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Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah.

ABSTRACT: IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.

SUBMITTER: Tian L

PROVIDER: S-EPMC5539835 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:PurposeTo determine the feasibility of using real-time fluoroscopic tracking for bolus-chase magnetic resonance (MR) angiography of peripheral vasculature to image three stations from the aortoiliac bifurcation to the pedal arteries.Materials and methodsThis prospective study was institutional review board approved and HIPAA compliant. Eight healthy volunteers (three men; mean age, 48 years; age range, 30-81 years) and 13 patients suspected of having peripheral arterial disease (five men; mean age, 67 years; age range, 47-81 years) were enrolled and provided informed consent. All subjects were imaged with the fluoroscopic tracking MR angiographic protocol. Ten patients also underwent a clinical computed tomographic (CT) angiographic runoff examination. Two readers scored the MR angiographic studies for vessel signal intensity and sharpness and presence of confounding artifacts and venous contamination at 35 arterial segments. Mean aggregate scores were assessed. The paired MR angiographic and CT angiographic studies also were scored for visualization of disease, reader confidence, and overall diagnostic quality and were compared by using a Wilcoxon signed rank test.ResultsReal-time fluoroscopic tracking performed well technically in all studies. Vessel segments were scored good to excellent in all but the following categories: For vessel signal intensity and sharpness, the abdominal aorta, iliac arteries, distal plantar arteries, and plantar arch were scored as fair to good; and for presence of confounding artifacts, the abdominal aorta and iliac arteries were scored as fair. The MR angiograms and CT angiograms did not differ significantly in any scoring category (reader 1: P = .50, .39, and .39; reader 2: P = .41, .61, and .33, respectively). CT scores were substantially better in 20% (four of 20) and 25% (five of 20) of the pooled evaluations for the visualization of disease and overall image quality categories, respectively, versus 5% (one of 20) for MR scores in both categories.ConclusionThree-station bolus-chase MR angiography with real-time fluoroscopic tracking provided high-spatial-resolution arteriograms of the peripheral vasculature, enabled precise triggering of table motion, and compared well with CT angiograms.

Project description:IntroductionTo mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.MethodsWe used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.ResultsMobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.ConclusionUsing crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

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Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah.

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