A multidomain virtual network embedding algorithm based on multiobjective optimization for Internet of Drones architecture in Industry 4.0. (5th March 2020)

• Record Type: Journal Article
• Authors: Zhang, Peiying; Wang, Chao; Qin, Zeyu; Cao, Haotong
• Other Names: Aujla Gagangeet Singh guestEditor.; Prodan Radu guestEditor.; Rawat Danda B. guestEditor.
• Is Part Of: Software, practice & experience. Volume 52:Number 3(2022)
• Page Start: 710
• Page End: 728
• Publication Date: 2020-03-05
• Subjects: cross‐domain virtual network mapping algorithm -- Industry 4.0 -- MP‐VNE algorithm -- UAV network technology; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2815
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Unmanned aerial vehicle (UAV) has a broad application prospect in the future, especially in the Industry 4.0. The development of Internet of Drones (IoD) makes UAV operation more autonomous. Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD. How to rationally allocate potential material resources has become an urgent problem to be solved. The main work of this paper is presented as follows: (a) In order to improve the optimization performance and reduce the computation time, we propose a multidomain virtual network embedding algorithm (MP‐VNE) adopting the centralized hierarchical multidomain architecture. The proposed algorithm can avoid the local optimum through incorporating the genetic variation factor into the traditional particle swarm optimization process. (b) In order to simplify the multiobjective optimization problem, we transform the multiobjective problem into a single‐objective problem through weighted summation method. The results prove that the proposed algorithm can rapidly converge to the optimal solution. (c) In order to reduce the mapping cost, we propose an algorithm for selecting candidate nodes based on the estimated mapping cost. Each physical domain calculates the estimated mapping cost of all nodes according to the formula of the estimated mapping cost, and chooses the node with the lowest estimated mapping cost as the candidate node. The simulation results show that the proposed MP‐VNE algorithm has better performance than MC‐VNM, LID‐VNE, and other algorithms in terms of delay, cost and comprehensive indicators.