Smart Sensing for Container Trucks

Xu，Chengyuan1; Zhao，Bin2; Yang，Rongwei3,5; Liu，Bingyi4; Wang，Jianping1; Wang，Jinfan3,5

10.1109/BigCom51056.2020.00007

2020-07-01

978-1-7281-8276-6

Proceedings - 2020 6th International Conference on Big Data Computing and Communications, BigCom 2020

1-7

24-25 July 2020

Deqing, China

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 Neigh-bor) 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.

Container Trucks kNN Load Measurement Machine Learning Situational Recognition Smart Logistics

其他

英语

EI

20203609138781

Trucks ; Containers ; Wages ; Automobiles ; Nearest neighbor search

Automobiles:662.1 ; Heavy Duty Motor Vehicles:663.1 ; Artificial Intelligence:723.4 ; Personnel:912.4 ; Optimization Techniques:921.5

2-s2.0-85090126825

Scopus

1.City University of Hong Kong,Department of Computer Science,Kowloon,Hong Kong
2.Carnegie Mellon University,Department of Electrical and Computer Engineering,Pittsburgh,United States
3.Research Center for Network Communication,Peng Cheng Laboratory,Shenzhen,China
4.School of Computer Science and Technology,Wuhan University of Technology,Wuhan,China
5.Institute of Future Networks,Southern University of Science and Technology,Shenzhen,China

Xu，Chengyuan,Zhao，Bin,Yang，Rongwei,et al. Smart Sensing for Container Trucks[C],2020:1-7.