Towards End-User Driven Power Saving Control in Android Devices

Bernardo, Vitor; Correia, Bruno; Curado, Marília; Braun, Torsten

doi:10.1007/978-3-319-10353-2_20

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Power saving mechanisms can also be deployed on Android smart phones. Bernardo et al [ 69 ] presented an end user power saving control in Android devices in which they extended Android framework for power saving control and moved that control to end users. The methodology provides end users with the application control of the IEEE 802.11 interface power management so that different energy saving schemes can be implemented.…”

Section: Energy Conserving Solutions In Wireless Networking-based mentioning

confidence: 99%

A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects

Abbas

Yoon

2015

Sensors

109

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The Internet of Things (IoT) is an emerging key technology for future industries and everyday lives of people, where a myriad of battery operated sensors, actuators, and smart objects are connected to the Internet to provide services such as mobile healthcare, intelligent transport system, environmental monitoring, etc. Since energy efficiency is of utmost importance to these battery constrained IoT devices, IoT-related standards and research works have focused on the device energy conserving issues. This paper presents a comprehensive survey on energy conserving issues and solutions in using diverse wireless radio access technologies for IoT connectivity, e.g., the 3rd Generation Partnership Project (3GPP) machine type communications, IEEE 802.11ah, Bluetooth Low Energy (BLE), and Z-Wave. We look into the literature in broad areas of standardization, academic research, and industry development, and structurally summarize the energy conserving solutions based on several technical criteria. We also propose future research directions regarding energy conserving issues in wireless networking-based IoT.

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Section: Energy Conserving Solutions In Wireless Networking-based mentioning

confidence: 99%

A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects

Abbas

Yoon

2015

Sensors

109

View full text Add to dashboard Cite

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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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Towards Optimizing WLANs Power Saving: Context-Aware Listen Interval

Saeed

Kolberg

2021

IEEE Access

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Towards End-User Driven Power Saving Control in Android Devices

Cited by 3 publications

References 11 publications

A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects

A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects

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

Towards Optimizing WLANs Power Saving: Context-Aware Listen Interval

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