2023 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第3页

Multi-Application Hierarchical Autoscaling for Kubernetes Edge Clusters Kubernetes边缘集群的多应用分层自动伸缩

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00074

Ioannis Dimolitsas, Dimitrios Spatharakis, Dimitrios Dechouniotis, Anastasios Zafeiropoulos, S. Papavassiliou

{"title":"Multi-Application Hierarchical Autoscaling for Kubernetes Edge Clusters","authors":"Ioannis Dimolitsas, Dimitrios Spatharakis, Dimitrios Dechouniotis, Anastasios Zafeiropoulos, S. Papavassiliou","doi":"10.1109/SMARTCOMP58114.2023.00074","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00074","url":null,"abstract":"The dynamic workload demands of smart city applications hosted on edge infrastructures require the development of advanced scaling mechanisms. Recent studies proposed single-application autoscaling solutions based on various technical approaches. However, for edge infrastructures with limited resource availability, it is essential to simultaneously manage heterogeneous application requirements, aiming at optimal resource allocation and minimal operational costs. This study introduces a multi-application hierarchical autoscaling framework for Kubernetes Edge Clusters. An application-based mechanism nominates the best applications’ deployments based on workload prediction and several criteria that guarantee the application’s performance while minimizing the infrastructure provider’s cost. For the joint application orchestration, an aggregation mechanism composes the candidate scaling solutions for the cluster. Then, a cluster autoscaling mechanism, based on the Analytic Hierarchy Process, undertakes the cluster’s scaling decision to optimize the resource allocation and energy consumption of the cluster. The evaluation illustrates the benefits of the proposed scaling strategy, achieving significant improvement in the average allocated resources and energy consumption compared to single-application approaches.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121460874","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

Nisshash: Design of An IoT-based Smart T-Shirt for Guided Breathing Exercises Nisshash:一种基于物联网的引导呼吸练习智能t恤的设计

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00019

Md Abdullah Al Rumon, Veeturi Suparna, Mehmet Seckin, Dhaval Solanki, K. Mankodiya

{"title":"Nisshash: Design of An IoT-based Smart T-Shirt for Guided Breathing Exercises","authors":"Md Abdullah Al Rumon, Veeturi Suparna, Mehmet Seckin, Dhaval Solanki, K. Mankodiya","doi":"10.1109/SMARTCOMP58114.2023.00019","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00019","url":null,"abstract":"Breathing exercises are gaining attention in managing anxiety and stress in daily life. Diaphragmatic breathing, in particular, fosters tranquility for both body and mind. Existing methods, such as meditation, yoga, and medical devices for guided breathing, often require expert guidance, complex instruments, cumbersome devices, and sticky electrodes. To address these challenges, we present Nisshash, an IoT-based smart T-shirt offering a personalized solution for regulated breathing exercises. Nisshash is embedded with three-channel e-textile respiration sensors and a tailored analog front-end (AFE) board to simultaneously monitor respiration rate (RR) and heart rate (HR). In this work, we seamlessly integrate soft textile sensors into a T-shirt and develop a detachable and Wi-Fi-enabled (2.4GHz) bio-instrumentation board, creating a pervasive wireless system (WPS) for guided breathing exercises (GBE). The system features an intuitive graphical user interface (GUI) and a seamless IoT-based control and computing system (CCS). It offers real-time instructions for inhaling and exhaling at various breathing speeds, including slow, normal, and fast breathing. Functions such as filtering, peak detections for respiration, and heart rate analysis are computed conjointly at the sender and receiver ends. We utilized the Pan-Tompkins and custom algorithms to calculate HR and RR from the filtered time-series signals. We conducted a study with 10 healthy adult participants who wore the T-shirt and performed guided breathing exercises. The average respiration event (inhale-exhale) detection accuracy was ≈98%. We validated the recorded HR against the 3-lead standard ECG monitoring device, achieving an accuracy of ≈99%. The RR-HR correlation analysis showed an R square value of 0.987. Collectively, these results demonstrate Nisshash’s potential as a personal guided breathing exercise solution.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115929416","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

SrPPG: Semi-Supervised Adversarial Learning for Remote Photoplethysmography with Noisy Data SrPPG:带噪声数据的远程光容积脉搏波半监督对抗学习

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00021

Zahid Hasan, A. Faridee, Masud Ahmed, Shibi Ayyanar, Nirmalya Roy

{"title":"SrPPG: Semi-Supervised Adversarial Learning for Remote Photoplethysmography with Noisy Data","authors":"Zahid Hasan, A. Faridee, Masud Ahmed, Shibi Ayyanar, Nirmalya Roy","doi":"10.1109/SMARTCOMP58114.2023.00021","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00021","url":null,"abstract":"Remote Photoplethysmography (rPPG) systems offer contactless, low-cost, and ubiquitous heart rate (HR) monitoring by leveraging the skin-tissue blood volumetric variation-induced reflection. However, collecting large-scale time-synchronized rPPG data is costly and impedes the development of generalized end-to-end deep learning (DL) rPPG models to perform under diverse scenarios. We formulate the rPPG estimation as a generative task of recovering time-series PPG from facial videos and propose SrPPG, a novel semi-supervised adversarial learning framework using heterogeneous, asynchronous, and noisy rPPG data. More specifically, we develop a novel encoder-decoder architecture, where rPPG features are learned from video in a self-supervised manner (encoder) to reconstruct the time-series PPG (decoder/generator) with physics-inspired novel temporal consistency regularization. The generated PPG is scrutinized against the real rPPG signals by a frequency-class conditioned discriminator, forming a generative adversarial network. Thus, SrPPG generates samples without point-wise supervision, alleviating the need for time-synchronized data collection. We experiment and validate SrPPG by amassing three public datasets in heterogeneous settings. SrPPG outperforms both supervised and self-supervised state-of-the-art methods in HR estimation across all datasets without any time-synchronous rPPG data. We also perform extensive experiments to study the optimal generative setting (architecture, joint optimization) and provide insight into the SrPPG behavior.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116823340","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

Data Acquisition and Analysis for Improving the Utility of Low Cost Soil Moisture Sensors 低成本土壤湿度传感器的数据采集与分析

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00087

Gautam Mundewadi, R. Wolski, C. Krintz

{"title":"Data Acquisition and Analysis for Improving the Utility of Low Cost Soil Moisture Sensors","authors":"Gautam Mundewadi, R. Wolski, C. Krintz","doi":"10.1109/SMARTCOMP58114.2023.00087","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00087","url":null,"abstract":"To cultivate healthy plants and high crop yields, growers must be able to measure soil moisture and irrigate accordingly. Errors in soil moisture measurements can lead to irrigation mismanagement with costly consequences. In this paper, we present a new approach to smart computing for irrigation management to address these challenges at a lower cost. We calibrate low cost, low precision soil moisture sensors to more accurately distinguish wet from dry soils using high cost, high precision Davis Instrument sensors. We investigate different modeling techniques including the natural log of the odds ratio (Log-odds), Monte Carlo simulation, and linear regression to distinguish between wet and moist soils and to establish a trustworthy threshold between these two moisture states. We have also developed a new smartphone application that simplifies the process of data collection and implements our analysis approach. The application is extensible by others and provides growers with low cost, data-driven decision support for irrigation. We implement our approach for UCSB’s Edible Campus student farm and empirically evaluate it using multiple test beds. Our results show an accuracy rate of 91% and lowers costs by 4x per deployment, making it useful for gardeners and farmers alike.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116140441","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 False Data Injection in a Large-Scale Water Distribution Network 大规模配水网络中假数据注入检测

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00062

Ayanfeoluwa Oluyomi

引用次数: 0

SmartAgr 2023 Welcome Message from Workshop Chairs SmartAgr 2023工作坊主席欢迎辞

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/smartcomp58114.2023.00012

引用次数: 0

DQN for Smart Transportation Supporting V2V Mobile Edge Computing 支持V2V移动边缘计算的智能交通DQN

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00048

Xiaoming Guo, Xiao Hong

引用次数: 0

Performance Tradeoff in DNN-based Coexisting Applications in Resource-Constrained Cyber-Physical Systems 资源受限网络物理系统中基于dnn共存应用的性能权衡

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00053

Elijah Spicer, S. Baidya

引用次数: 0

An Online Continuous Semantic Segmentation Framework With Minimal Labeling Efforts 基于最小标注的在线连续语义分割框架

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00032

Masud Ahmed, Zahid Hasan, Tim Yingling, Eric O'Leary, S. Purushotham, Suya You, Nirmalya Roy

{"title":"An Online Continuous Semantic Segmentation Framework With Minimal Labeling Efforts","authors":"Masud Ahmed, Zahid Hasan, Tim Yingling, Eric O'Leary, S. Purushotham, Suya You, Nirmalya Roy","doi":"10.1109/SMARTCOMP58114.2023.00032","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00032","url":null,"abstract":"The annotation load for a new dataset has been greatly decreased using domain adaptation based semantic segmentation, which iteratively constructs pseudo labels on unlabeled target data and retrains the network. However, realistic segmentation datasets are often imbalanced, with pseudo-labels tending to favor certain \"head\" classes while neglecting other \"tail\" classes. This can lead to an inaccurate and noisy mask. To address this issue, we propose a novel hard sample mining strategy for an active domain adaptation based semantic segmentation network, with the aim of automatically selecting a small subset of labeled target data to fine-tune the network. By calculating class-wise entropy, we are able to rank the difficulty level of different samples. We use a fusion of focal loss and regional mutual information loss instead of cross-entropy loss for the domain adaptation based semantic segmentation network. Our entire framework has been implemented in real-time using the Robotics Operating System (ROS) with a server PC and a small Unmanned Ground Vehicle (UGV) known as the ROSbot2.0 Pro. This implementation allows ROSbot2.0 Pro to access any type of data at any time, enabling it to perform a variety of tasks with ease. Our approach has been thoroughly evaluated through a series of extensive experiments, which demonstrate its superior performance compared to existing state-of-the-art methods. Remarkably, by using just 20% of hard samples for fine-tuning, our network has achieved a level of performance that is comparable (≈88%) to that of a fully supervised approach, with mIOU scores of 60.51% in the In-house dataset.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128671127","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

Ready or Not? A Robot-Assisted Crop Harvest Solution in Smart Agriculture Contexts 准备好了吗?智能农业背景下的机器人辅助作物收获解决方案

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00088

Thai Thao Nguyen, Jesse Parron, Omar Obidat, A. Tuininga, Weitian Wang

{"title":"Ready or Not? A Robot-Assisted Crop Harvest Solution in Smart Agriculture Contexts","authors":"Thai Thao Nguyen, Jesse Parron, Omar Obidat, A. Tuininga, Weitian Wang","doi":"10.1109/SMARTCOMP58114.2023.00088","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00088","url":null,"abstract":"As robotics and artificial intelligence (AI) technologies have become increasingly relevant over the past couple of years, they will inevitably be key components for industries of all aspects which continue to expand to technological solutions. Particularly, the agricultural industry has progressed to using such means to minimize human involvement and reduce tasks that are time-consuming and costly. Motivated by this, we developed a robot-assisted crop maturity recognition and harvest system to accurately classify and detect the stages of ripeness the crops are in—ripe, medium ripe, and not ripe. Our proposed approach integrates computer vision, image processing, collaborative robotics, and a subcategory of artificial intelligence—transfer learning. The transfer learning-based model is trained to classify and recognize the crop in its maturity stages and locate the crop during real-time detection. Experimental results and analysis in real-world robot-assisted smart agriculture environments successfully demonstrated crop ripeness recognition accuracy, proving transfer learning could be utilized to effectively improve the efficiency and productivity of harvesting processes in the agricultural industry. The future work of this study is also discussed.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133936286","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