Smart Sensing for Container Trucks

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)

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Detail(s)

Original languageEnglish
Number of pages7
Publication statusPublished - 24 Jul 2020

Conference

Title6th International Conference on Big Data Computing and Communications (BigCom 2020)
Location
City
Period24 - 25 July 2020

Abstract

Container logistics is a typical logistics model with complex business processes. In recent years, operation optimization in logistics is identified as significant in cutting labor costs and boosting productivity. To implement operation optimization in container logistics, the accurate load measuring and operational situation information are the prerequisites. However, the current labor-driven measurement and situation recognition suffer from inevitable delays, errors, and expenses. To tackle these challenges, the authors propose a smart sensing system that intelligently achieves the container load measurement and operational situation recognition. In this paper, the authors present a novel approach for load measurement and the design of an edge-computing-powered controller. The kNN (k-Nearest Neighbor) has been introduced to develop an Operational Situation Recognition (OSR) model. The authors realize and implement the smart sensing system in the production environment. Rigorous analysis and prototype evaluations demonstrate the effectiveness of the proposed system.

Research Area(s)

  • Load Measurement, Machine Learning, kNN, Smart Logistics, Container Trucks, Situational Recognition

Citation Format(s)

Smart Sensing for Container Trucks. / Xu, Chengyuan; Zhao, Bin; Yang, Rongwei; Liu, Bingyi; Wang, Jianping; Wang, Jinfan.

2020. Paper presented at 6th International Conference on Big Data Computing and Communications (BigCom 2020), .

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)