A hardware and software Web-based environment for Energy Consumption analysis in mobile devices

2016 ◽  
Author(s):  
Sidartha A. L. Carvalho ◽  
Rafael N. Lima ◽  
Daniel C. Cunha ◽  
Abel G. Silva-Filho
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Web Based ◽  
Energy Consumption Analysis

scholarly journals An Energy Efficiency Study of Web-Based Communication in Android Phones

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Inmaculada Ayala ◽  
Mercedes Amor ◽  
Lidia Fuentes
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Energy Demand ◽  
Web Applications ◽  
Asynchronous Communication ◽  
Mobile App ◽  
Web Based ◽  
Mobile Web ◽  
The Impact ◽  
Web Developers

Currently, mobile devices are the most popular pervasive computing devices, and they are becoming the primary way for accessing Internet. Battery is a critical resource in such personal computing gadgets, network communications being one of the primary energy consuming activities in any mobile app. Indeed, as web-based communication is the most used explicitly or implicitly by mobile devices, HTTP-based traffic is the most power demanding one. So, mobile web developers should be aware of how much energy demands the different web-based communication alternatives. The goal of this paper is to measure and compare the energy consumption of three asynchronous HTTP-based methods in mobile devices in different browsers. Our experiments focus on three HTTP-based asynchronous communication models that allow a web server to push data to a client browser through a HTTP/1.1 interaction: Polling, Long Polling, and WebSockets. The resulted measurements are then analysed to get more accurate understanding of the impact of the selected method, and the mobile browser, in the energy consumption of the asynchronous HTTP-based communication. The utility of these experiments is to show developers what are the factors and settings that mostly influence the energy consumption when different web-based asynchronous communication methods are used, helping them to choose the most beneficial solution if possible. With this information, mobile web developers should be able to reduce the power consumption of the front-end of web applications for mobile devices, just selecting and configuring the best asynchronous method or mobile browser, improving the performance of HTTP-based communication in terms of energy demand.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

scholarly journals Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1800
Author(s):  
Linfei Hou ◽  
Fengyu Zhou ◽  
Kiwan Kim ◽  
Liang Zhang
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Mobile Robot ◽  
Load Capacity ◽  
Working Environment ◽  
Energy Consumption Analysis ◽  
Mecanum Wheel ◽  
Save Energy ◽  
Energy Consumption Modeling ◽  
Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

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