Abstract
Drone networks are playing a significant role in a wide variety of applications such as the delivery of goods, surveillance, search and rescue missions, etc. The development of the drone to drone (D2D) networks can increase the success of these applications. One way of improving D2D network performance is the monitoring of the channel utilization of the link between drones. There are many works about monitoring channel utility; however, either they sense channel physically, which is not reliable and effective due to noise in the channel and miss-sense of signals, or they have protocol-based solutions with high time-complexity. Hence, we propose a less time and power-consuming MAC layer protocol based monitoring model, which works on the IEEE 802.11 RTS/CTS protocol for D2D communication. We work on this protocol because it solves the hidden terminal problem, which can be seen widely in drone communication due to the characteristics of wireless networks and mobility of drones. Our model consists of Searching & Finding and Functional Sub-layers. In the Searching & Finding Sub-layer, we locate the other drones in the air with a specific flying pattern; we also sense and collect frame information on the channel. With a Functional Sub-layer, we calculate channel utilization with Network Allocation Vector (NAV) vector sizes, showing the duration of the drone about how long it must defer from accessing the link. Also, we create a visualization map with Voronoi Diagram. In that diagram, according to drone coordinates, each region is generated after the k-means clustering algorithm, which is one of the simplest and popular unsupervised machine learning algorithms. Hence, each Voronoi section shows channel utility in terms of percentage in a more precise and discretized way. Furthermore, with our model, we decrease the sensing time of the channel by about 25%, and we reduce the power consumption of sensing drone approximately 26%. Also, our model uses about 57% less area during the calculation phase.
Original language | English |
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Title of host publication | Industrial Networks and Intelligent Systems - 6th EAI International Conference, INISCOM 2020, Proceedings |
Editors | Nguyen-Son Vo, Van-Phuc Hoang |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 3-18 |
Number of pages | 16 |
ISBN (Print) | 9783030630829 |
DOIs | |
Publication status | Published - 2020 |
Event | 6th EAI International Conference on Industrial Networks and Intelligent Systems, INISCOM 2020 - Hanoi, Viet Nam Duration: 24 Aug 2020 → 28 Aug 2020 |
Publication series
Name | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST |
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Volume | 334 |
ISSN (Print) | 1867-8211 |
Conference
Conference | 6th EAI International Conference on Industrial Networks and Intelligent Systems, INISCOM 2020 |
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Country/Territory | Viet Nam |
City | Hanoi |
Period | 24/08/20 → 28/08/20 |
Bibliographical note
Publisher Copyright:© 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
Funding
Acknowledgements. The paper is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) 1001 The Scientific and Technological Research Projects Funding Program with project number 119E434.
Funders | Funder number |
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TUBITAK | 119E434 |
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu |
Keywords
- Drone to drone networks
- IEEE 802.11 RTS/CTS
- Monitoring of channel utilization
- NAV vectors
- Voronoi diagram