Clock Drift Impact on Target Wake Time in IEEE 802.11ax/ah Networks

Bankov, Dmitry; Khorov, Evgeny; Lyakhov, Andrey; Stepanova, Ekaterina

doi:10.1109/ent-mipt.2018.00014

Cited by 12 publications

(13 citation statements)

References 11 publications

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“…This allows them to ignore intermediary beacons and save significant amounts of energy if they transmit sporadically. However, problems as the clock drift phenomenon can arise [30], which we leave out of consideration in this study.…”

Section: Results and Evaluationmentioning

confidence: 99%

Accurate Energy Modeling and Characterization of IEEE 802.11ah RAW and TWT

Santi

Tian

Khorov

et al. 2019

Sensors

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Minimizing the energy consumption is one of the main challenges in internet of things (IoT) networks. Recently, the IEEE 802.11ah standard has been released as a new low-power Wi-Fi solution. It has several features, such as restricted access window (RAW) and target wake time (TWT), that promise to improve energy consumption. Specifically, in this article we study how to reduce the energy consumption thanks to RAW and TWT. In order to do this, we first present an analytical model that calculates the average energy consumption during a RAW slot. We compare these results to the IEEE 802.11ah simulator that we have extended for this scope with an energy life-cycle model for RAW and TWT. Then we study the energy consumption under different conditions using RAW. Finally, we evaluate the energy consumption using TWT. In the results, we show that the presented model has a maximum deviation from the simulations of 10% in case of capture effect (CE) and 7% without it. RAW always performs better than carrier-sense multiple access with collision avoidance (CSMA/CA) when the traffic is higher and the usage of more slots has showed to have better energy efficiency, of up to the 76%, although also significantly increasing the latency. We will show how TWT outperforms pure RAW, by over 100%, when the transmission time is over 5 min.

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Section: Results and Evaluationmentioning

confidence: 99%

Accurate Energy Modeling and Characterization of IEEE 802.11ah RAW and TWT

Santi

Tian

Khorov

et al. 2019

Sensors

Self Cite

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“…where V is a control parameter between the queue delay and the average system penalty. Now, in the Lyapunov optimization framework, we turn our energy minimization and network stability problem to be the minimization of (11). Instead of directly attacking to minimize (11), in the following Lemma we first derive an upper bound on ( 11) and then we propose our algorithm that minimizes the bound.…”

Section: Joint Twt Interval and Scheduling Algorithm (Jtwsa)mentioning

confidence: 99%

“…Also, a cloud-based centralized management is proposed for the energy saving of a dense network with many 802.11ax based APs in [10]. The "clock drift" problem which is the misalignment of the scheduling times of STAs within TWT mechanism and its effect on the power consumption is investigated in [11]. The most relevant studies to our work are found in [6,12,13].…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of target wake time mechanism and scheduling for IEEE 802.11ax

Karaca¹

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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IEEE 802.11ax as the newest wireless local area networks (WLANs) standard brings enormous improvements in network throughput, coverage and energy efficiency in densely populated areas. Unlike previous IEEE 802.11 WLAN standards where power saving mechanisms have a limited capability and flexibility, 802.11ax comes with a different mechanism called target wake time (TWT) where stations (STAs) wake up only after each TWT interval and different STAs can wake up at different time instance depending on their application requirements. As an example, for a periodic data arrival occurring in IoT applications, STA can wake up by following the data period and go to sleep mode for a much longer time, and STAs with high traffic volume can have shorter TWT interval to wake up more frequently. Moreover, as multiuser transmission capability is added to 802.11ax, multiple STAs can have the same TWT interval and wake up at the same time, and hence there is a great opportunity to have collision-free transmission by scheduling multiple STAs on appreciate TWT intervals to reduce energy consumption and also increase network throughput. In this paper, we investigate the problem of STAs scheduling and TWT interval assignment together to reduce overall energy consumption of the network. We propose an algorithm that dynamically selects STAs to be served and assigns the most suitable TWT interval given STAs' traffic and channel conditions. We analyze our algorithm through Lyapunov optimization framework and show that our algorithm is arbitrarily close to the optimal performance at the price of increased queue sizes.Simulation results show that our algorithm consumes less power and support higher traffic compared to a benchmark algorithm that operates randomly for TWT assignment.

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“…There are very few articles that study the efficiency of TWT as a power-saving mechanism in IEEE 802.11ax [13]. At the same time, many articles focus on TWT usage as a part of scheduled access in Wi-Fi networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…With the TWT with guard interval (GI) method, the AP groups the sensors' wake-up times and uses a guard interval to protect all sensor transmissions in order to reduce the energy consumption caused by the contention for the channel. A detailed study of this case has been presented in [13], where it is shown that for the best performance, the AP should separate sensors' wake-up times by a double successful data exchange duration. Thus with this method, the AP assigns the sensors' TWTs in such a way that the interval between two successive TWTs equals twice the successful data exchange.…”

Section: Scenariomentioning

confidence: 99%

On the Joint Usage of Target Wake Time and 802.11ba Wake-Up Radio

et al. 2020

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Energy efficiency is a significant challenge for modern wireless networking devices. It is crucial for the Internet of Things devices, is required for battery-supplied user devices such as smartphones, and is advisable for high-performance devices such as wireless VR headsets. This paper examines the ability of modern Wi-Fi devices to achieve extremely low power consumption when they rarely send and receive data. Two recently developed mechanisms, namely Target Wake Time (TWT) and Wake-Up Radio (WUR), are studied. The first one allows stations to schedule the frame exchanges in advance, while the second one introduces a low-power radio for control information exchange. Although TWT and WUR differ significantly, they both suffer from the clock drift effect that significantly degrades their performance in the case of rare traffic. The paper describes these mechanisms, focusing on their revolutionary features, and presents mathematical models to evaluate the impact of this effect on TWT and WUR mechanism efficiency in terms of energy and channel time consumption. The paper also proposes and thoroughly examines various approaches to the joint and separate usage of TWT and WUR in Wi-Fi networks.

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Clock Drift Impact on Target Wake Time in IEEE 802.11ax/ah Networks

Cited by 12 publications

References 11 publications

Accurate Energy Modeling and Characterization of IEEE 802.11ah RAW and TWT

Accurate Energy Modeling and Characterization of IEEE 802.11ah RAW and TWT

Joint optimization of target wake time mechanism and scheduling for IEEE 802.11ax

On the Joint Usage of Target Wake Time and 802.11ba Wake-Up Radio

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