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

Green Thumb Engineering: Artificial intelligence for managing IoT enabled houseplants 绿拇指工程:管理物联网室内植物的人工智能

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345850

Antti Nurminen, A. Malhi

{"title":"Green Thumb Engineering: Artificial intelligence for managing IoT enabled houseplants","authors":"Antti Nurminen, A. Malhi","doi":"10.1109/GCAIoT51063.2020.9345850","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345850","url":null,"abstract":"Many hobbies and household activities can be carried out with little effort, and without deeper understanding of the phenomenon at hand. Managing houseplants and herbs is such a hobby; one can simply provide water for plants and hope they would prosper. However, some people seem to have a better luck in maintaining their plants, while many fail and claim they do not possess a “green thumb”. Fortunately, this does not require magic, but engineering. We use innovative Internet of Things and artificial intelligence technologies to monitor and analyze moisture, humidity, luminosity and temperature levels to assist end users for optimization of environmental conditions for their houseplants, simultaneously increasing their understanding of plant life. For plant health monitoring, we construct a system yielding the normalized difference vegetation index, supported by visual validation by users. We run the system for a selected plant, basil, in varying environmental conditions to cater for typical home conditions in pursue of bootstrapping our artificial intelligence with the acquired data. For end users, we implement a web based user interface which provides both instructions and explanations.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"40 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":"128740868","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

Heuristic Based Routing Algorithms for Vehicular Network Using Tabu Search and ANN 基于禁忌搜索和人工神经网络的启发式车辆网络路由算法

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345893

H. Ignatious, S. Harous, H. El-Sayed

引用次数: 0

LoRa WAN Roaming for Intelligent Shipment Tracking LoRa WAN漫游用于智能货物跟踪

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345843

Francesco Flammini, A. Gaglione, Daniel Tokody, Dalihor Dohrilovic

引用次数: 9

Secure Smart Cities Framework Using IoT and AI 使用物联网和人工智能保护智慧城市框架

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345912

Shaibal Chakrabarty, D. Engels

{"title":"Secure Smart Cities Framework Using IoT and AI","authors":"Shaibal Chakrabarty, D. Engels","doi":"10.1109/GCAIoT51063.2020.9345912","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345912","url":null,"abstract":"We present a framework to secure Internet-of- Things (IoT) enabled Smart Cities using Black Networks and Artificial Intelligence (AI) to protect against a broad range of current and future cyber attacks. Smart city cyber systems carry critical data beginning with the simple sensor data captured by IoT devices up to and including providing the essential services and commands dependent upon that data. The broad reliance upon IoT in smart cities significantly increases the attack surface of the already large, complex, and heterogeneous smart city system by making each and every IoT device and its communications a potential entry point into the system. Our proposed framework utilizes Black Network protocols and key management to secure the most vulnerable, and typically unsecured, IoT communications. The framework utilizes a hierarchical, distributed architecture with pooled resources to prevent single points of failure and to sandbox attack impacts. This hierarchy allows AI enabled management tools to be placed both near the IoT edge using localized data and in the Big Data collections. Our analysis reveals that the proposed framework provides an adaptive health and security management framework that accounts for the dynamic, continuous, and ever-changing life in a smart city.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"39 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":"130622221","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: 11

SmartBlackBox: Enhancing Driver's Safety Via Real-Time Machine Learning on IoT Insurance Black-Boxes 智能黑匣子:通过物联网保险黑匣子上的实时机器学习提高驾驶员安全

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345888

Eliana S. Stivan, Andrea Damiani, Emanuele Del Sozzo, M. Santambrogio

{"title":"SmartBlackBox: Enhancing Driver's Safety Via Real-Time Machine Learning on IoT Insurance Black-Boxes","authors":"Eliana S. Stivan, Andrea Damiani, Emanuele Del Sozzo, M. Santambrogio","doi":"10.1109/GCAIoT51063.2020.9345888","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345888","url":null,"abstract":"The Internet of Things (IoT) is shifting from a purely technical perspective to being a technology with implications on society and its economy. Responding to the global rise of awareness on how the IoT impacts on important themes such as health, safety and social responsibility, we propose and evaluate an IoT device in a field where safety is critical: a smart black-box for the automotive sector, starting from the concept popularized by insurance companies. The SmartBlackBox is a device that supports on-board machine learning to classify the drivers' behavior and supply valuable insight on how to enhance their driving styles. It features reconfigurable hardware within an embedded System-on-a-Chip that is programmed to transform what is usually a simple IoT data-ingestion node into an intelligent companion that learns the drivers' behavior, supporting them in achieving a safer driving style. Compared to traditional black-boxes, thanks to accelerators synthesized on reconfigurable hardware, the SmartBlackBox enters the domain of cyber-physical systems as it supports faster data input streams coming from multiple sensors, ad-hoc data compression for edge-cloud communication, and, especially, realtime classification of driving maneuvers.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"43 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114097908","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

Designing A Compact Convolutional Neural Network Processor on Embedded FPGAs 基于嵌入式fpga的紧凑卷积神经网络处理器设计

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345903

Yingjian Ling, Hsu-Hsun Chin, Hsin-I Wu, R. Tsay

{"title":"Designing A Compact Convolutional Neural Network Processor on Embedded FPGAs","authors":"Yingjian Ling, Hsu-Hsun Chin, Hsin-I Wu, R. Tsay","doi":"10.1109/GCAIoT51063.2020.9345903","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345903","url":null,"abstract":"FPGA-based Convolutional Neural Network (CNN) processor has been widely applied for highly-parallelized computations and fast deployment. However, designing on embedded FPGA needs to consider multiple aspects, such as the feasibility of limited configurable resource on FPGA, external memory latency and the scheduling between memory and computation units. These considerations hence hinder the usage of FPGA. Addressing these issues, we elaborate a systematic design approach that allow fast deployment, which includes the parameterized computation and memory unit, which can be configured based on the target platform, and an evaluation approach for searching the optimal setting sets. To evaluate the proposed approach, we performed object detection task, YOLOv2, on PYNQ-Zl and achieved 48.23 GOPs throughputs as well as 0.611 seconds execution time. This is 42.38 and 12.8 times faster than the same inference on CPU and GPU and is 2.36 times faster than other FPGA implementations. Additionally, our created evaluation model is only 5-22% apart from the implementation result, which is 60% less than previous work.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"43 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":"126785883","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

Run-Time Analysis of Road Surface Conditions Using Non-Contact Microwave Sensing 基于非接触式微波传感的路面状况运行时分析

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345917

J. Blanche, D. Mitchell, D. Flynn

{"title":"Run-Time Analysis of Road Surface Conditions Using Non-Contact Microwave Sensing","authors":"J. Blanche, D. Mitchell, D. Flynn","doi":"10.1109/GCAIoT51063.2020.9345917","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345917","url":null,"abstract":"Safety management of winter roads is dependent on targeted distributions of salt. Insufficient salt dispersal results in dangerous driving conditions, while excessive deposition results in adverse environmental effects and wastes valuable resources. In this paper we present the results of Frequency Modulated Continuous Wave radar (FMCW) analysis for real-time salt detection on road surfaces. Experiments are conducted within laboratory conditions and field trials, with the FMCW sensor installed onto a commercial road gritter. Performed to industry-standard salt dispersal concentrations, we test FMCW sensitivity to ice-thaw on concrete, marine rock-salt on ice and brown-salt brine concentrations. Results demonstrate that FMCW in the K-band is sensitive to brine and rock-salt in both laboratory and field conditions. Consistent results for incremental salt residues in the field of view of the sensor are observed, where the return signal is consistently within a 0.5-3 x106 absolute unit (a.u.) range in the laboratory and a 10–50 (a.u.) range in the field. We propose that FMCW is uniquely suited to detecting black ice, concentrations of brine solutions and residual salt, invisible to visual inspection. FMCW sensing holds significant prospect for providing previously inaccessible data relating to runtime dynamic road surface conditions and environmental monitoring.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"23 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":"127289050","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: 5

Entropy Weighted-Based (EWB) I-LEACH Protocol for Energy-Efficient IoT Applications 基于熵权(EWB)的I-LEACH协议用于节能物联网应用

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345819

Prinu C. Philip, Mohammed Abdelhafez

{"title":"Entropy Weighted-Based (EWB) I-LEACH Protocol for Energy-Efficient IoT Applications","authors":"Prinu C. Philip, Mohammed Abdelhafez","doi":"10.1109/GCAIoT51063.2020.9345819","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345819","url":null,"abstract":"The Internet of Things (IoT) is one of the emerging applications in the Wireless Sensor Networks (WSNs). The major concern in WSN is the limited battery power, which can be overcome by selecting the effective cluster head (CH) for the transmission of data in the network. In this paper, an entropy weighted-based I-LEACH protocol is developed for the selection of the CH. The proposed entropy weighted-based I-LEACH protocol is developed by modifying the standard LEACH protocol with the entropy weight. Initially, the IoT nodes are grouped together to form clusters, which is followed by the selection of the cluster head (CH) for the transmission of the data packets to the base station (BS). The CH is selected depending on the threshold value, which is based on the entropy function. The performance metrics, such as number of alive nodes and energy is used for evaluating the effectiveness of the proposed entropy weighted-based (EWB) I-LEACH protocol. The proposed EWB I-LEACH protocol obtained a maximal alive nodes of 11 and maximal energy of 0.1138 J for 50 nodes under dual-slop channel model and obtained a maximal alive nodes of 9 and maximal energy of 0.1116 J for 50 nodes by considering log normal shadowing channel model when compared to the existing protocols.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"85 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":"129843711","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

Qurra : an Offline AI-based Mobile Doctor Qurra:一个基于离线人工智能的移动医生

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345862

Hamza Alsharif, Alaa Badokhon, Khaled Alhazmi

{"title":"Qurra : an Offline AI-based Mobile Doctor","authors":"Hamza Alsharif, Alaa Badokhon, Khaled Alhazmi","doi":"10.1109/GCAIoT51063.2020.9345862","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345862","url":null,"abstract":"The recent global health pandemic has shifted the way healthcare is provided. Healthcare providers are overwhelmed with hospitals' beds being occupied and concerned about well being of visiting patients in need of regular checkups. Hence, innovative mobile-based healthcare solutions are needed. This work named Qurra or in Arabic presents a real-time solution that uses pre-trained built-in machine learning (ML)-models on mobile devices for convenient health checkups. The Qurra application operates by sampling data from various mobile sensors and uses the sampled data as input to different machine learning modules to produce a meaningful health diagnosis. The developed modules are the heart rate and cough detection. These modules are the focus of this paper. The approach used in this work relies on externally ML- based trained models that are ported to the application. Then, sensory inputs are tested against these pre-trained models, locally computed and analyzed on the mobile phone. The results are displayed to the user in real-time. This approach of having the models embedded to the mobile phone eliminates the need for internet connectivity. Moreover, the developed system is compared with a third party application in addition to its native model on a desktop computer. Also, the app has fast overall processing time of approximately 2-3.5 seconds.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"21 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":"115469297","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

Trust Zone Formation for Building Automation Networks Using Building Information Modeling 基于建筑信息模型的楼宇自动化网络信任区域形成

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345857

A. Wall, Björn Butzin, D. Timmermann

引用次数: 0