The Target Wake Time (TWT) feature introduced in 802.11ax can be employed to achieve higher power efficiency, reduced channel access contention, and lower network congestion by scheduling clients' transmissions. Despite these potential benefits, because TWT allows communication during service periods only, it can negatively impact the Transmission Control Protocol (TCP) performance. In this paper, we identify and discuss the impact of TWT on TCP performance for RTOS and Linux-based wireless devices and show that TWT leads to unnecessary retransmissions, which wastes channel bandwidth and increases communication delay and energy consumption. To address this problem, we propose and implement a method to share TWT operational parameters with the TCP layer to adjust its packet loss detection method. Empirical evaluations across different hardware platforms confirm the effectiveness of the proposed approach in preventing premature packet loss detection and reducing unnecessary retransmissions.

Identifying IoT devices is crucial for network monitoring, security enforcement, and inventory tracking. However, most existing identification methods rely on deep packet inspection, which raises privacy concerns and adds computational complexity. Moreover, existing works overlook the impact of wireless channel dynamics on the accuracy of layer-2 features, thereby limiting their effectiveness in real-world scenarios. In this work, we define and use the latency of specific probe-response packet exchanges, referred to as "device latency," as the main feature for device identification. Additionally, we reveal the critical impact of wireless channel dynamics on the accuracy of device identification based on device latency features. Specifically, this work introduces "accumulation score" as a novel approach to capturing fine-grained channel dynamics and their impact on device latency when training machine learning models. We implement the proposed methods and measure the accuracy and overhead of device identification in real-world scenarios. The results confirm that by incorporating the accumulation score for balanced data collection and training machine learning algorithms, we achieve an F1 score of over 97% for device identification, even amidst wireless channel dynamics, a significant improvement over the 75% F1 score achieved by disregarding the impact of channel dynamics on data collection and device latency.

To reduce packet collisions and enhance the energy efficiency of stations, Target Wake Time (TWT), which is a feature of the 802.11ax standard (WiFi 6), allows the allocation of communication service periods to stations. While effective TWT allocation requires characterizing the traffic pattern of stations, in this paper, we empirically study and reveal that the existing methods (i.e., channel utilization estimation, packet sniffing, and buffer status report) do not provide adequate accuracy. To remedy this problem, we propose a 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. We empirically evaluate the proposed method and confirm its superior traffic characterization performance against the existing ones. We also present a sample TWT allocation scenario that leverages the proposed method to enhance throughput.

Wireless sensor networks (WSNs) are being used in various applications, such as structural health monitoring and industrial control. Since energy efficiency is one of the major design factors, the existing WSNs primarily rely on low-power,low-ratewireless technologies, such as 802.15.4 and Bluetooth. In this article, by proposing Sensifi, we strive to tackle the challenges of developingultrahigh-rateWSNs based on the 802.11 (WiFi) standard. As an illustrative structural health monitoring application, we consider the spacecraft vibration test and identify system design requirements and challenges. Our main contributions are as follows. First, we propose packet encoding methods to reduce the overhead of assigning accurate timestamps to samples. Second, we propose energy-efficiency methods to enhance the system’s lifetime. Third, to enhance sampling rate and mitigate sampling rate instability, we reduce the overhead of processing outgoing packets through the network stack. Fourth, we study and reduce the delay of processing time synchronization packets through the network stack. Fifth, we propose a low-power node design, particularly targeting vibration monitoring. Sixth, we use our node design to empirically evaluate energy efficiency, sampling rate, and data rate. We leave large-scale evaluations as future work.

The 802.11 standard, a.k.a., WiFi, is becoming more popular for IoT connectivity. The three essential operations performed to ensure connectivity in an 802.11 network are association, maintaining association, and periodic beacon reception. Understanding and enhancing the energy efficiency of these operations is essential for building IoT systems. Unfortunately, the overheads of these operations have not been studied considering station's software and hardware configuration, access point configuration, and link unreliability. In this paper, we show that: (i) association cost depends on multiple factors including probing, key generation, operating system, and network stack, (ii) increasing listen interval to reduce beacon reception wake-up instances may negatively impact energy efficiency, (iii) maintaining association by relying on the poll messages generated by the access point is not reliable, and (iv) key renewal aggravates the chance of disassociation. We also present station- and access point-based solutions that address some of these problems.