Date of Award

6-9-2022

Document Type

Dissertation

Publisher

Santa Clara : Santa Clara University, 2022.

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science and Engineering

First Advisor

Behnam Dezfouli

Abstract

The 802.11 standard, known as WiFi, is currently being used for a wide variety of applications including Internet of Things (IoT). However, the contention between the traffic of IoT stations (STAs) as well as the contention between these flows and regular user-generated traffic reduces the energy efficiency and timeliness of IoT communication. To remedy this problem, in this thesis, we take the following approaches for mitigating the challenges faced by WiFi-based IoT networks: First, we highlight the importance of observability with respect to WiFi networks and how it helps the researchers to better examine the dynamics of issues and its causes. We then develop two tools that enable high-rate monitoring of the Linux networking stack. These tools rely on the fact that all data traffic in WiFi networks flows through the Access Point (AP). This enables us to deploy these tools on only the APs and not each connected device; thus enabling monitoring of large-scale networks. Second, we enhance this tool by utilizing the extended Berkeley Packet Filter (eBPF) technology for monitoring of the networks without modifying any kernel modules to analyze the delays incurred by the packets at different parts of the networking subsystem on the APs and also monitor the energy consumption of the associated STAs. Third, utilizing these tools, we obtain insights and measurements to design a scheduling algorithm that computes per-packet priorities to arbitrate the contention between the transmission of IoT packets. This algorithm employs a least-laxity first (LLF) scheme that assigns priorities based on the remaining wake-up time of the destination STAs. Fourth, we estimate the interval uplink-request and downlink-response due to overheads in the wired and wireless networking components across the path of the packet from the edge device and server. We facilitate the STA with the estimated wired and wireless transit delay, such that the STA can utilize this information to transition to low power sleep state during the packet’s transit; thus, enhancing the STAs energy efficiency. Fifth, focusing on the power-save functionality introduced in next-generation WiFi standard, known as Target Wake-up Time (TWT), we first, highlight the importance of traffic characterization and shortcomings of existing methods. We then propose a transport layer-based traffic characterization method that can accurately capture inter-packet and inter-burst intervals on a per-flow basis in the presence of factors such as channel access and packet preparation delay. By addressing the challenges due to the shared nature of WiFi spectrum the contributions in this thesis provide open-source tools for better understanding the internals of networking stack and methods for improving the energy efficiency and quality of service of WiFi communication in IoT networks.

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