C4ISR Service Deployment Based on an Improved Quantum Evolutionary Algorithm

To overcome the limited communication bandwidth in the deployment of command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) services, a service deployment platform was extended from the cloud to a terminal platform based on the concept of fog computing. Additionally, computing and memory resources on the terminal platform were used to alleviate the communication pressure due to the flow of combat information. A corresponding C4ISR service deployment model was constructed, and the quantum evolutionary algorithm, the shortest path algorithm, and the pairwise exchange method were combined to create a two-level search algorithm. This method can plan the operational information flow path and service deployment simultaneously, thereby achieving the optimal deployment of services even under the constraints of limited communication bandwidth.

[1]  Wang Xun,et al.  Nonlinear optimal model and solving algorithms for platform planning problem in battlefield , 2018 .

[2]  Pingzhi Fan,et al.  Key techniques for 5G wireless communications: network architecture, physical layer, and MAC layer perspectives , 2015, Science China Information Sciences.

[3]  Jong-Hwan Kim,et al.  Genetic quantum algorithm and its application to combinatorial optimization problem , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[4]  Zhiqiang Jiao,et al.  Capability Construction of C4ISR Based on AI Planning , 2019, IEEE Access.

[5]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[6]  Laurent D. Michel,et al.  A Counting-Based Approach to Scalable Micro-service Deployment , 2019, CPAIOR.

[7]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[8]  Optimization-Based Decision Support Software for a Team-in-the-Loop Experiment: Multilevel Asset Allocation , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Luo Xue-shan Military information service composition and validation method based on object Petri net , 2011 .

[10]  Mojtaba Alizadeh,et al.  Authentication in mobile cloud computing: A survey , 2016, J. Netw. Comput. Appl..

[11]  Samee Ullah Khan,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems towards Secure Mobile Cloud Computing: a Survey , 2022 .

[12]  Mohit Chamania,et al.  Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey , 2018, Opt. Switch. Netw..

[13]  Chadi Assi,et al.  A Reliability-Aware Network Service Chain Provisioning With Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks , 2017, IEEE Transactions on Network and Service Management.

[14]  Florin Pop,et al.  Microservices Scheduling Model Over Heterogeneous Cloud-Edge Environments As Support for IoT Applications , 2018, IEEE Internet of Things Journal.

[15]  Shanzhi Chen,et al.  A tutorial on 5G and the progress in China , 2018, Frontiers of Information Technology & Electronic Engineering.

[16]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[17]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[18]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[19]  Nanning Zheng,et al.  Artificial intelligence test: a case study of intelligent vehicles , 2018, Artificial Intelligence Review.