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

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.

[1]  Robin Kravets,et al.  Event-driven, role-based mobility in disaster recovery networks , 2007, CHANTS '07.

[2]  Feng Jiang,et al.  Optimization of UAV Heading for the Ground-to-Air Uplink , 2011, IEEE Journal on Selected Areas in Communications.

[3]  Kandeepan Sithamparanathan,et al.  Optimal LAP Altitude for Maximum Coverage , 2014, IEEE Wireless Communications Letters.

[4]  Xiaohui Li,et al.  Deployment of Drone Base Stations for Cellular Communication Without Apriori User Distribution Information , 2018, 2018 37th Chinese Control Conference (CCC).

[5]  Andrey V. Savkin,et al.  Optimized Deployment of Autonomous Drones to Improve User Experience in Cellular Networks , 2017, ArXiv.

[6]  Rui Zhang,et al.  Energy-Efficient UAV Communication With Trajectory Optimization , 2016, IEEE Transactions on Wireless Communications.

[7]  Zaher Dawy,et al.  Optimized LTE Cell Planning With Varying Spatial and Temporal User Densities , 2016, IEEE Transactions on Vehicular Technology.

[8]  Xiaoyan Hong,et al.  A group mobility model for ad hoc wireless networks , 1999, MSWiM '99.

[9]  Rose Qingyang Hu,et al.  Resource Management for Heterogeneous Networks in LTE Systems , 2014, Springer Briefs in Electrical and Computer Engineering.

[10]  Mahbub Hassan,et al.  Flying Drone Base Stations for Macro Hotspots , 2018, IEEE Access.

[11]  Jeffrey G. Andrews,et al.  Towards Understanding the Fundamentals of Mobility in Cellular Networks , 2012, IEEE Transactions on Wireless Communications.

[12]  Ismail Güvenç,et al.  UAV assisted heterogeneous networks for public safety communications , 2015, 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[13]  Karina Mabell Gomez,et al.  Capacity evaluation of Aerial LTE base-stations for public safety communications , 2015, 2015 European Conference on Networks and Communications (EuCNC).

[14]  Suvadip Batabyal,et al.  Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[15]  Xu Li,et al.  Drone-assisted public safety wireless broadband network , 2015, 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[16]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[17]  N. Golmie,et al.  Nationwide Safety: Nationwide Modeling for Broadband Network Services , 2013, IEEE Vehicular Technology Magazine.

[18]  Mira Yun,et al.  Efficient Mobile Base Station Placement for First Responders in Public Safety Networks , 2019, Lecture Notes in Networks and Systems.