Delay analysis for drone-based vehicular Ad-Hoc Networks

Using Unmanned Aerial Vehicles (UAVs) or drones in Vehicular Ad-hoc Networks (VANETs) has started to attract attention. This paper proposes a mathematical framework to determine the minimum drone density (maximum separation distance between two adjacent drones) that stochastically limits the worst case for the vehicle-to-drone packet delivery delay. In addition, it proposes a drones-active service (DAS) that is added to the location service in a VANET to obtain the required number of active drones based on the current vehicular density while satisfying a probabilistic requirement for vehicle-to-drone packet delivery delay. Our goal is boosting VANET communications using infrastructure drones to achieve the minimum vehicle-to-drone packet delivery delay. We are interested in two-way highway VANET networks with low vehicular density. The simulation results show the accuracy of our mathematical framework and reflect the relation between the vehicle-to-drone packet delivery delay and the drone density.

[1]  Jörg Widmer,et al.  Hierarchical location service for mobile ad-hoc networks , 2004, MOCO.

[2]  David R. Karger,et al.  A scalable location service for geographic ad hoc routing , 2000, MobiCom '00.

[3]  Der-Jiunn Deng,et al.  Optimal Two-Lane Placement for Hybrid VANET-Sensor Networks , 2015, IEEE Transactions on Industrial Electronics.

[4]  Weihua Zhuang,et al.  Probabilistic Delay Control and Road Side Unit Placement for Vehicular Ad Hoc Networks with Disrupted Connectivity , 2011, IEEE Journal on Selected Areas in Communications.

[5]  Mehdi Bennis,et al.  Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis , 2014, GLOBECOM 2014.

[6]  Weihua Zhuang,et al.  Delay Analysis for Sparse Vehicular Sensor Networks with Reliability Considerations , 2013, IEEE Transactions on Wireless Communications.

[7]  Abbas Jamalipour,et al.  Modeling air-to-ground path loss for low altitude platforms in urban environments , 2014, 2014 IEEE Global Communications Conference.

[8]  Abderrahmane Lakas,et al.  UVAR: An intersection UAV-assisted VANET routing protocol , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[9]  Fotini-Niovi Pavlidou,et al.  Investigating a Junction-Based Multipath Source Routing Algorithm for VANETs , 2013, IEEE Communications Letters.

[10]  Xiaoying Gan,et al.  VDNet: an infrastructure-less UAV-assisted sparse VANET system with vehicle location prediction , 2016, Wirel. Commun. Mob. Comput..

[11]  Yacine Ghamri-Doudane,et al.  A Comparison of Reactive, Grid and Hierarchical Location-Based Services for VANETs , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[12]  Christian Bonnet,et al.  VanetMobiSim: generating realistic mobility patterns for VANETs , 2006, VANET '06.

[13]  Halim Yanikomeroglu,et al.  The New Frontier in RAN Heterogeneity: Multi-Tier Drone-Cells , 2016, IEEE Communications Magazine.

[14]  Lorenzo Rubio,et al.  Path Loss Characterization for Vehicular Communications at 700 MHz and 5.9 GHz Under LOS and NLOS Conditions , 2014, IEEE Antennas and Wireless Propagation Letters.

[15]  Abderrahmane Lakas,et al.  CRUV: Connectivity-based traffic density aware routing using UAVs for VANets , 2015, 2015 International Conference on Connected Vehicles and Expo (ICCVE).

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

[17]  Antonella Molinaro,et al.  Multichannel communications in vehicular Ad Hoc networks: a survey , 2013, IEEE Communications Magazine.

[18]  Sidi-Mohammed Senouci,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .