Traffic Flow Modeling for UAV-Enabled Wireless Networks

TL;DR

This paper develops a comprehensive traffic flow model for UAV-enabled wireless networks supporting multiple services, classifying services and UAVs, and validating predictions through simulations.

Contribution

It introduces a novel traffic flow model that classifies services and UAVs, and accurately predicts network traffic in UAV-enabled wireless systems.

Findings

Model accurately predicts packet numbers and data sizes.

Simulation results match real-world scenarios.

Supports multi-service UAV network planning.

Abstract

This paper investigates traffic flow modeling issue in multi-services oriented unmanned aerial vehicle (UAV)-enabled wireless networks, which is critical for supporting future various applications of such networks. We propose a general traffic flow model for multi-services oriented UAV-enable wireless networks. Under this model, we first classify the network services into three subsets: telemetry, Internet of Things (IoT), and streaming data. Based on the Pareto distribution, we then partition all UAVs into three subgroups with different network usage. We further determine the number of packets for different network services and total data size according to the packet arrival rate for the nine segments, each of which represents one map relationship between a subset of services and a subgroup of UAVs. Simulation results are provided to illustrate that the number of packets and the data size predicted by our traffic model can well match with these under a real scenario.