Abstract-We present an experimental evaluation of energy usage and performance in a wireless LAN cell based on a testbed using the 5GHz ISM band for 802.11a and 802.11n. We have taken an application-level approach, by varying the packet size and transmission rate at the protocol level and evaluating energy usage across a range of application transmission rates using both large and small packet sizes. We have observed that both the application's transmission rate and the packet size have an impact on energy efficiency for transmission in our testbed. We also included in our experiments evaluation of the energy efficiency of emulations of YouTube and Skype flows, and a comparison with Ethernet transmissions.
A four-step synthesis of the indanone
core of belzutifan (MK-6482)
is described. This route starts from the commodity raw material dihydrocoumarin
and was successfully demonstrated on a large scale to produce indanone 11 in the synthesis of belzutifan, an FDA-approved first-in-class
therapy for the treatment of patients with certain types of Von Hippel–Lindau
disease-associated tumors.
We have investigated the scope for enabling WLAN applications to manage the trade-off between performance and energy usage. We have conducted measurements of energy usage and performance in our 802.11n WLAN testbed, which operates in the 5 GHz ISM band. We have defined an effective energy usage envelope with respect to application-level packet transmission, and we demonstrate how performance as well as the effective energy usage envelope is effected by various configurations of IEEE 802.11n, including transmission power levels and channel width. Our findings show that the packet size and packet rate of the application flow have the greatest impact on applicationlevel energy usage, compared to transmission power and channel width. As well as testing across a range of packet sizes and packet rates, we emulate a Skype flow, a YouTube flow and file transfers (HTTP over Internet and local server) to place our results in context. Based on our measurements we discuss approaches and potential improvements of management in effective energy usage for the tested applications.
Radio Frequency Fingerprinting (RFF) is one of the promising passive authentication approaches for improving the security of the Internet of Things (IoT). However, with the proliferation of low-power IoT devices, it becomes imperative to improve the identification accuracy at low SNR scenarios. To address this problem, this paper proposes a general Denoising Au-toEncoder (DAE)-based model for deep learning RFF techniques. Besides, a partially stacking method is designed to appropriately combine the semi-steady and steady-state RFFs of ZigBee devices. The proposed Partially Stacking-based Convolutional DAE (PSC-DAE) aims at reconstructing a high-SNR signal as well as device identification. Experimental results demonstrate that compared to Convolutional Neural Network (CNN), PSCDAE can improve the identification accuracy by 14% to 23.5% at low SNRs (from -10 dB to 5 dB) under Additive White Gaussian Noise (AWGN) corrupted channels. Even at SNR = 10 dB, the identification accuracy is as high as 97.5%.
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