Call for Papers - Special Issue on Intelligent Transportation and Logistics in Manufacturing with Deployment of Big Data and Artificial Intelligence

2022-09-09

In recent years, sensor and IoT deployment have improved significantly in transportation and goods maintenance. The data obtained from various sensors used in transportation is utilised to analyse automated transportation and supply chain management. Moreover, deploying a broad range of IoT devices enables the collection and analysis of data collected during transportation and logistics. Assistive technology such as sensors in autonomous automobiles and vehicles collect various data relating to audio and picture processing. The data mentioned above might be utilised for environmental research and other functions. Furthermore, technical implementations based on big data and artificial intelligence result in data collecting, analysis, and prediction. Thus, the technologies mentioned above may aid in route prediction, demand forecasting in warehouses, and inventory management through robotic aided analysis.

Several aspects of AI include conducting cognitive functions in a complicated environment, perception, reasoning, and problem-solving. Some of the techniques used are fuzzy approaches, deep neural networks, sentiment analysis, pattern analysis, and reinforcement learning. Furthermore, the use of big data in intelligent transportation and warehousing systems assists in sorting sensor data utilising data analysis methodologies. Because the data generated from IoT-enabled transportation systems are complex, the combination of big data and AI algorithms may aid in resolving real-time issues associated with smart logistics management and ubiquitous transportation.

The manufacturing business is interested in forecasting product output, aided by AI algorithms that make data predictions. Thus, the industry's data gathering might comprise augmented reality/virtual reality data, video and image data, and voice control and prediction analysis for intelligent mobility. Furthermore, deploying 5G/6G communication systems and RFID in the communication medium aids in the efficient transmission and structuring of data in automated systems. Also, with distributed data management enabled by a blockchain network, fog/edge computing may aid in data protection and management.

This special issue discusses the benefits of AI and effective data management systems to intelligent systems and logistics management. Researchers and industrialists are invited to share their perspectives on using intelligent systems in conjunction with artificial intelligence to collect a trained data set for effective analysis of logistics transportation, road traffic analysis, transportation services, route planning, and asset maintenance.

Topics include, but are not limited to:

  • Intelligent port management and warehousing related to shipping maintenance related to big data and AI
  • Smart container terminal management with data analysis and prediction methods
  • Intelligent technologies for the deployment of AI and data analytics in industry 4.0
  • Maritime logistics with the security deployment related to fuzzy neural networks and big data
  • Optimised applications related to deep learning methods for data analytics related to smart industry and logistics
  • Implementation of 5G technologies for mobility management and tracking with data analysis
  • Enhanced cargo management and Procurement 4.0 deployment with data analytics and ML
  • Deployment of digital twins in the management of logistics and industry 4.0
  • Enhanced deployment of IoT and sensors in smart transportation and logistics
  • Distributed logistics data management with blockchain and edge computing


Important Timeline

Submission deadline: January 25, 2023
Authors notification: April 25, 2023
Final notification: September 26, 2023

Guest Editors
A. Shanthini (Lead GE), SRM Institute of Science and Technology, Kattankulathur, India
Gunasekaran Manogaran (Co-GE), Universidad Distrital Francisco José de Caldas, Colombia
Priyan Malarvizhi Kumar (Co-GE), Kyung Hee University, South Korea