Energy consumption analysis of cloud-based video games streaming to mobile devices

2015 ◽  
Author(s):  
Ioana Ghergulescu ◽  
Arghir-Nicolae Moldovan ◽  
Cristina Hava Muntean
Keyword(s):  
Energy Consumption ◽  
Video Games ◽  
Mobile Devices ◽  
Energy Consumption Analysis

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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