2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)最新文献

AI Driven IoT Web-Based Application for Automatic Segmentation and Reconstruction of Abdominal Organs from Medical Images 基于AI驱动的基于物联网的腹部器官医学图像自动分割和重建应用

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00045

B. Villarini, Hykoush Asaturyan

{"title":"AI Driven IoT Web-Based Application for Automatic Segmentation and Reconstruction of Abdominal Organs from Medical Images","authors":"B. Villarini, Hykoush Asaturyan","doi":"10.1109/DCOSS54816.2022.00045","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00045","url":null,"abstract":"Medical imaging technology has rapidly advanced in the last few decades, providing detailed images of the human body. The accurate analysis of these images and the segmentation of anatomical structures can produce significant morphological information, provide additional guidance toward subject stratification after diagnosis or before a clinical trial, and help predict a medical condition. Usually, medical scans are manually segmented by expert operators, such as radiologists and radiographers, which is complex, time-consuming and prone to inter-observer variability. A system that generates automatic, accurate quantitative organ segmentation on a large scale could deliver a clinical impact, supporting current investigations in subjects with medical conditions and aiding early diagnosis and treatment planning. This paper proposes a web-based application that automatically segments multiple abdominal organs and muscle, produces respective 3D reconstructions and extracts valuable biomarkers using a deep learning backend engine. Furthermore, it is possible to upload image data and access the medical image segmentation tool without installation using any device connected to the Internet. The final aim is to deliver a web-based image-processing service that clinical experts, researchers and users can seamlessly access through IoT devices without requiring knowledge of the underpinning technology.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115629612","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

Grace: Low-Cost Time-Synchronized GPIO Tracing for IoT Testbeds Grace:物联网测试平台的低成本时间同步GPIO跟踪

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00013

Laura Harms, Christian Richter, O. Landsiedel

{"title":"Grace: Low-Cost Time-Synchronized GPIO Tracing for IoT Testbeds","authors":"Laura Harms, Christian Richter, O. Landsiedel","doi":"10.1109/DCOSS54816.2022.00013","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00013","url":null,"abstract":"Testbeds have become a vital tool for evaluating and benchmarking applications and algorithms in the Internet of Things (IoT). Testbeds commonly consist of low-power IoT de-vices augmented with observer nodes providing control, logging, and often also power-profiling. Today, the research community operates numerous testbeds, sometimes with hundreds of IoT nodes, to allow for detailed and large-scale evaluation. Most testbeds, however, lack opportunities for tracing distributed program execution with high accuracy in time, for example, via minimally invasive, distributed GPIO tracing. And the ones that do, like Flocklab, are built from custom hardware, which is often too complex, inflexible, or expensive to use for other research groups.This paper closes this gap and introduces Grace, a low-cost, retrofittable, distributed, and time-synchronized GPIO tracing system built from off-the-shelf components, costing less than €20 per node. Grace extends observer nodes in a testbed with (1) time-synchronization via wireless sub-GHz transceivers and (2) logic analyzers for GPIO tracing and logging, enabling time-synchronized GPIO tracing at a frequency of up to 8 MHz. We deploy Grace in a testbed and show that it achieves an average time synchronization error between nodes of 1.53 μs.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"1 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120807729","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

(POSTER) SmartTwins: Secure and Auditable DLT-based Digital Twins for the WoT (海报)SmartTwins:安全的、可审计的基于dlt的WoT数字双胞胎

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00027

Iakovos Pittaras, George C. Polyzos

引用次数: 0

Work in Progress paper: Experiment Planning for Heterogeneous Programmable Networks 工作进展论文:异构可编程网络的实验计划

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00079

Nik Sultana

{"title":"Work in Progress paper: Experiment Planning for Heterogeneous Programmable Networks","authors":"Nik Sultana","doi":"10.1109/DCOSS54816.2022.00079","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00079","url":null,"abstract":"Private and publicly-funded cloud infrastructure and testbeds increasingly feature programmable network hardware. Programmable network cards and switches support the execution of increasingly-complex in-network programs that can operate independently of end-hosts to improve the network’s performance, resilience and utilisation. Reasoning about in-network programs, their placement, and workloads is needed to plan jobs on programmable networks. On programmable testbed networks, this reasoning feeds into resource allocation, fairness and reproducible research. But this reasoning is made challenging by the performance and resource diversity of hardware and by the failure modes that can arise in a distributed system.Flightplanner is currently the most comprehensive reasoning system for distributed and heterogeneous in-network programs but it uses a custom formalism and tool implementation, making it difficult to understand, extend, and scale.This paper describes Lightplanner, a generalisation of Flight-planner’s reasoning system that has been implemented on Prolog. It provides an executable formalisation in a well-understood logic. By relying on Prolog’s proof search, Lightplanner is 10 smaller than Flightplanner’s implementation in C++, making×it better suited for others to understand, extend, and scale. A benchmark of publicly-available in-network programs is used to evaluate Lightplanner against Flightplanner. Though the time overhead is slightly larger, Lightplanner can find better allocations than the original, more complex C++ implementation.Lightplanner is being incubated to plan experiments in a local programmable network testbed at Illinois Tech, and as a future step it will be extended to work across federated networks such as FABRIC.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124690012","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

Prototyping a Fine-Grained QoS Framework for 5G and NextG Networks using POWDER 使用POWDER对5G和NextG网络的细粒度QoS框架进行原型设计

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00075

Udhaya Kumar Dayalan, Rostand A. K. Fezeu, T. Salo, Zhi-Li Zhang

{"title":"Prototyping a Fine-Grained QoS Framework for 5G and NextG Networks using POWDER","authors":"Udhaya Kumar Dayalan, Rostand A. K. Fezeu, T. Salo, Zhi-Li Zhang","doi":"10.1109/DCOSS54816.2022.00075","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00075","url":null,"abstract":"Unlike previous generation cellular technologies, 5G networks support diverse radio bands from low-band, mid-band to (mmWave) high-band, and offer a wide variety of new and enhanced features. In particular, 3GPP 5G standards adopt a flow-based 5G Quality-of-Service (QoS) framework that allows more flexibility in mapping QoS \"flows\" to data radio bearers. Nonetheless, the 5G QoS classes are pre-defined and QoS treatment is limited to the \"flow\" level. As we will argue in an earlier paper, the 5G QoS framework cannot fully and intelligently utilize the diversity of 5G radio bands and other capabilities to cope with fast varying channel conditions, and is therefore inadequate in meeting the quality-of-experience (QoE) requirements of many emerging applications such as augmented/virtual realities (AR/VR) and connected and autonomous vehicles (CAV). This has led us to advance a novel software-defined, fine-grained QoS framework for 5G/NextG networks.In this \"work in progress\" paper, we share our initial experience in prototyping the proposed fine-grained QoS framework. Our framework extends both the 5G core network and 5G radio access network (RAN) functionality to enable intelligent control of radio resources in a fashion that exploits application semantics to improve user QoE. We discuss in detail about the changes in different systems and its individual components, share the current state of implementation progress (work completed and in-progress) and finally our evaluation plan to validate the framework when the implementation is complete.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130364631","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

A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images 轻型深度可分离卷积神经网络筛查胸部CT和x线图像Covid-19感染

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00072

Malliga Subramanian, Sathishkumar V E, C. Ramya, S. V. Kogilavani, Deepti Ravi

{"title":"A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images","authors":"Malliga Subramanian, Sathishkumar V E, C. Ramya, S. V. Kogilavani, Deepti Ravi","doi":"10.1109/DCOSS54816.2022.00072","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00072","url":null,"abstract":"Because Covid-19 spreads swiftly in the community, an automatic detection system is required to prevent Covid-19 from spreading among humans as a rapid diagnostic tool. In this paper, we propose to employ Convolution Neural Networks to detect coronavirus-infected patients using Computed Tomography and X-ray images. In addition, we look into the transfer learning of a deep CNN model, DenseNet201 for detecting infection from CT and X-ray scans. Grid Search optimization is utilized to select ideal values for hyper-parameters, while image augmentation is employed to increase the model’s capacity to generalize. We further modify DenseNet architecture to incorporate a depthwise separable convolution for detecting coronavirus-infected patients utilizing CT and X-ray images. Interestingly, all of the proposed models scored greater than 94% accuracy, which is equivalent to or higher than the accuracy of earlier deep learning models. Further, we demonstrate that depthwise separable convolution reduces the training time and computation complexity.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116474885","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

GNN-based End-to-end Delay Prediction in Software Defined Networking 软件定义网络中基于gnn的端到端时延预测

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00066

Zhun Ge, Jiacheng Hou, A. Nayak

引用次数: 3

Elaborating on Sub-Space Modeling as an Enrollment Solution for Strong PUF 子空间建模在强PUF招生中的应用

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00069

Amir Ali Pour, D. Hély, V. Beroulle, G. D. Natale

{"title":"Elaborating on Sub-Space Modeling as an Enrollment Solution for Strong PUF","authors":"Amir Ali Pour, D. Hély, V. Beroulle, G. D. Natale","doi":"10.1109/DCOSS54816.2022.00069","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00069","url":null,"abstract":"In this work we present sub-space modeling of strong PUF as a cost efficient solution for PUF enrollment for the designers’ community. Our goal is to demonstrate a method which can reduce the overall cost in terms of number of CRPs required for training, training time and memory. Instead of modifying the estimated model structure, we propose to reduce the complexity of the modeling target. This means to provide secured access to the internal responses of strong PUF during the enrollment and capture internal CRPs to model each sub-component of the PUF independently. It also necessitates to permanently remove the internal access after the enrollment to prevent exposure of the internal responses. This means that the internal responses should not be directly accessible after enrollment. Our sub-space modeling method requires lesser number of CRPs compared to modeling the whole PUF. We experimentally prove that sub-space modeling can significantly reduce the cost of training compared to some of the latest works. For instance, we could model 128-stage 6-XOR Arbiter PUF with just above 90% prediction accuracy with 5000 CRPs. Here the response in the CRP is a vector including the responses of the sub-components. Our results show that sub-space modeling is potentially a cost-efficient solution to enroll strong PUF with high complexity.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124493555","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

Semi-supervised Multi-source Domain Adaptation in Wearable Activity Recognition 可穿戴活动识别中的半监督多源域自适应

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00017

Avijoy Chakma, A. Faridee, R. Rao, Nirmalya Roy

{"title":"Semi-supervised Multi-source Domain Adaptation in Wearable Activity Recognition","authors":"Avijoy Chakma, A. Faridee, R. Rao, Nirmalya Roy","doi":"10.1109/DCOSS54816.2022.00017","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00017","url":null,"abstract":"The scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. The traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sources – providing motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132325092","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

Utilizing Carriers for the Energy Node Placement Algorithm in WSNs and IoT Networks 无线传感器网络和物联网网络中基于载波的能量节点布局算法

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) Pub Date : 2022-05-01 DOI: 10.1109/DCOSS54816.2022.00044

Natalie Temene, Charalampos Sergiou, Chryssis Georgiou, V. Vassiliou

{"title":"Utilizing Carriers for the Energy Node Placement Algorithm in WSNs and IoT Networks","authors":"Natalie Temene, Charalampos Sergiou, Chryssis Georgiou, V. Vassiliou","doi":"10.1109/DCOSS54816.2022.00044","DOIUrl":"https://doi.org/10.1109/DCOSS54816.2022.00044","url":null,"abstract":"The limitations of the networks of Internet of Things (IoTs) and Wireless Sensor Networks (WSNs) in terms of computational power, memory and connectivity give rise to several issues that need to be tackled, mostly dynamically, to achieve their tasks. Definitively, a critical factor for the proper operation of these networks is to maintain the connectivity between the nodes, especially in a wireless mesh setting, where communication is performed in hop-by-hop fashion. A method that gains significant research interest for tackling the aforementioned issues is the employment of mobile nodes or as they are frequently called, mobile elements. In this work, we propose a scheme that utilizes carriers to transport mobile nodes to the required points in the network. We provide both a high level description of the concept and also a detailed algorithmic solution. The proposed solution is evaluated through a case study, where hot-spots are created due to congestion in the network and mobile elements are being used to resolve the problem. The experimental results demonstrate that the proposed algorithm can effectively restore the network operation. We believe that our proposed approach can be used to solve similar types of problems.","PeriodicalId":300416,"journal":{"name":"2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133228518","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