Low-Cost Internet of Things Based Real-Time Pavement Monitoring System

被引：1

作者：

Bekiroglu, Korkut ^{[1
]}

Tekeoglu, Ali ^{[2
]}

Shen, Jiayue ^{[3
]}

Boz, Ilker ^{[4
]}

机构：

[1] SUNY Polytech Inst, Elect Engn Technol Dept, Utica, NY 13502 USA

[2] Johns Hopkins Univ, Appl Phys Lab, Baltimore, MD 21218 USA

[3] SUNY Polytech Inst, Mech Engn Technol Dept, Utica, NY USA

[4] Virginia Transportat Res Council, Charlottesville, VA USA

来源：

IEEE CONGRESS ON CYBERMATICS / 2021 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS (ITHINGS) / IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) / IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) / IEEE SMART DATA (SMARTDATA) | 2021年

关键词：

IoT; pavement monitoring system; data collection;

D O I：

10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00018

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A low-cost IoT-based data collection apparatus for pavement monitoring systems was developed and tested in this study. The proposed system consists of various components, and each part requires a specific design. Therefore, the complete system is partitioned into five modules (Sensor, power, IoT, LTE/4G, and UI/WebApp modules), and each module is developed according to the project specification. While designing the modules, rough environmental conditions alongside pavements in rural areas are considered. Therefore, the final product can be used in rural areas to collect real-time data as long as there are LTE connections in the area. The final product allows users to collect real-time, dense data such as strain, pressure, temperature, and humidity without traveling to the site. In addition, the data is accessible anywhere in the world as long as an internet connection is available. The developed apparatus is tested through the sensors embedded under the heavy vehicle simulator (HVS) at the Accelerated Pavement Testing (APT) facility located at Blacksburg, Virginia. The continuous data streaming to the Cloud, as well as even more dense data, is collected and recorded in the local microprocessor Raspberry-pi memory and Cloud at the same time. The sample data both from local R-pi and Cloud is presented in this paper.

引用

页码：17 / 22

页数：6

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