN can correct 75% – 93% of label errors in the source domains. When only 10-second clean labeled data per class is available from a new target subject, even with 40% label noise in training data, it achieves test accuracy. Code is available at: https://github.com/YujiaoHao/VALERIAN.git","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121121702","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

Fairguard: Harness Logic-based Fairness Rules in Smart Cities Fairguard:在智慧城市中利用基于逻辑的公平规则

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-02-22 DOI: 10.1145/3576842.3582371

Yiqi Zhao, Ziyan An, Xuqing Gao, Ayan Mukhopadhyay, Meiyi Ma

{"title":"Fairguard: Harness Logic-based Fairness Rules in Smart Cities","authors":"Yiqi Zhao, Ziyan An, Xuqing Gao, Ayan Mukhopadhyay, Meiyi Ma","doi":"10.1145/3576842.3582371","DOIUrl":"https://doi.org/10.1145/3576842.3582371","url":null,"abstract":"Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair prediction results. In this work, we first demonstrate that bias persists at a micro-level both temporally and spatially by studying real city data from Chattanooga, TN. To alleviate the issue of such bias, we introduce Fairguard, a micro-level temporal logic-based approach for fair smart city policy adjustment and generation in complex temporal-spatial domains. The Fairguard framework consists of two phases: first, we develop a static generator that is able to reduce data bias based on temporal logic conditions by minimizing correlations between selected attributes. Then, to ensure fairness in predictive algorithms, we design a dynamic component to regulate prediction results and generate future fair predictions by harnessing logic rules. Evaluations show that logic-enabled static Fairguard can effectively reduce biased correlations while dynamic Fairguard can guarantee fairness on protected groups at run-time with minimal impact on overall performance.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132475718","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

Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting 因为每个传感器都是唯一的，所以每对传感器也是唯一的:处理交通预测中的动态性

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-02-20 DOI: 10.1145/3576842.3582362

Arian Prabowo, Wei Shao, Hao Xue, Piotr Koniusz, Flora D. Salim

{"title":"Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting","authors":"Arian Prabowo, Wei Shao, Hao Xue, Piotr Koniusz, Flora D. Salim","doi":"10.1145/3576842.3582362","DOIUrl":"https://doi.org/10.1145/3576842.3582362","url":null,"abstract":"Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the dynamics at each sensor are unique. For example, the afternoon peaks at sensors near schools are more likely to occur earlier than those near residential areas. In this paper, we first analyze real-world traffic data to show that each sensor has a unique dynamic. Further analysis also shows that each pair of sensors also has a unique dynamic. Then, we explore how node embedding learns the unique dynamics at every sensor location. Next, we propose a novel module called Spatial Graph Transformers (SGT) where we use node embedding to leverage the self-attention mechanism to ensure that the information flow between two sensors is adaptive with respect to the unique dynamic of each pair. Finally, we present Graph Self-attention WaveNet (G-SWaN) to address the complex, non-linear spatiotemporal traffic dynamics. Through empirical experiments on four real-world, open datasets, we show that the proposed method achieves superior performance on both traffic speed and flow forecasting. Code is available at: https://github.com/aprbw/G-SWaN","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114897715","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

E3Pose: Energy-Efficient Edge-assisted Multi-camera System for Multi-human 3D Pose Estimation E3Pose:用于多人三维姿态估计的节能边缘辅助多相机系统

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-01-21 DOI: 10.1145/3576842.3582370

Letian Zhang, Jie Xu

{"title":"E3Pose: Energy-Efficient Edge-assisted Multi-camera System for Multi-human 3D Pose Estimation","authors":"Letian Zhang, Jie Xu","doi":"10.1145/3576842.3582370","DOIUrl":"https://doi.org/10.1145/3576842.3582370","url":null,"abstract":"Multi-human 3D pose estimation plays a key role in establishing a seamless connection between the real world and the virtual world. Recent efforts adopted a two-stage framework that first builds 2D pose estimations in multiple camera views from different perspectives and then synthesizes them into 3D poses. However, the focus has largely been on developing new computer vision algorithms on the offline video datasets without much consideration on the energy constraints in real-world systems with flexibly-deployed and battery-powered cameras. In this paper, we propose an energy-efficient edge-assisted multiple-camera system, dubbed E3Pose, for real-time multi-human 3D pose estimation, based on the key idea of adaptive camera selection. Instead of always employing all available cameras to perform 2D pose estimations as in the existing works, E3Pose selects only a subset of cameras depending on their camera view qualities in terms of occlusion and energy states in an adaptive manner, thereby reducing the energy consumption (which translates to extended battery lifetime) and improving the estimation accuracy. To achieve this goal, E3Pose incorporates an attention-based LSTM to predict the occlusion information of each camera view and guide camera selection before cameras are selected to process the images of a scene, and runs a camera selection algorithm based on the Lyapunov optimization framework to make long-term adaptive selection decisions. We build a prototype of E3Pose on a 5-camera testbed, demonstrate its feasibility and evaluate its performance. Our results show that a significant energy saving (up to 31.21%) can be achieved while maintaining a high 3D pose estimation accuracy comparable to state-of-the-art methods.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124702551","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

Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks Async-HFL:分层物联网网络中高效鲁棒的异步联邦学习

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-01-17 DOI: 10.1145/3576842.3582377

Xiaofan Yu, L. Cherkasova, Hars Vardhan, Quanling Zhao, Emily Ekaireb, Xiyuan Zhang, A. Mazumdar, T. Rosing

{"title":"Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks","authors":"Xiaofan Yu, L. Cherkasova, Hars Vardhan, Quanling Zhao, Emily Ekaireb, Xiyuan Zhang, A. Mazumdar, T. Rosing","doi":"10.1145/3576842.3582377","DOIUrl":"https://doi.org/10.1145/3576842.3582377","url":null,"abstract":"Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies. Although existing works have proposed various approaches to account data heterogeneity, system heterogeneity, unexpected stragglers and scalibility, none of them provides a systematic solution to address all of the challenges in a hierarchical and unreliable IoT network. In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture. In response to the largely varied networking and system processing delays, Async-HFL employs asynchronous aggregations at both the gateway and cloud levels thus avoids long waiting time. To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level. Device selection module chooses diverse and fast edge devices to trigger local training in real-time while device-gateway association module determines the efficient network topology periodically after several cloud epochs, with both modules satisfying bandwidth limitations. We evaluate Async-HFL’s convergence speedup using large-scale simulations based on ns-3 and a network topology from NYCMesh. Our results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and saves up to 21.6% total communication cost compared to state-of-the-art asynchronous FL algorithms (with client selection). We further validate Async-HFL on a physical deployment and observe its robust convergence under unexpected stragglers.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128557171","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

FedRule: Federated Rule Recommendation System with Graph Neural Networks 基于图神经网络的联邦规则推荐系统

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2022-11-13 DOI: 10.1145/3576842.3582328

Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen, Carlee Joe-Wong, Tianqiang Liu

{"title":"FedRule: Federated Rule Recommendation System with Graph Neural Networks","authors":"Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen, Carlee Joe-Wong, Tianqiang Liu","doi":"10.1145/3576842.3582328","DOIUrl":"https://doi.org/10.1145/3576842.3582328","url":null,"abstract":"Much of the value that IoT (Internet-of-Things) devices bring to “smart” homes lies in their ability to automatically trigger other devices’ actions: for example, a smart camera triggering a smart lock to unlock a door. Manually setting up these rules for smart devices or applications, however, is time-consuming and inefficient. Rule recommendation systems can automatically suggest rules for users by learning which rules are popular based on those previously deployed (e.g., in others’ smart homes). Conventional recommendation formulations require a central server to record the rules used in many users’ homes, which compromises their privacy and leaves them vulnerable to attacks on the central server’s database of rules. Moreover, these solutions typically leverage generic user-item matrix methods that do not fully exploit the structure of the rule recommendation problem. In this paper, we propose a new rule recommendation system, dubbed as FedRule, to address these challenges. One graph is constructed per user upon the rules s/he is using, and the rule recommendation is formulated as a link prediction task in these graphs. This formulation enables us to design a federated training algorithm that is able to keep users’ data private. Extensive experiments corroborate our claims by demonstrating that FedRule has comparable performance as the centralized setting and outperforms conventional solutions.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116576943","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

REACT: Streaming Video Analytics On The Edge With Asynchronous Cloud Support REACT:支持异步云的边缘流媒体视频分析

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2022-10-04 DOI: 10.1145/3576842.3582385

Anurag Ghosh, Srinivasan Iyengar, Stephen Lee, Anuj Rathore, V. Padmanabhan

{"title":"REACT: Streaming Video Analytics On The Edge With Asynchronous Cloud Support","authors":"Anurag Ghosh, Srinivasan Iyengar, Stephen Lee, Anuj Rathore, V. Padmanabhan","doi":"10.1145/3576842.3582385","DOIUrl":"https://doi.org/10.1145/3576842.3582385","url":null,"abstract":"Emerging Internet of Things (IoT) and mobile computing applications are expected to support latency-sensitive deep neural network (DNN) workloads. To realize this vision, the Internet is evolving towards an edge-computing architecture, where computing infrastructure is located closer to the end device to help achieve low latency. However, edge computing may have limited resources compared to cloud environments and thus, cannot run large DNN models that often have high accuracy. In this work, we develop REACT, a framework that leverages cloud resources to execute large DNN models with higher accuracy to improve the accuracy of models running on edge devices. To do so, we propose a novel edge-cloud fusion algorithm that fuses edge and cloud predictions, achieving low latency and high accuracy. We extensively evaluate our approach and show that our approach can significantly improve the accuracy compared to baseline approaches. We focus specifically on object detection in videos (applicable in many video analytics scenarios) and show that the fused edge-cloud predictions can outperform the accuracy of edge-only and cloud-only scenarios by as much as 50%. REACT shows that for Edge AI, the choice between offloading and on-device inference is not binary — redundant execution at cloud and edge locations complement each other when carefully employed.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114605197","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

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation 第八届ACM/IEEE物联网设计与实现会议论文集

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 1900-01-01 DOI: 10.1145/3576842

引用次数: 0