Research Article
Intelligent Channel Utilization Discovery in Drone to Drone Networks for Smart Cities
@INPROCEEDINGS{10.1007/978-3-030-63083-6_1, author={Muhammed Raşit Erol and Berk Canberk}, title={Intelligent Channel Utilization Discovery in Drone to Drone Networks for Smart Cities}, proceedings={Industrial Networks and Intelligent Systems. 6th EAI International Conference, INISCOM 2020, Hanoi, Vietnam, August 27--28, 2020, Proceedings}, proceedings_a={INISCOM}, year={2020}, month={11}, keywords={Monitoring of channel utilization IEEE 802.11 RTS/CTS Drone to drone networks Voronoi diagram NAV vectors}, doi={10.1007/978-3-030-63083-6_1} }- Muhammed Raşit Erol
Berk Canberk
Year: 2020
Intelligent Channel Utilization Discovery in Drone to Drone Networks for Smart Cities
INISCOM
Springer
DOI: 10.1007/978-3-030-63083-6_1
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.
