2021 IEEE 7th World Forum on Internet of Things (WF-IoT)最新文献_第2页

A Sub-1 GHz Wireless Sensor Network Concentrator Using Multicollectors with Load Balancing for Improved Capacity and Performance 一种采用负载平衡的多收集器的sub - 1ghz无线传感器网络集中器，以提高容量和性能

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595068

Ricardo Arjona, Craig Lee, M. Razo, M. Tacca, A. Fumagalli, Kumaran Vijayasankar

{"title":"A Sub-1 GHz Wireless Sensor Network Concentrator Using Multicollectors with Load Balancing for Improved Capacity and Performance","authors":"Ricardo Arjona, Craig Lee, M. Razo, M. Tacca, A. Fumagalli, Kumaran Vijayasankar","doi":"10.1109/WF-IoT51360.2021.9595068","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595068","url":null,"abstract":"The exponential growth of IoT end devices creates the necessity for cost-effective solutions to further increase the capacity of IEEE802.15.4g-based wireless sensor networks (WSNs). For this reason, the authors present a wireless sensor network concentrator (WSNC) that integrates multiple collocated collectors, each of them hosting an independent WSN on a unique frequency channel. A load balancing algorithm is implemented at the WSNC to uniformly distribute the number of aggregated sensor nodes across the available collectors. The WSNC is implemented using a BeagleBone board acting as the Network Concentrator (NC) whereas collectors and sensor nodes realizing the WSNs are built using the TI CC13X0 LaunchPads. The system is assessed using a testbed consisting of one NC with up to four collocated collectors and fifty sensor nodes. The performance evaluation is carried out under race conditions in the WSNs to emulate high dense networks with different network sizes and channel gaps. The experimental results show that the multicollector system with load balancing proportionally scales the capacity of the network, increases the packet delivery ratio, and reduces the energy consumption of the IoT end devices.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124346120","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

ATCPiM: Analog to Time Coded Processing in Memory for IoT at the Edge ATCPiM:边缘物联网内存中的模拟到时间编码处理

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595467

M. N. Sakib, R. Sreekumar, Vaibhav Verma, Tommy Tracy, M. Stan

引用次数: 2

Enabling civil-military information sharing in federated smart environments 在联合智能环境中实现军民信息共享

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595715

Krzysztof Kanciak, M. Jarosz, Pawel Glebocki, K. Wrona

引用次数: 1

Towards Real-time Learning for Edge-Cloud Continuum with Vehicular Computing 面向边缘云连续体的车载计算实时学习

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595628

Ella Peltonen, Arun Sojan, Tero Päivärinta

{"title":"Towards Real-time Learning for Edge-Cloud Continuum with Vehicular Computing","authors":"Ella Peltonen, Arun Sojan, Tero Päivärinta","doi":"10.1109/WF-IoT51360.2021.9595628","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595628","url":null,"abstract":"Sensor-driven IoT systems are well-known for their capacity to accelerate massive amounts of data in a comparatively short period of time. To have any use, the information delivery and decision making based on the data require efficient learning models together with dynamically deployed computing and network resources. The current cloud and high-performance computing infrastructures, as well as modern edge computing systems especially in the 5G and beyond networks, can be addressed to resolve these challenges. However, there are several application areas especially in vehicular and urban computing, where just harnessing more computational power does not solve computational and real-time requirements of the modern sensing systems that operate in mobile and context-dependent environments. For now, the mathematical challenges of distributed computing and real-time learning algorithms have not been profoundly addressed in the context of the IoT and real-world sensing applications. Data-driven systems also require giving full attention to information delivery, data management, data cleaning, and sensor fusion technologies that need to be equally distributed and real-time competent as the learning algorithms themselves. New software-defined computing and networking approaches and architectures are required to orchestrate the numerous connected resources dynamically, controllably, and securely along with the evolving needs. The key challenge here is to uniform collaboration between different aspects of the system, from data processing and delivery to the algorithms and learning models, not forgetting the computational capacity and networking capabilities, all this in real-time with real-world applications.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130057709","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

Deep Learning of CSI for Efficient Device-free Human Activity Recognition 面向高效无设备人体活动识别的CSI深度学习

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595661

Danista Khan, I. W. Ho

{"title":"Deep Learning of CSI for Efficient Device-free Human Activity Recognition","authors":"Danista Khan, I. W. Ho","doi":"10.1109/WF-IoT51360.2021.9595661","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595661","url":null,"abstract":"Over the years, wireless sensing is gaining popularity in the applications of indoor localization and human activity recognition (HAR). As wireless signals are sensitive to human motion, they reflect and scatter in different directions depending on the activities performed by people. The channel state information (CSI) stores the combined effect of changes in the environment, and such stored pattern is utilized to recognize different human activities such as walking, standing, and sitting. Prior studies on activity recognition mostly differentiate human activities by classifying one complete series into an activity. However, these approaches require massive datasets to give accurate results in real-time scenarios, and the classification is in fact based on short-term activity samples instead of the complete activity series. In this paper, highly accurate sample-level activity recognition is achieved by exploiting a special type of convolutional neural network (CNN), U-Net. The data collection setup does not require manual feature extraction and can efficiently classify short-term activity samples. Our experimental results indicate that the proposed architecture can classify different levels of human activities with an accuracy of 98.57%, which outperforms conventional Deep Neural Network by 14.67% for the same dataset.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"77 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132790012","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

Gait-based People Identification with Millimeter-Wave Radio 基于步态的毫米波无线电人物识别

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595283

M. Z. Ozturk, Chenshu Wu, Beibei Wang, K. Liu

{"title":"Gait-based People Identification with Millimeter-Wave Radio","authors":"M. Z. Ozturk, Chenshu Wu, Beibei Wang, K. Liu","doi":"10.1109/WF-IoT51360.2021.9595283","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595283","url":null,"abstract":"Human gait has been proposed as a biometric that could be used to monitor and identify people unobtrusively. A pervasive gait recognition system would require robustness against environmental changes, minimum cooperation for registering new users, and it should maintain high accuracy over different locations and times, without the need for re-calibration. In this paper, we present a high-accuracy gait recognition system with minimal training requirement using a single commodity millimeter wave (mmWave) radio. In order to reduce the training overhead, we propose a novel 3D joint-feature representation of micro-Doppler and micro-Range signatures that can comprehensively embody the physical relevant features of one’s gait. Our system can automatically detect and segment human walking into gait cycles and effectively extract features with several signal processing methods. These features are then used with a simple convolutional neural network that can be trained quickly. We implement and evaluate our system through experiments conducted at 6 different locations in a typical indoor space with 10 subjects over a month, resulting in >50,000 gait instances. The results indicate that our system achieves an accuracy of 96.1% with a single gait cycle and this performance is sustained over different locations and times.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133117978","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

Securing IoT Cooperative Networks Using Energy Harvesting. 利用能量收集保护物联网合作网络。

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595654

A. Gouissem, K. Abualsaud, E. Yaacoub, T. Khattab, M. Guizani

引用次数: 1

Parking Space Occupancy Monitoring System Using Computer Vision and IoT 基于计算机视觉和物联网的车位占用监测系统

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595935

Luiz Eduardo Giampaoli, Fabiano Hessel

引用次数: 0

Securely & Efficiently Integrating Constrained Devices into an ICN-IoT 安全高效地将受限设备集成到ICN-IoT中

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595708

Nicholas K. Clark

引用次数: 1

Monitoring Social Distancing in Smart Spaces using Infrastructure-Based Sensors 利用基于基础设施的传感器监测智能空间中的社交距离

2021 IEEE 7th World Forum on Internet of Things (WF-IoT) Pub Date : 2021-06-14 DOI: 10.1109/WF-IoT51360.2021.9595897

Naser Hossein Motlagh, Pupu Toivonen, M. A. Zaidan, Eemil Lagerspetz, Ella Peltonen, Ekaterina Gilman, P. Nurmi, S. Tarkoma

{"title":"Monitoring Social Distancing in Smart Spaces using Infrastructure-Based Sensors","authors":"Naser Hossein Motlagh, Pupu Toivonen, M. A. Zaidan, Eemil Lagerspetz, Ella Peltonen, Ekaterina Gilman, P. Nurmi, S. Tarkoma","doi":"10.1109/WF-IoT51360.2021.9595897","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595897","url":null,"abstract":"Social distancing is a critical tool for mitigating disease transmission, particularly in crowded indoor spaces. In this paper, we contribute by assessing the feasibility of re-purposing existing infrastructure of occupancy monitoring sensors and environmental sensors for the dual purpose of monitoring social distancing and supporting disease transmission risk estimation. We consider 410 continuous days of measurements from CO2 and PIR (passive infrared) motion detectors collected from a collaborative smart space, prior to the start of the pandemic in 2017-2018. We demonstrate how these sensors can be used to estimate occupancy levels, as well as analyze occupancy patterns within the space. We also consider the use of overall air quality within the space for estimating insights about potential transmission risks. Based on our analysis, we derive insights into how infrastructure-based sensors can be used to detect problematic areas in the space and offer guidelines on how to modify these areas to be more social distancing aware.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128684629","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: 9