Multi-channel IEEE 802.11 Mac layer scheduling for ultra-dense IoT networks.

Abstract

Over the last decades, wireless networks have experienced major growth in their enabling technologies and global penetration. One of the more recent components of this evolution is the Internet of Things (IoT), where different types of everyday objects are connected to the internet for purposes such as sensing and monitoring with applications that span a wide range of vertical sectors such as e-health, environment, and transportation. The IoT paradigm is seeing an exponential growth and is predicted to reach tens of billions of devices by 2030. To meet the high IoT connectivity demands, the IEEE 802.11 WiFi protocol is constantly being improved, yet performance bottlenecks still exist especially in ultra-dense network scenarios such as concerts, sports events, exhibitions, etc. In this thesis, we propose a practical approach that is compliant with the IEEE 802.11 standard to accommodate periodic IoT traffic in scenarios with very large number of users and critical delay-sensitive application data. The proposed approach combines data-control channel separation with optimized time division scheduling, including an extension to maintain a certain level of fairness among different IoT devices. Using NS-3 network simulation, the proposed approach is shown to achieve notable performance gains compared to the IEEE 802.11n WiFi standard in various scenarios with different system and design parameters.