Interference-aware self-optimizing Wi-Fi for high efficiency internet of things in dense networks

Abstract

Wi-Fi is one of the candidate technologies for the Internet of Things (IoT), and today connects billions of devices world-wide in dense networks to offer Internet connectivity in a partially or fully automated manner. In order to provide seamless and high quality service, wireless local area networks (WLANs) can adopt dynamic channel access technologies such as dynamic bandwidth or channel hopping schemes in order to avoid interference for better link quality. However, in dense networks, the dynamic channel access leads to a higher probability of adjacent channel interference (ACI). The efficiency of IEEE 802.11-based WLANs using multi-channel and wide dynamic ranges is thus severely degraded by ACIs in dense networks. In this paper, we analyze the ACI effect on WLANs and propose an interference-aware self-optimizing (IASO) Wi-Fi design that incorporates a multi-channel multi-level carrier sense and adaptive initial gain control scheme. This scheme controls carrier sensing thresholds in each band for multi-level sensors, as well as initial gains for amplifiers. The proposed scheme reduces false carrier sensing and avoids saturation of amplifiers while simultaneously improving the dynamic range of the receiver. Our prototype evaluation results demonstrate that the proposed scheme can improve the dynamic range of the receiver by approximately 45dB and 30dB for a low data rate and a high data rate mode, respectively, compared with the conventional receiver designs. Furthermore, network emulation results demonstrate that the IASO Wi-Fi can improve the average throughput, latency, and energy efficiency by approximately 32% (24%), 41% (43%), and 13% (17%), respectively, compared with the conventional receiver designs (and channel hopping techniques) in dynamically varying interfered channel conditions.