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Peer-to-Peer Networking and Applications

, Volume 12, Issue 6, pp 1785–1798 | Cite as

Cyber-physical battlefield perception systems based on machine learning technology for data delivery

  • Jian Zhao
  • Chengzhuo Han
  • Zhengqi Cui
  • Rui Wang
  • Tingting YangEmail author
Article
  • 96 Downloads
Part of the following topical collections:
  1. Special Issue on Networked Cyber-Physical Systems

Abstract

Data delivery in Cyber-Physical Battlefield Perception Systems(CPBPS) is a challenging task due to the ubiquity locations and the high mobility of node. Due to the special geographical circumstances, communication networks based on fixed infrastructure are unlikely to be established. This paper presents an air-ground coordination communication transmission network, which consists of Unmanned Aerial Vehicle (UAV) subnets and ground vehicle subnets. The UAVs exploit air-to-air (A2A) and air-to-ground (A2G) communication links to assist vehicle communications. However, overreliance on satellite positioning may cause military information to leak. Therefore, we proposed a K-Nearest Neighbor (KNN )combined with genetic algorithms and based on machine learning system (MLS) for data delivery for battlefield environment to realize the privacy protection and guarantee the security with better prediction. The proposed KNN machine learning system can estimate the movement and path of vehicles based on the mobile information obtained. Furthermore, in order to transmit data of UAVs more efficiently, the genetic algorithms (GA) is utilized to determine the relative location of UAVs. Simulation results verify the performance of proposed algorithm.

Keywords

Machine learning Cyber-physical battlefield perception systems K-nearest neighbor 

Notes

Acknowledgments

This work was supported in part by Research Project for FY2017 of International Association of Maritime Universities, China Postdoctoral Science Foundation under Grant 2015T80238, Natural Science Foundation of China under Grant 61401057, Natural Science Foundation of Liaoning Province under Grant 201602083, Science and technology research program of Liaoning under Grant L2014213, Dalian science and technology project under Grant 2015A11GX018, Research Funds for the Central Universities 3132016007 and 01760325. Dalian high-level innovative talent project under Grant 2016RQ035.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jian Zhao
    • 1
  • Chengzhuo Han
    • 1
  • Zhengqi Cui
    • 1
  • Rui Wang
    • 1
  • Tingting Yang
    • 1
    • 2
    Email author
  1. 1.Navigation CollegeDalian Maritime UniversityDalianChina
  2. 2.School of Electrical Engineering, IntelligentizationDongguan University of TechnologyDongguanChina

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