2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)最新文献_第3页

Task Scheduling in Cloud Computing Environment Using Bumble Bee Mating Algorithm 基于大黄蜂交配算法的云计算环境任务调度

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345824

Mohammad Alotaibi, Mohammad S. Almalag, Kyle Werntz

{"title":"Task Scheduling in Cloud Computing Environment Using Bumble Bee Mating Algorithm","authors":"Mohammad Alotaibi, Mohammad S. Almalag, Kyle Werntz","doi":"10.1109/GCAIoT51063.2020.9345824","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345824","url":null,"abstract":"Tasks scheduling in cloud computing environment plays an important role for both Cloud Service Providers (CSPs) and the users of the services provided. Therefore, designing an efficient task scheduling algorithm, which fulfill the requirements of CSPs and their clients is essential. Several scheduling algorithms are proposed by various researchers for task scheduling in cloud computing environments. This paper introduces an alternative method for cloud task scheduling problem, which aims to minimize makespan of executing a number tasks on different Virtual Machines (VMs). This method is based on Bumble Bee Mating Optimization (BBMO) algorithm. BBMO is powered by the features of swarm intelligence and local search algorithms. The performance of BBMO is compared to two existing algorithms, Honey Bee Mating Optimization (HBMO) algorithm and Genetic Algorithm (GA). Finally, we analyze the performance of the proposed algorithm with other two algorithms using different scenarios of experiments. The results show that the proposed algorithm (BBMO) outperforms other algorithms.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123241340","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: 3

Blockchain Smart Contract for Scalable Data Sharing in IoT: A Case Study of Smart Agriculture 物联网中可扩展数据共享的区块链智能合约:智能农业案例研究

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345874

Mohsina Rahman, F. Baiardi, L. Ricci

{"title":"Blockchain Smart Contract for Scalable Data Sharing in IoT: A Case Study of Smart Agriculture","authors":"Mohsina Rahman, F. Baiardi, L. Ricci","doi":"10.1109/GCAIoT51063.2020.9345874","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345874","url":null,"abstract":"The emerging Smart Agriculture based on Internet of Things (IoT) is facing major challenges like data sharing, storage, and monitoring, primarily due to the distributed nature of IoT and massive scale. We performed a review of the literature and found that blockchain performance, scalability, cost, and throughput are the major challenges in adopting blockchain for smart agriculture. To overcome these challenges, this paper proposes a scalable and distributed data sharing system integrating access control for smart agriculture. We demonstrate our approach in a smart agriculture setting, which consists of four tiers that are: smart agriculture, smart contract, Interplanetary File System (IPFS) and agriculture stakeholders (remote users). This paper explains in detail the different components of our proposed architecture. Our approach uses anonymous identities to ensure users' privacy. Our approach is fully scalable because a large number of resource owners can use their data sharing smart contracts to create, update or delete data sharing policies. In addition, our approach does not require transaction fees when the smart contract receives a large number of policy evaluation requests. For the sake of simplicity, we publish and test a single data sharing smart contract. However, in practice, multiple smart contracts need to be deployed to allow each resource owner to securely share agriculture data with stakeholders. Finally, we evaluate the performance of our proposed system on the EOS blockchain to show that the resource consumption (in terms of computing power and network bandwidth) introduced by our framework are insignificant compared to its scalability, cost and security benefits.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134426179","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: 20

Towards Intelligent Web Context-Based Content On-Demand Extraction Using Deep Learning 基于深度学习的智能Web内容按需提取

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345816

Mina A. Melek, B. Mokhtar

{"title":"Towards Intelligent Web Context-Based Content On-Demand Extraction Using Deep Learning","authors":"Mina A. Melek, B. Mokhtar","doi":"10.1109/GCAIoT51063.2020.9345816","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345816","url":null,"abstract":"Information extraction and reasoning from massive high-dimensional data at dynamic contexts, is very demanding and yet is very hard to obtain in real-time basis. However, such process capability and efficiency might be affected and limited by the available computational resources and the consequent power consumption. Conventional search mechanisms are often incapable of real-time fetching a predefined content from data source, without concerning the increased number of connected devices that contribute to the same source. In this work, we propose and present a concept for an efficient approach for online content searching, takes advantage of a) the structure of data profiling employed at the related data source; and b) the learning algorithms that are used for extracting its common features and for generating a map of indices to data contents. This enables instant mapping of users requests to make the process as realtime as possible. The adopted learning algorithms main blocks are built to capture the semantic features in the targeted context of data sentences. We reviewed several learning approaches and compared their results based on the criteria of capturing the semantic features that appeared through the preliminary results. The preliminary results conclusively confirmed that employing the weighted recurrent neural networks and the GloVE pre-trained model paired with NMF topic modeling, yielded highly acceptable levels of Fl-score and prediction time.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465824","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

Towards an IoT-based Deep Learning Architecture for Camera Trap Image Classification 基于物联网的相机陷阱图像分类深度学习架构研究

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345858

I. Zualkernan, S. Dhou, J. Judas, A. Sajun, Brylle Ryan Gomez, Lana Alhaj Hussain, Dara Sakhnini

{"title":"Towards an IoT-based Deep Learning Architecture for Camera Trap Image Classification","authors":"I. Zualkernan, S. Dhou, J. Judas, A. Sajun, Brylle Ryan Gomez, Lana Alhaj Hussain, Dara Sakhnini","doi":"10.1109/GCAIoT51063.2020.9345858","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345858","url":null,"abstract":"Maintaining biodiversity is a key component of the United Nations (UN) “Life on Land” sustainability goal. Remote camera traps monitoring animals' movements support research in biodiversity. However, images from these camera traps are currently labeled manually resulting in high processing costs and long delays. This paper proposes an IoT -based system that leverages deep learning and edge computing to automatically label camera trap images and transmit this information to scientists in a timely manner. Inception-V3, MobileNet-V2, ResNet-18, and DenseNet-121 were trained on data consisting of 33,984 images taken during day and night with 6 animal classes. Inception- V3 yielded the highest macro average F1-score of 0.93 and an accuracy of 94%. An IoT-based system was developed that directly captures images from a commercial camera trap, does the inference on the edge using a Raspberry Pi (RPi), and sends the classification results back to a cloud database system. A mobile App is used to monitor the camera images classified on camera traps in real-time. The RPi could easily sustain a rate of processing 1 image every 2 seconds with an average latency of 1.8 second/image. After capture and pre-processing, each inference took an average of 0.2 Millisecond/image on a RPi Model 4B.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115516304","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: 16

Index 指数

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/gcaiot51063.2020.9345872

引用次数: 0

Optimization Model for an Individualized IoT Ambient Monitoring and Control System 个性化物联网环境监控系统的优化模型

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345849

R. L. Patrão, Marcos B. Andrade, Fernanda F. da Silva, L. M. C. E. Martins, Francisco L. de Caldas Filho, Rafael Timóteo de Sousa Júnior

引用次数: 0

Technological Solution Development During the COVID-19 Pandemic: a Case Study in an IoT Lab COVID-19大流行期间的技术解决方案开发:物联网实验室的案例研究

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345864

R. L. Patrão, C. Silva, Gustavo P. C. P. da Luz, Francisco L. de Caldas Filho, Fábio L. L. Mendonça, R. A. D. Sousa

{"title":"Technological Solution Development During the COVID-19 Pandemic: a Case Study in an IoT Lab","authors":"R. L. Patrão, C. Silva, Gustavo P. C. P. da Luz, Francisco L. de Caldas Filho, Fábio L. L. Mendonça, R. A. D. Sousa","doi":"10.1109/GCAIoT51063.2020.9345864","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345864","url":null,"abstract":"Major crisis in human history impose new challenges to all people affected by it. These great challenges usually represent a great opportunity for technological development, since technological solutions are a significant part of the effort to overcome crisis. The COVID-19 pandemic is no exception to this historical trend. We present in this work the development of a technological solution for one challenge imposed by the corona virus outbreak: disinfection of enclosed spaces. Hence, the objectives of this work were: 1 - To present some of the technical choices made to develop the sanitization solution using uvc light; 2 - To present a methodological framework to adapt R&D work to the needs of social/physical distance; 3 - To assess the productivity of the members of the UIoT laboratory during this remote work period. The solution development was carried out by a multidisciplinary team and, in order to evaluate the proposed methodology, a questionnaire was used to assess the team member's perception of productivity. Its results show an overall quality increase, and an individual quantity increase regarding the project's outputs. We concluded that the projects' results were better than what was expected at the beginning of the year.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122978670","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

IoT-Ready Millimeter-Wave Radar Sensors 物联网就绪毫米波雷达传感器

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345836

W. Ahmad, J. Wessel, H. Ng, D. Kissinger

{"title":"IoT-Ready Millimeter-Wave Radar Sensors","authors":"W. Ahmad, J. Wessel, H. Ng, D. Kissinger","doi":"10.1109/GCAIoT51063.2020.9345836","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345836","url":null,"abstract":"This paper demonstrates a millimeter-wave (mm-Wave) radar sensor chip set for industrial, scientific, medical (ISM) and internet of things (IoT) applications. Thanks to their modular expandable wireless transceiver architecture, these radar chips offer implementing multimode radar sensors capable of deploying multiple radar techniques to detect object presence, range, velocity, vibrations and direction of arrival across multiple applications in conjunction with data communication capability for machine-to-machine (M2M) interaction. A 60GHz single-channel radar sensor prototype is implemented where the frequency modulated continuous wave (FMCW) radar technique is applied for object detection and range measurement in a multitarget scenario. A range resolution of 6cm and a ranging precision of 0.lmm at 1m range are experimentally verified. Another two-channel sensor prototype is implemented where multiple-input multiple-output (MIMO) radar technique is applied for direction-of-arrival (DoA) estimation. An experiment of measuring vibration rates from multiple targets at different locations using Doppler radar technique is successfully conducted. This experiment simulates a remote control environment of running machines in factories. Furthermore, an experiment of monitoring a human heartbeat rate remotely by the sensor is performed where a 78bpm rate is measured. Such contactless measurement is extremely important to prevent disease spreading during pandemic seasons such as COVID-19.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"50 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123698015","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

An Elastic IoT Device Management Platform 弹性物联网设备管理平台

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-10-12 DOI: 10.1109/GCAIoT51063.2020.9345907

R. D. Murthy, Mingming Liu

{"title":"An Elastic IoT Device Management Platform","authors":"R. D. Murthy, Mingming Liu","doi":"10.1109/GCAIoT51063.2020.9345907","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345907","url":null,"abstract":"With the recent advancement of technologies over the past year, IoT has become a paradigm in which devices communicate with each other and the cloud to achieve various applications in multidisciplinary fields. However, developing, deploying, and experimenting with IoT applications are still tedious, expensive, and time-consuming due to the factors like heterogeneity of hardware and software. This is where an IoT testbed plays a vital role in aiding developers to test their applications without being deploying it to the target environment. In this paper, we present a testbed that is scalable for heterogeneous devices and mainly focused on a small scale and medium scale IoT application. This testbed would be best suited for testing applications which demand robust nature, remote monitoring and control, incorporation of heterogeneous devices, location tracking of devices, and easy troubleshooting with security and internet connectivity concerns. This testbed is also embraced with the feature to work limit access to the internet. A detailed explanation of the design and architecture of the proposed testbed is provided. We also present a conceptual prototype of the testbed and the results obtained on experimenting under various conditions.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131920407","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

Low Complexity Classification of Power Asset Faults for Real Time IoT-based Diagnostics 基于物联网实时诊断的低复杂度电力资产故障分类

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-10-02 DOI: 10.1109/GCAIoT51063.2020.9345846

Alireza Salimy, I. Mitiche, P. Boreham, A. Nesbitt, G. Morison

{"title":"Low Complexity Classification of Power Asset Faults for Real Time IoT-based Diagnostics","authors":"Alireza Salimy, I. Mitiche, P. Boreham, A. Nesbitt, G. Morison","doi":"10.1109/GCAIoT51063.2020.9345846","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345846","url":null,"abstract":"This paper investigates a new application of Capsule Neural Network (CapsNet), in combination with Constant-Q Transform (CQT), for insulation fault signal detection in High Voltage (HV) power plants. First, a mapping from insulation fault time-series signals to time-frequency images is obtained using the CQT, providing both time and frequency information. Different ways of exploiting the resulting complex-valued CQT are proposed; the CQT magnitude as a 1-channel image and the real-imaginary values of the CQT as a 2-channel image. This paper presents novel work in HV condition monitoring by utilising the CQT and CapsNet methods. Feature extraction and classification, from the produced CQT spectrum, is performed by CapsNet and the Residual Neural Network (ResNet). A performance comparison between both models, shows that CapsNet outperforms the ResNet in terms of classification accuracy with lower computation. The reduced computation and improved classification accuracy proves ideal, for system implementation on an edge embedded device incorporated in an Internet of Things (IoT) arrangement.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131068490","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: 3