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  5. An Adaptive Channel Division MAC Protocol for High Dynamic UAV Networks

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Article
English
2020

An Adaptive Channel Division MAC Protocol for High Dynamic UAV Networks

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English
2020
IEEE Sensors Journal
DOI: 10.1109/jsen.2020.2987525

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Rui Zhang
Rui Zhang

The Chinese University of Hong Kong

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Yuhan Ruan
Yi Zhang
Yongzhao Li
+2 more

Abstract

Nowadays, unmanned aerial vehicle (UAV) has captured great attentions for its versatility, flexibility, and low-cost. In this paper, we consider a high altitude platform (HAP) assisted UAV network towards Internet of Things (IoT), where high dynamic UAVs can transmit real-time IoT data to remote ground control center via the HAP. Because of the movement characteristic of UAVs, the number of UAVs within the HAP coverage and the traffic load change frequently, which poses new challenges for the Media Access Control (MAC) protocol in this network. To make full use of the channel resources and guarantee the communication performance of multiple UAVs, we firstly propose an adaptive channel division MAC (ACD-MAC) protocol for this high dynamic UAV network, where the relative length of control channel interval to service channel interval, denoted by channel allocation parameter, can be flexibly adjusted according to the number of UAVs and traffic load. Then, based on a Markov model, we give an algorithm to calculate the optimal channel allocation parameter in the proposed ACD-MAC protocol. Finally, simulation results show that the proposed ACD-MAC protocol can achieve a lower end-to-end delay and a higher throughput performance in such a high dynamic UAV network than the traditional fixed channel division MAC protocol.

How to cite this publication

Yuhan Ruan, Yi Zhang, Yongzhao Li, Rui Zhang, Rongnan Hang (2020). An Adaptive Channel Division MAC Protocol for High Dynamic UAV Networks. IEEE Sensors Journal, pp. 1-1, DOI: 10.1109/jsen.2020.2987525.

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Publication Details

Type

Article

Year

2020

Authors

5

Datasets

0

Total Files

0

Language

English

Journal

IEEE Sensors Journal

DOI

10.1109/jsen.2020.2987525

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