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Open AccessArticle

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

by 1, 1, 1,*,† and 2,*,†
School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, Korea
Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(10), 3364;
Received: 15 August 2018 / Revised: 5 October 2018 / Accepted: 5 October 2018 / Published: 9 October 2018
(This article belongs to the Special Issue Future Research Trends in Internet of Things and Sensor Networks)
The increasing use of Internet of Things (IoT) devices in specific areas results in an interference among them and the quality of communications can be severely degraded. To deal with this interference issue, the IEEE 802.11ax standard has been established in hyper-dense wireless networking systems. The 802.11ax adopts a new candidate technology that is called multiple network allocation vector in order to mitigate the interference problem. In this paper, we point out a potential problem in multiple network allocation vector which can cause delays to communication among IoT devices in hyper-dense wireless networks. Furthermore, this paper introduces an adaptive beam alignment algorithm for interference resolution, and analyzes the potential delays of communications among IoT devices under interference conditions. Finally, we simulate our proposed algorithm in densely deployed environments and show that the interference can be mitigated and the IEEE 802.11ax-based IoT devices can utilize air interface more fairly compared to conventional IEEE 802.11 distributed coordination function. View Full-Text
Keywords: IEEE 802.11ax; multiple NAVs; Internet-of-Things (IoT); beamforming IEEE 802.11ax; multiple NAVs; Internet-of-Things (IoT); beamforming
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Kwon, D.; Kim, S.-W.; Kim, J.; Mohaisen, A. Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks. Sensors 2018, 18, 3364.

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