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Performance Analysis of UAV Routing Protocol Based on Mobility Models

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Recent Trends in Communication and Intelligent Systems (ICRTCIS 2023)

Abstract

The communication between the flying birds or unmanned aerial vehicles (UAVs) a.k.a Flying Adhoc Network (FANET), is the most challenging task in an extremly vibrant environment. To overcome the problem of data dissemination in changed topology, many routing protocols are investigated and modified. The stable path and congestion avoidance are the evolving area in FANET. This paper investigates preliminary UAV routing protocol with different mobility models to examine the factors such as latency, average end-to-end (E2E) delay, packet delivery ratio (PDR), etc. in dynamic environment with numerous node count and node speed variations. The simulation results presented here show that increasing the node count from 0 to 49 keeping constant velocity of 20 m/sec reflects that throughput of Optimized Link State Routing (OLSR) is 1.7 kbps using Gauss Markov (GM) mobility model (MM), which is higher than throughput using Random Way-point (RWP) MM. For the same simulation condition, a lower average E2E delay of 0.44 ms for OLSR is achieved using GM MM. However, by increasing the node speed in the range of 20 m/sec to 100 m/sec for 50 nodes, the average throughput for Ad Hoc On-Demand Distance Vector (AODV) protocol increased to 28 kbps at 75 m/sec, which is higher than OLSR using RWP MM. The simulation achieves an end-to-end delay of 600 ms and 790 ms at 50 m/sec for AODV protocol using RWP MM and GM MM, respectively. Thus, the experiment demonstrates that GM MM with variable nodes and node speed performs better than RWP MM.

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Acknowledgements

The authors of this paper are thankful to L&T Mumbai, India, for their support in this presented research work.

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Correspondence to Kanchan Vipul Bakade .

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Bakade, K.V., More, A. (2023). Performance Analysis of UAV Routing Protocol Based on Mobility Models. In: Pundir, A.K.S., Yadav, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. ICRTCIS 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-5792-7_1

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