2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)最新文献_第2页

An Evolutionary Algorithm for Collaborative Mobile Crowdsourcing Recruitment in Socially Connected IoT Systems 社交物联网系统中协同移动众包招聘的进化算法

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345852

Aymen Hamrouni, Hakim Ghazzai, Turki Alelyani, Y. Massoud

{"title":"An Evolutionary Algorithm for Collaborative Mobile Crowdsourcing Recruitment in Socially Connected IoT Systems","authors":"Aymen Hamrouni, Hakim Ghazzai, Turki Alelyani, Y. Massoud","doi":"10.1109/GCAIoT51063.2020.9345852","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345852","url":null,"abstract":"Mobile crowd sourcing (MCS) enables a distributed problem-solving model in which a crowd of smart devices' users is engaged in the task of solving a data sensing problem through an open call. With the increasing complexity of tasks that are crowdsourced and the need of collaboration among workers, collaborative MCS (CMCS) has emerged to enable requesters to form teams of skilled IoT workers and promote their ability to cooperate together. To efficiently execute such tasks, optimizing the team recruitment process must be conducted. In this paper, we design a low complexity CMCS team recruitment approach that forms and hires a group of socially connected workers having sufficient skills to accomplish a CMCS task. Inspired from swam intelligence, the proposed recruitment approach enables project matching and virtual team formation according to four different fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and platform's confidence level. Applied to a real-world data set, experimental results illustrate the performances of the proposed genetic algorithm for CMCS recruitment and show that our approach outperforms the metaheuristic particle swarm optimization algorithm. Moreover, it is shown that the proposed approach achieves close performance to those of the baseline optimal integer linear program with significant computational saving.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"26 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":"123628342","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

A Social IoT-Driven Pedestrian Routing Approach During Epidemic Time 疫情期间社会物联网驱动的行人路径选择方法

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345900

Abdullah Khanfor, Hamdi Friji, Hakim Ghazzai, Y. Massoud

{"title":"A Social IoT-Driven Pedestrian Routing Approach During Epidemic Time","authors":"Abdullah Khanfor, Hamdi Friji, Hakim Ghazzai, Y. Massoud","doi":"10.1109/GCAIoT51063.2020.9345900","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345900","url":null,"abstract":"The unprecedented worldwide spread of coronavirus disease has significantly sped up the development of technology-based solutions to prevent, combat, monitor, or predict pandemics and/or its evolution. The omnipresence of smart Internet-of-things (IoT) devices can play a predominant role in designing advanced techniques helping in minimizing the risk of contamination. In this paper, we propose a practical framework that uses the Social IoT (SIoT) concept to improve pedestrians safely navigate through a real-wold map of a smart city. The objective is to mitigate the risks of exposure to the virus in high-dense areas where social distancing might not be well-practiced. The proposed routing approach recommends pedestrians' route in a real-time manner while considering other devices' mobility. First, the IoT devices are clustered into communities according to two SIoT relations that consider the devices' locations and the friendship levels among their owners. Accordingly, the city map roads are assigned weights representing their safety levels. Afterward, a navigation algorithm, namely the Dijkstra algorithm, is applied to recommend the safest route to follow. Simulation results applied on a real-world IoT data set have shown the ability of the proposed approach in achieving trade-offs between both safest and shortest paths according to the pedestrian preference.","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":"126538074","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

A DQN-Based Autonomous Car-Following Framework Using RGB-D Frames 采用RGB-D框架的基于dqn的自动车辆跟踪框架

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345899

Hamdi Friji, Hakim Ghazzai, Hichem Besbes, Y. Massoud

引用次数: 7

Interactive Digital Twins Framework for Asset Management Through Internet 基于Internet的资产管理交互式数字孪生框架

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345890

L. Kong, S. Harper, D. Mitchell, J. Blanche, T. Lim, D. Flynn

{"title":"Interactive Digital Twins Framework for Asset Management Through Internet","authors":"L. Kong, S. Harper, D. Mitchell, J. Blanche, T. Lim, D. Flynn","doi":"10.1109/GCAIoT51063.2020.9345890","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345890","url":null,"abstract":"Digitalization is influencing the design, operation and management, as well as planning functions for products and services across a myriad of industries. In our research we focus on the specific needs and challenges in the asset management of remote critical infrastructure. We propose a single Digital Twin framework which can synchronize the data and communication protocols across multiple devices to support exchanging data between the physical world and the cyber world under any scenario, anywhere and at any time. Our framework can support the synchronization of 1000 different sensors and actuators. The results of our Digital Twin are demonstrated using embedded, front-end sensing for offshore energy assets. It can filter and translate complex data and messages from any embedded sensor and operating system. Furthermore, we show how a complete Digital Twin framework allows end-users to simulate future events capturing the interactions between the environment, people and assets, enabling a better understanding of operational risks and remaining useful life of assets.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"35 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":"125342928","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: 13

Reducing Tail Latency In Cassandra Cluster Using Regression Based Replica Selection Algorithm 基于回归的副本选择算法减少Cassandra聚类的尾部延迟

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345823

Euclides Chauque, Ismail Arai, K. Fujikawa

引用次数: 0

RSSI Based Real-Time and Secure Smart Parking Management System 基于RSSI的实时安全智能停车场管理系统

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345877

Thilina Weliwita, H. Ekanayake

引用次数: 2

An autonomous loyalty program based on blockchains for IoT solution providers 物联网解决方案提供商基于区块链的自主忠诚度计划

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345892

S. Gheitanchi

引用次数: 0

Enhanced Reliability of Mobile Robots with Sensor Data Estimation at Edge 边缘传感器数据估计增强移动机器人可靠性

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345811

V. Sarker, Prateeti Mukherjee, Tomi Westerlund

{"title":"Enhanced Reliability of Mobile Robots with Sensor Data Estimation at Edge","authors":"V. Sarker, Prateeti Mukherjee, Tomi Westerlund","doi":"10.1109/GCAIoT51063.2020.9345811","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345811","url":null,"abstract":"The proliferation of sensing equipment serving an expansive range of applications has led the Internet of Things (loT) paradigm to cover technologies beyond Wireless Sensor Networks (WSN). Extensive advancement in electronics, communication methods and sensors has made it possible to leverage advanced technologies such as Machine Learning and Probabilistic Modeling in resource-constrained embedded systems. These techniques increase reliability and enhance interactions among physical elements of an loT-based system in which data loss or corruption seems inevitable. However, traditional data estimation and reconstruction methods cannot be directly applied considering the computational limitations at the edge of the network. Therefore, mobile robots would greatly benefit from a resource efficient sensor data recovery procedure, capable of generating near-accurate estimates at the resource-constrained Edge layer. In this paper, we introduce a novel Bayesian filtering-based data reconstruction scheme, with real-time performance and precision for incoming semantic and geometric information from a varied set of sensors to increase reliability of autonomous navigation of mobile robots. Afterwards, we corrupt each stream of observations to validate model performance against a baseline. Furthermore, we also provide benchmark on execution latency, CPU usage and current draw while running the models in a practical setup.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"1 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":"129448526","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

Weight Compression-Friendly Binarized Neural Network 加权压缩友好二值化神经网络

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345815

Yuzhong Jiao, Xiao Huo, Yuan Lei, Sha Li, Yiu Kei Li

{"title":"Weight Compression-Friendly Binarized Neural Network","authors":"Yuzhong Jiao, Xiao Huo, Yuan Lei, Sha Li, Yiu Kei Li","doi":"10.1109/GCAIoT51063.2020.9345815","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345815","url":null,"abstract":"The resources of edge devices in AIoT systems are usually constrained with size and power. The computational complexity of neural network models in these edge devices has become a major concern. The most compact form of deep neural networks is binarized neural network (BNN), which adopts binary weights and exclusive NOR (XNOR) operations as binary convolution. In this paper, we propose weight compression-friendly BNN to save hardware resources by reducing memory space. The proposed technique does not binarize weights just according to the signs of weights, but fully considers compression efficiency in the training of the BNN model. The experiments are performed by using the binary version of a 6-layer convolutional neural network (CNN) and MNIST case. The results show that the proposed technique can achieve more than 25% reduction in memory space with accuracy loss of 1 %, or more than 35% memory reduction with about 2.5% accuracy drop for MNIST classification. The weight compression method does not destroy the regular structure of neural networks, so the proposed technique is very fit for processor-based BNN hardware accelerators.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"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":"133366302","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

IoT-Based Smart Watering System Towards Improving the Efficiency of Agricultural Irrigation 面向提高农业灌溉效率的物联网智能灌溉系统

2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) Pub Date : 2020-12-12 DOI: 10.1109/GCAIoT51063.2020.9345902

T. Balasooriya, Pranav Mantri, Piyumika S. Suriyampola

{"title":"IoT-Based Smart Watering System Towards Improving the Efficiency of Agricultural Irrigation","authors":"T. Balasooriya, Pranav Mantri, Piyumika S. Suriyampola","doi":"10.1109/GCAIoT51063.2020.9345902","DOIUrl":"https://doi.org/10.1109/GCAIoT51063.2020.9345902","url":null,"abstract":"A large amount of water is wasted in agriculture today due to inefficient irrigation techniques. By monitoring the soil moisture of crops and the pH level of the irrigation water, not only can water be conserved, but healthier plants can also be cultivated. Even though several smart watering systems have been proposed, currently none of the proposed systems consider both the pH of irrigation water and soil moisture together. This research proposes an IoT-Based Smart Watering System (IBSWS) which addresses both of these concerns by using pH and soil moisture sensors to take real-time data and process it through a cloud environment using microcontrollers. This enables continuous monitoring of soil moisture and pH levels. In addition, IBSWS implements a mobile app for farmers who use the system to monitor and control the irrigation system as well as the crop environment. The IBSWS prototype demonstrates that the use of sensors and WiFi-enabled microcontrollers over a cloud environment can be used to implement such a system and properly manage crop irrigation.","PeriodicalId":398815,"journal":{"name":"2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","volume":"1 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":"134052341","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