Analysis of an energy-efficient MAC protocol based on polling for IEEE 802.11 WLANs

Palacios, Raul; Mekonnen, Gedlu Mengistie; Alonso-Zárate, Jesús; Kliazovich, Dzmitry; Granelli, Fabrizio

doi:10.1109/icc.2015.7249269

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…The SPSM in the infrastructure Basic Service Set (BSS), where each of wireless devices is connected to AP, the WNIC of a wireless device will be in one of these two modes, awake mode or sleep mode. In the awake mode, the wireless device is fully powered and is ready to transmit or receive; in contrast, the wireless device in sleep mode is not fully powered and consumes very low power, and also cannot transmit or receive [4].…”

Section: A Review Of Commercialized Power Saving Protocols 1) Staticmentioning

confidence: 99%

Towards Optimizing WLANs Power Saving: Novel Context-Aware Network Traffic Classification Based on a Machine Learning Approach

Saeed

Kolberg

2019

IEEE Access

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Energy is a vital resource in wireless computing systems. Despite the increasing popularity of wireless local area networks (WLANs), one of the most important outstanding issues remains the power consumption caused by wireless network interface controller. To save this energy and reduce the overall power consumption of wireless devices, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on machine learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed context-aware listen interval power saving modes. A real-world dataset is recorded, based on nine smartphone applications' network traffic, reflecting different types of network behavior and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. The comparative results show that more than 99% of accuracy can be achieved. This paper indicates that ML classifiers are suited for classifying smartphone applications' network traffic based on the levels of interaction in the background. INDEX TERMS 802.11, energy consumption, machine learning (ML), power save mode (PSM), traffic classification, WLAN.

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Section: A Review Of Commercialized Power Saving Protocols 1) Staticmentioning

confidence: 99%

Towards Optimizing WLANs Power Saving: Novel Context-Aware Network Traffic Classification Based on a Machine Learning Approach

Saeed

Kolberg

2019

IEEE Access

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“…In the workflow of PCF mechanism [6], the destination address of DATA and CF-Poll is the same, so it can be sent as a frame at the same time. If the station transmits a simple polling frame CF-Poll, the Frame Body is 0 bytes and the frame size is 32 bytes; if the frame to be transmitted is CF-Poll + DATA, the Frame Body is a DATA byte (0~2312).…”

Section: Fig 4 Mac Frame Formatmentioning

confidence: 99%

The analysis of IEEE 802.11 PCF protocol based on LEO satellite Wi-Fi

Tao

Ren

2018

MATEC Web Conf.

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In view of the problem of long propagation delay, hidden terminals and serious user conflicts in LEO satellite Wi-Fi scenario, this paper analysis the incompatibility of 802.11 DCF protocol briefly. A scheme of PCF polling mechanism based on priority is proposed and the frame format is adaptively modified. Through MATLAB simulation, the system throughput of the original scheme and the improvement scheme over the different transmission distance and nodes are analysed. The simulation results show that the improved PCF protocol can improve the throughput under the multi-user over long distances.

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“…Qiao et al [24] analyzed the energy-efficiency of the PCF that combines transmit power control with adaptive PHY rate selection. Palacios et al [25] roughly estimated the power consumption of the PCF. Both papers assumed homogeneous saturated traffic (where each node always has packets to transmit) and did not consider the feature of PM.…”

Section: Related Workmentioning

confidence: 99%

A novel analysis of delay and power consumption for polling schemes in the IoT

Zhao

et al. 2017

Tinshhua Sci. Technol.

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In the Internet of Things (IoT), various battery-powered wireless devices are connected to collect and exchange data, and typical traffic is periodic and heterogeneous. Polling with power management is a very promising technique that can be used for communication among these devices in the IoT. In this paper, we propose a novel and scalable model to study the delay and the power consumption performance for polling schemes with power management under heterogeneous settings (particularly the heterogeneous sleeping interval). In our model, by introducing the concept of virtual polling interval, we successfully convert the considered energy-efficient polling scheme into an equivalent purely-limited vacation system. Thus, we can easily evaluate the mean and variance of the delay and the power consumption by applying existing queueing formulae, without developing a new theoretical model as required in previous works. Extensive simulations show that our analytical results are very accurate for both homogeneous and heterogeneous settings.

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Analysis of an energy-efficient MAC protocol based on polling for IEEE 802.11 WLANs

Cited by 14 publications

References 8 publications

Towards Optimizing WLANs Power Saving: Novel Context-Aware Network Traffic Classification Based on a Machine Learning Approach

Towards Optimizing WLANs Power Saving: Novel Context-Aware Network Traffic Classification Based on a Machine Learning Approach

The analysis of IEEE 802.11 PCF protocol based on LEO satellite Wi-Fi

A novel analysis of delay and power consumption for polling schemes in the IoT

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