Special Issue "Nature Inspired Computing for Intelligent Vehicular Network"

Submission Deadline: 30 January 2021 (closed)
Guest Editors
Dr. Ashish Kr. Luhach, The PNG University of Technology, Papua New Guinea
Dr. Xiao-Zhi Gao, University of Eastern Finland, Finland
Dr. Sujatha Krishnamoorthy, Wenzhou - Kean University, China


Aim and Scope: With the rapid advancement is the communication technologies which leads to fast developments in pervasive and intelligent vehicles networks. Around the globe researchers have been working on designing new applications for Intelligent Vehicular Network (SVN) to create a better experience while driving for the users. Current challenges for the same include: how to integrate computing devices on vehicles which give real time feedback to the user regarding the traffic management and vehicle to vehicle (V2V). How to manage the devices which are resource dependent. How to manage the computing capabilities regarding for the communication.

Nature inspired computing, (NIC) emerged as new research dimension to solve complex computing and engineering problems by inculcating the natural phenomena. The same has produced pathbreaking research and shown a pathway for occurrence of new sub-branches such swarm intelligence and evolutionary computation.

Nature inspired solutions should help relieve the burden of computational powers or loads from the vehicles. Nature inspired computing achieved remarkable achievements in load balancing and efficient resource utilization in cloud computing etc. However, the use of nature inspired computing in manage intelligent vehicles in infancy. How to integrate NIC with intelligent vehicles and make it work to efficient and reliable solutions? Furthermore there many open research issues on how to design the more efficient and reliable resource management in intelligent vehicles network.


The purpose of the Special Section is to present and discuss novel ideas, research, applications and results related to techniques of intelligent vehicles based on nature inspired computing. It aims to bring together researchers from various fields to report the latest findings and developments in intelligent vehicular network with the focus on nature inspired computing to explore future research directions.

The objective of this Special Section is to introduce the current developments and advancements of the technical elements of the nature inspired computing for intelligent vehicular network, from both theoretical and practical perspectives.


The topics of interest include, but are not limited to:

• Architecture and framework establishment of NIC based intelligent vehicular Network

• Design of efficient NIC solutions for intelligent vehicular Network

• NIC-Empowered approach of route planning for intelligent vehicular Network

• NIC-Empowered approach of traffic scheduling for intelligent vehicular Network

• NIC-Empowered resource management for intelligent vehicular Network

• NIC-Empowered security and privacy protection for intelligent vehicular Network

• NIC-Empowered real-time applications for intelligent vehicular Network

• Future NIC-Empowered Smart vehicular Network: challenges and open issues

• Modelling and Analysis of Vehicular Network Protocols

• Scalability issues in Large-scale Vehicular Networks

• NIC based Vehicular Sensor Networks

• NIC based Vehicular Cloud Networks

• NIC based Vehicular Network Applications

Nature Inspired Computing, Soft Computing, Intelligent Vehicular Network

Published Papers
  • Thermodynamics Inspired Co-operative Self-Organization of Multiple Autonomous Vehicles
  • Abstract This paper presents a co-operative, self-organisation method for Multiple Autonomous Vehicles aiming to share surveillance responsibilities. Spatial organization or formation configuration of multiple vehicles/agents’ systems is crucial for a team of agents to achieve their mission objectives. In this paper we present simple yet efficient thermodynamic inspired formation control framework. The proposed method autonomously allocates region of surveillance to each vehicle and also re-adjusts the area of their responsibilities during the mission. It provides framework for heterogeneous UAVs to scatter themselves optimally in order to provide maximum coverage of a given area. The method is inspired from a natural phenomenon… More
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