scholarly journals A Tool for Energy Consumption Monitoring and Analysis of the Android Terminal

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Deguang Li ◽  
Zhanyou Cui ◽  
Chenguang Bai ◽  
Qiurui He ◽  
Xiaoting Yan
Keyword(s):  
Power Consumption ◽  
Rapid Development ◽  
Mobile Terminal ◽  
Power Model ◽  
Power Modeling ◽  
Application Layer ◽  
Consumption Data ◽  
Android Platform ◽  
Instrument Measurement ◽  
Original Information

With the rapid development of communication technology, the intelligent mobile terminal brings about great convenience to people’s life with rich applications, while its power consumption has become a great concern to researchers and consumers. Power modeling is the basis to understand and analyze the power consumption characteristics of the terminal. In this paper, we analyze the Bluetooth and hidden power consumption of the android platform and fix the power model of open-source Android platform. Then, a power consumption monitoring tool is implemented based on the model; the tool is divided into three layers, which are original information monitor layer, power consumption calculation layer, and application layer. The original monitor layer gets the power consumption data and running time of the different components under different states, the calculation layer calculates the power consumption of each hardware and each application based on the power model of each component, and the application layer displays the real-time power consumption of the software and hardware. Finally, we test our tool in real environment by using Xiaomi 9 Pro and perform comparison with actual instrument measurement; the error between the monitored value and the measured value is less than 5%.

Power Modeling Framework for an Asynchronous Processor

2016 ◽  
Vol 25 (06) ◽  
pp. 1650057
Author(s):  
Je-Hoon Lee
Keyword(s):  
Power Consumption ◽  
High Accuracy ◽  
The Other ◽  
Power Model ◽  
Power Modeling ◽  
Modeling Framework ◽  
Low Level ◽  
Architectural Model ◽  
Power Models

This paper presents two power models for an asynchronous processor, A8051. The first one is a pipeline accurate model which models power consumption at each pipeline stage. The other one is a micro-architectural model which models power consumption at micro-operation level. Then, we demonstrate the feasibility of the proposed approach on an A8051 processor case study. The experimental results based on applying the proposed pipeline-accurate and micro-architectural power models on an A8051 processor demonstrate that the proposed power models have high accuracy with simulation times much faster than the conventional low-level power simulator. It also shows similar results compared to the conventional power model for a synchronous processor. Even though the simulation speeds for the proposed power models are approximately 100–900 times faster than the low-level power simulator, the differences are less than 18% and 15%, respectively. Thus, the proposed power models can give a guide for SoC designers who want to integrate the asynchronous processor for low-power SoC design.

scholarly journals Phase-Based Accurate Power Modeling for Mobile Application Processors

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1197
Author(s):  
Kitak Lee ◽  
Seung-Ryeol Ohk ◽  
Seong-Geun Lim ◽  
Young-Jin Kim
Keyword(s):  
Power Consumption ◽  
Mobile Application ◽  
Total Power ◽  
Average Error ◽  
Power Model ◽  
Power Modeling ◽  
Estimation Errors ◽  
Total Power Consumption ◽  
Battery Capacity ◽  
Power Models

Modern mobile application processors are required to execute heavier workloads while the battery capacity is rarely increased. This trend leads to the need for a power model that can analyze the power consumed by CPU and GPU at run-time, which are the key components of the application processor in terms of power savings. We propose novel CPU and GPU power models based on the phases using performance monitoring counters for smartphones. Our phase-based power models employ combined per-phase power modeling methods to achieve more accurate power consumption estimations, unlike existing power models. The proposed CPU power model shows estimation errors of 2.51% for ARM Cortex A-53 and 1.97% for Samsung M1 on average, and the proposed GPU power model shows an average error of 8.92% for the Mali-T880. In addition, we integrate proposed CPU and GPU models with the latest display power model into a holistic power model. Our holistic power model can estimate the smartphone′s total power consumption with an error of 6.36% on average while running nine 3D game benchmarks, improving the error rate by about 56% compared with the latest prior model.

scholarly journals Goals and measures for analyzing power consumption data in manufacturing enterprises

2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Manufacturing Industry ◽  
Fog Computing ◽  
Electrical Power ◽  
Time Data ◽  
Manufacturing Enterprises ◽  
Computing Paradigm ◽  
Consumption Data ◽  
Analyzing Power

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

Research on the Visualization of Living Conditions from the Power Consumption Data using the ECHONET Lite Standard

2021 ◽  
Author(s):  
Takahiro Sakai ◽  
Ryuta Imanishi ◽  
Shouma Yasuda ◽  
Hiroshi Sugimura ◽  
Masao Isshiki
Keyword(s):  
Power Consumption ◽  
Living Conditions ◽  
Consumption Data
Sign in / Sign up

Export Citation Format

Share Document