Cognitive Radio-Oriented Wireless Networks. 14th EAI International Conference, CrownCom 2019, Poznan, Poland, June 11–12, 2019, Proceedings

Research Article

Dynamic Placement Algorithm for Multiple Classes of Mobile Base Stations in Public Safety Networks

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  • @INPROCEEDINGS{10.1007/978-3-030-25748-4_9,
        author={Chen Shen and Mira Yun and Amrinder Arora and Hyeong-Ah Choi},
        title={Dynamic Placement Algorithm for Multiple Classes of Mobile Base Stations in Public Safety Networks},
        proceedings={Cognitive Radio-Oriented Wireless Networks. 14th EAI International Conference, CrownCom 2019, Poznan, Poland, June 11--12, 2019, Proceedings},
        proceedings_a={CROWNCOM},
        year={2019},
        month={8},
        keywords={Mobile base station placement Adhoc public safety networks 5G LTE},
        doi={10.1007/978-3-030-25748-4_9}
    }
    
  • Chen Shen
    Mira Yun
    Amrinder Arora
    Hyeong-Ah Choi
    Year: 2019
    Dynamic Placement Algorithm for Multiple Classes of Mobile Base Stations in Public Safety Networks
    CROWNCOM
    Springer
    DOI: 10.1007/978-3-030-25748-4_9
Chen Shen1,*, Mira Yun2,*, Amrinder Arora1,*, Hyeong-Ah Choi1,*
  • 1: The George Washington University
  • 2: Wentworth Institute of Technology
*Contact email: shenchen@gwu.edu, yunm@wit.edu, amrinder@gwu.edu, hchoi@gwu.edu

Abstract

As new mobile base stations (mBSs) have been constantly developed with various capacities, mobile coverage, and mobility models, the level of heterogeneity in public safety networks (PSNs) has been increasing. Since disasters and emergencies require the ad hoc PSN deployments, dynamic mBS placement and movement algorithm is one of the most important decisions to provide the critical communication channels for first responders (FRs). In this paper, we propose a heterogeneous mBS placement algorithm in an ad hoc public safety network. We define different classes of mobile base stations that have varying performance characteristics and consider three different FRs mobility models. Our proposed algorithm applies the modern clustering technique to deal with the characteristics of different kinds of mBSs.