Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation最新文献_第5页

Poster Abstract: Battery-free and Passive Wake-up Receiver for Underwater Communication 摘要:用于水下通信的无电池无源唤醒接收机

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3589178

Lukas Schulthess, Philipp Mayer, M. Magno

{"title":"Poster Abstract: Battery-free and Passive Wake-up Receiver for Underwater Communication","authors":"Lukas Schulthess, Philipp Mayer, M. Magno","doi":"10.1145/3576842.3589178","DOIUrl":"https://doi.org/10.1145/3576842.3589178","url":null,"abstract":"Underwater Wireless Networks (UWNs), together with Underwater Wireless Sensor Nodes (UWSNs), are key enablers for various underwater activities in the fields of research, surveillance, rescue, and even military usage. In order to fulfill their mission in remote areas and harsh environments, UWSNs must meet high standards. Since the deployment of such a device is very costly and labor extensive, a long lifetime, high reliability, as well as no maintenance, are highly desirable. However, to achieve this goal, the overall power consumption of a UWSN needs to be reduced. By combining energy harvesting capabilities with a power-efficient wake-up circuit, the constant power drain in idle state can be heavily reduced, and even prevented. This paper presents a maintenance free and energy-neutral wake-up receiver for underwater communication that targets the challenge of the energy limitations of underwater communication frontends with the potential to expand the usability of the Internet of Things (IoT) beyond the water surface. This is achieved by hybrid energy and information transmission combined with a passive wake-up receiver that entirely eliminates radio frontend idle consumption.","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":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130200487","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

Poster Abstract: A Human Body Power Transfer System to Enable Battery-Less Sensor Nodes 摘要:实现无电池传感器节点的人体能量传输系统

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3589167

Rabea Rogge, Lukas Schulthess, C. Vogt, M. Magno

引用次数: 0

Practical Crowdsourcing of Wearable IoT Data with Local Differential Privacy 具有局部差分隐私的可穿戴物联网数据的实用众包

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3582367

Thomas Marchioro, Andrei Kazlouski, E. Markatos

{"title":"Practical Crowdsourcing of Wearable IoT Data with Local Differential Privacy","authors":"Thomas Marchioro, Andrei Kazlouski, E. Markatos","doi":"10.1145/3576842.3582367","DOIUrl":"https://doi.org/10.1145/3576842.3582367","url":null,"abstract":"In this work, we present and evaluate a crowdsourcing platform to collect wearable IoT data with local differential privacy (LDP). LDP protects privacy by perturbing data with noise, which may hinder their utility in some cases. For this reason, most researchers are wary of adopting it in their studies. To address these concerns, we consider the impact of different privacy budget values on the real wearable IoT data (steps, calories, distance, etc.) from N = 71 Fitbit users. Our goal is to demonstrate that, even if the collected information is protected with LDP, it is possible for data analysts to extract statistically significant insights on the studied population. To this end, we evaluate the error for various metrics of interest, such as sample average and empirical distribution. Furthermore, we verify that, in most cases, statistical tests produce the same results regardless of whether LDP has been applied or not. Our findings suggest that LDP with a privacy budget between 4 and 8 maintains an acceptable error of and over agreement on t-tests. Finally, we show that such values of privacy budget, albeit providing loose theoretical guarantees, can effectively defend against re-identification attacks on wearable IoT data.","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":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117090371","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

Dělen: Enabling Flexible and Adaptive Model-serving for Multi-tenant Edge AI 为多租户边缘AI实现灵活和自适应的模型服务

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3582375

Qianlin Liang, Walid A. Hanafy, Noman Bashir, A. Ali-Eldin, David E. Irwin, P. Shenoy

{"title":"Dělen: Enabling Flexible and Adaptive Model-serving for Multi-tenant Edge AI","authors":"Qianlin Liang, Walid A. Hanafy, Noman Bashir, A. Ali-Eldin, David E. Irwin, P. Shenoy","doi":"10.1145/3576842.3582375","DOIUrl":"https://doi.org/10.1145/3576842.3582375","url":null,"abstract":"Model-serving systems expose machine learning (ML) models to applications programmatically via a high-level API. Cloud platforms use these systems to mask the complexities of optimally managing resources and servicing inference requests across multiple applications. Model serving at the edge is now also becoming increasingly important to support inference workloads with tight latency requirements. However, edge model serving differs substantially from cloud model serving in its latency, energy, and accuracy constraints: these systems must support multiple applications with widely different latency and accuracy requirements on embedded edge accelerators with limited computational and energy resources. To address the problem, this paper presents Dělen,1 a flexible and adaptive model-serving system for multi-tenant edge AI. Dělen exposes a high-level API that enables individual edge applications to specify a bound at runtime on the latency, accuracy, or energy of their inference requests. We efficiently implement Dělen using conditional execution in multi-exit deep neural networks (DNNs), which enables granular control over inference requests, and evaluate it on a resource-constrained Jetson Nano edge accelerator. We evaluate Dělen flexibility by implementing state-of-the-art adaptation policies using Dělen’s API, and evaluate its adaptability under different workload dynamics and goals when running single and multiple applications.","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":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126850321","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

A Blockchain-Based Privacy-Preserving Model for Consent and Transparency in Human-Centered Internet of Things 以人为中心的物联网中基于区块链的同意和透明度隐私保护模型

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3582379

Jorge Eduardo Rivadeneira, María B. Jiménez, R. Marculescu, A. Rodrigues, F. Boavida, J. Sá Silva

{"title":"A Blockchain-Based Privacy-Preserving Model for Consent and Transparency in Human-Centered Internet of Things","authors":"Jorge Eduardo Rivadeneira, María B. Jiménez, R. Marculescu, A. Rodrigues, F. Boavida, J. Sá Silva","doi":"10.1145/3576842.3582379","DOIUrl":"https://doi.org/10.1145/3576842.3582379","url":null,"abstract":"The inclusion of human-related aspects in the Internet of Things paradigm leads to the development of models and solutions that address several challenges of our society. The adoption of these novel approaches is expanding rapidly on the road to what is now termed Society 5.0. However, leaving aside all the potential benefits that come from the interaction with these novel systems, an increasing number of people are concerned with the amount of data these systems can collect and share with data requesters. Several legal frameworks call for the adoption of practices regarding data protection and pushing for data control by the data owners. Unfortunately, most human-centric IoT-based systems lack mechanisms for managing resources and data in the user domain. Moreover, these tasks are typically delegated to a central entity; this necessarily implies a relationship of trust and can lead to problems related to transparency. To cope with these issues in this paper we present a privacy-preserving model that leverages the intrinsic features of the blockchain technology for consent management and transparency in Human-Centered Internet of Things environments. To show the feasibility of our approach, the proposed model is implemented, deployed in a test environment, and assessed using realistic scenarios.","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":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128084454","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

LegoSENSE: An Open and Modular Sensing Platform for Rapidly-Deployable IoT Applications LegoSENSE:面向快速部署物联网应用的开放模块化传感平台

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3582369

Minghui Zhao, S. Xia, Jingping Nie, Kaiyuan Hou, Avik Dhupar, Xiaofan Jiang

{"title":"LegoSENSE: An Open and Modular Sensing Platform for Rapidly-Deployable IoT Applications","authors":"Minghui Zhao, S. Xia, Jingping Nie, Kaiyuan Hou, Avik Dhupar, Xiaofan Jiang","doi":"10.1145/3576842.3582369","DOIUrl":"https://doi.org/10.1145/3576842.3582369","url":null,"abstract":"Domain-specific sensor deployments are critical to enabling various IoT applications. Existing solutions for quickly deploying sensing systems require significant amount of work and time, even for experienced engineers. We propose LegoSENSE, a low-cost open-source and modular platform, built on top of the widely popular Raspberry Pi single-board computer, that makes it simple for anyone to rapidly set up and deploy a customized sensing solution for application specific IoT deployments. In addition, the ‘plug and play’ and ‘mix and match’ functionality of LegoSENSE makes the sensor modules reusable, and allows them to be mixed and matched to serve a variety of needs. We show, through a series of user studies, that LegoSENSE enables users without engineering background to deploy a wide range of applications up to 9 × faster than experienced engineers without the use of LegoSENSE. We open-source the hardware and software designs to foster an ever-evolving community, enabling IoT applications for enthusiasts, students, scientists, and researchers across various application domains with or without prior experiences with embedded platforms or coding.","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":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116227270","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

Detecting Mental Disorders with Wearables: A Large Cohort Study 用可穿戴设备检测精神障碍:一项大型队列研究

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3582389

R. Dai, T. Kannampallil, Seunghwan Kim, Vera Thornton, L. Bierut, Chenyang Lu

{"title":"Detecting Mental Disorders with Wearables: A Large Cohort Study","authors":"R. Dai, T. Kannampallil, Seunghwan Kim, Vera Thornton, L. Bierut, Chenyang Lu","doi":"10.1145/3576842.3582389","DOIUrl":"https://doi.org/10.1145/3576842.3582389","url":null,"abstract":"Depression and anxiety are among the most prevalent mental disorders, and they are usually interconnected. Although these mental disorders have drawn increasing attention due to their tremendous negative impacts on working ability and job performance, over 50% of patients are not recognized or adequately treated. Recent literature has shown the potential of using wearables for expediting the detection of mental health disorders, as physical activities are reported to be related to some mental health disorders. However, most prior studies on mental health with wearables were limited to small cohorts. The feasibility of detecting mental disorders in the community with a large and diverse population remains an open question. In this paper, we study the problem of detecting depression and anxiety disorders with commercial wearable activity trackers based on a public dataset including 8,996 participants and 1,247 diagnosed with mental disorders. The large cohort is highly diverse, spanning a wide spectrum of age, race, ethnicity, and education levels. While prior studies were usually limited to shallow machine learning models and feature engineering to accommodate the small sample sizes, we develop an end-to-end deep model combining a transformer encoder and convolutional neural network to directly learn from daily wearable features and detect mental disorders. WearNet achieves an area Under the Receiver Operating Characteristic curve (AUROC) of 0.717 (S.D. 0.009) and an AUPRC of 0.487 (S.D. 0.008) in detecting mental disorders while outperforming traditional and state-of-the-art machine learning models. This work demonstrates the feasibility and promise of using wearables to detect mental disorders in a large and diverse community.","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":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121848469","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

Poster Abstract: Automatic Deployment Right-Sizing Through Hyperparameter Optimization 摘要:通过超参数优化自动部署正确的大小

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-05-09 DOI: 10.1145/3576842.3589157

Aniruddha Rakshit, Jayson G. Boubin

引用次数: 0

Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras 基于端到端深度强化学习的PTZ相机自主控制

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-04-10 DOI: 10.1145/3576842.3582366

S. Sandha, Bharathan Balaji, L. Garcia, Mani Srivastava

{"title":"Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras","authors":"S. Sandha, Bharathan Balaji, L. Garcia, Mani Srivastava","doi":"10.1145/3576842.3582366","DOIUrl":"https://doi.org/10.1145/3576842.3582366","url":null,"abstract":"Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms. These approaches require manual labels and suffer from performance bottlenecks due to error propagation across the multi-stage flow of information. The large size of object detection neural networks also makes prior solutions infeasible for real-time deployment in resource-constrained devices. We present an end-to-end deep reinforcement learning (RL) solution called Eagle1 to train a neural network policy that directly takes images as input to control the PTZ camera. Training reinforcement learning is cumbersome in the real world due to labeling effort, runtime environment stochasticity, and fragile experimental setups. We introduce a photo-realistic simulation framework for training and evaluation of PTZ camera control policies. Eagle achieves superior camera control performance by maintaining the object of interest close to the center of captured images at high resolution and has up to 17% more tracking duration than the state-of-the-art. Eagle policies are lightweight (90x fewer parameters than Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS) and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for resource-constrained environments. With domain randomization, Eagle policies trained in our simulator can be transferred directly to real-world scenarios2.","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":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116216431","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

Amalgamated Intermittent Computing Systems 混合间歇计算系统

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation Pub Date : 2023-03-23 DOI: 10.1145/3576842.3582388

Bashima Islam, Yubo Luo, S. Nirjon

{"title":"Amalgamated Intermittent Computing Systems","authors":"Bashima Islam, Yubo Luo, S. Nirjon","doi":"10.1145/3576842.3582388","DOIUrl":"https://doi.org/10.1145/3576842.3582388","url":null,"abstract":"Intermittent computing systems undergo frequent power failure, hindering necessary data sample capture or timely on-device computation. These missing samples and deadlines limit the potential usage of intermittent computing systems in many time-sensitive and fault-tolerant applications. However, a group/swarm of intermittent nodes may amalgamate to sense and process all the samples by taking turns in waking up and extending their collective on-time. However, coordinating a swarm of intermittent computing nodes requires frequent and power-hungry communication, often infeasible with limited energy. Though previous works have shown promises when all intermittent nodes have access to the same amount of energy to harvest, work has yet to be looked into scenarios when the available energy distribution is different for each node. The proposed AICS framework provides an amalgamated intermittent computing system where each node schedules its wake-up schedules based on the duty cycle without communication overhead. We propose one offline tailored duty cycle selection method (Prime-Co-Prime), which schedules wake-up and sleep cycles for each node based on the measured energy to harvest for each node and the prior knowledge or estimation regarding the relative energy distribution. However, when the energy is variable, the problem is formulated as a Decentralized-Partially Observable Markov Decision Process (Dec-POMDP). Each node uses a group of heuristics to solve the Dec-POMDP and schedule its wake-up cycle. We conduct a real-world experiment by implementing a deep acoustic event classifier in three MSP430 microcontrollers. AICS successfully captures and processes 41.17% more samples than a swarm of greedy intermittent computing systems while spending 69.7% less time with multiple redundant active systems. Our simulation-based evaluations show a 35.73%–54.40% higher compute and process success rate with AICS than with state-of-the-art algorithms (including reinforcement learning).","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":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116091920","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