An evolutionary approach for video application energy consumption estimation in mobile devices

2019 ◽  
Author(s):  
Irandir O. de Amorim ◽  
Jose F. V. de Melo ◽  
Andson M. Balieiro ◽  
Bruno B. dos Santos
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Evolutionary Approach ◽  
Video Application

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 Studying the Energy Consumption in Mobile Devices

2016 ◽  
Vol 94 ◽  
pp. 183-189 ◽  
Author(s):  
Mohammad Tawalbeh ◽  
Alan Eardley ◽  
Lo’ai Tawalbeh
Keyword(s):  
Energy Consumption ◽  
Mobile Devices

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

Optimization of Energy Consumption of Mobile Devices

2021 ◽  
Author(s):  
Khalil Ibrahim Hamzaoui ◽  
Mohammed Gabli ◽  
Anas Mokhtari ◽  
Soufiane Dahmani
Keyword(s):  
Energy Consumption ◽  
Mobile Devices

Energy Consumption Optimization by Increasing The Processor Speed of Moving Communication Devices in Transport Layer Protocol

2019 ◽  
Vol 16 (2) ◽  
pp. 30
Author(s):  
Fakhrur Razi ◽  
Ipan Suandi ◽  
Fahmi Fahmi
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Device ◽  
Packet Loss ◽  
Video Data ◽  
Transmission Delay ◽  
Transport Layer ◽  
Tcp Protocol ◽  
Transport Layer Protocol ◽  
Processor Speed

The energy efficiency of mobile devices becomes very important, considering the development of mobile device technology starting to lead to smaller dimensions and with the higher processor speed of these mobile devices. Various studies have been conducted to grow energy-aware in hardware, middleware and application software. The step of optimizing energy consumption can be done at various layers of mobile communication network architecture. This study focuses on examining the energy consumption of mobile devices in the transport layer protocol, where the processor speed of the mobile devices used in this experiment is higher than the processor speed used in similar studies. The mobile device processor in this study has a speed of 1.5 GHz with 1 GHz RAM capacity. While in similar studies that have been carried out, mobile device processors have a speed of 369 MHz with a RAM capacity of less than 0.5 GHz. This study conducted an experiment in transmitting mobile data using TCP and UDP protocols. Because the video requires intensive delivery, so the video is the traffic that is being reviewed. Energy consumption is measured based on the amount of energy per transmission and the amount of energy per package. To complete the analysis, it can be seen the strengths and weaknesses of each protocol in the transport layer protocol, in this case the TCP and UDP protocols, also evaluated the network performance parameters such as delay and packet loss. The results showed that the UDP protocol consumes less energy and transmission delay compared to the TCP protocol. However, only about 22% of data packages can be transmitted. Therefore, the UDP protocol is only effective if the bit rate of data transmitted is close to the network speed. Conversely, despite consuming more energy and delay, the TCP protocol is able to transmit nearly 96% of data packets. On the other hand, when compared to mobile devices that have lower processor speeds, the mobile devices in this study consume more energy to transmit video data. However, transmission delay and packet loss can be suppressed. Thus, mobile devices that have higher processor speeds are able to optimize the energy consumed to improve transmission quality.Key words: energy consumption, processor, delay, packet loss, transport layer protocol

On Balancing Energy Consumption, Rendering Speed, and Image Quality on Mobile Devices

2012 ◽  
pp. 231-250
Author(s):  
Fan Wu ◽  
Emmanuel Agu ◽  
Clifford Lindsay ◽  
Chung-han Chen
Keyword(s):  
Energy Consumption ◽  
Image Quality ◽  
Mobile Devices ◽  
Time Frame ◽  
Operating Conditions ◽  
Mobile Graphics ◽  
Balancing Energy ◽  
Interactive Frame ◽  
Battery Energy ◽  
Predict Model

Mobile games and graphics are popular because un-tethered computing is convenient and ubiquitous entertainment is compelling. However, rendering graphics on mobile devices faces challenges due to limited system resources, such as battery energy, and low memory and disk space. Real time frame rates, low energy consumption and high image quality are all desirable attributes of interactive mobile graphics; however, achieving these objectives is conflicting. For instance, increasing mesh resolutions improves rendered image quality but consumes more battery energy. Therefore, the authors propose a mobile graphics heuristic to minimize energy consumption while maintaining acceptable image quality and interactive frame rates. Over the lifetime of a mobile graphics application, scene complexity, animation paths, user interactivity and other elements all change its CPU and resource demands. In this regard, a heuristic that dynamically changes scene mesh LoDs and amount of CPU timeslices allotted to the mobile graphics application is presented to select optimal operating conditions that balance rendering speed, energy conservation and image quality. Additionally, a workload predict model is proposed so that the heuristic can monitor both application workload and the availability of resources of mobile devices periodically, while adaptively determining how much resources will be allocated to applications.

Designing Mobile Collaborative Applications for Cloud Environments

2016 ◽  
pp. 34-60 ◽  
Author(s):  
Nadir Guetmi ◽  
Abdessamad Imine
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Mobile Devices ◽  
Design Patterns ◽  
Self Control ◽  
Network Coverage ◽  
Collaborative Editing ◽  
Collaborative Applications ◽  
Cloud Environments ◽  
Continuous Use

Mobile devices have experienced a huge progress in the capacity of computing, storage and data visualization. They are becoming the device of choice for operating a large variety of applications while supporting real-time collaboration of people and their mobility. Despite this progress, the energy consumption and the network coverage remain a serious problem against an efficient and continuous use of these mobile collaborative applications and a great challenge for their designers and developers. To address these issues, this chapter describes design patterns that help modelling mobile collaborative applications to support collaboration through the cloud. Two levels are presented: the first level provides self-control to create clones of mobile devices, manage users' groups and recover failed clones in the cloud. The second level supports group collaboration mechanisms in real-time. These design patterns have been used as a basis for the design of a mobile collaborative editing application.

Energy Conservation in the Era of Ubiquitous Computing

2019 ◽  
pp. 1142-1151
Author(s):  
P. P. Abdul Haleem
Keyword(s):  
Energy Consumption ◽  
Ubiquitous Computing ◽  
Mobile Devices ◽  
Exponential Growth ◽  
Carbon Emission ◽  
The Internet ◽  
Conservation Of Energy ◽  
Limited Power ◽  
Widespread Availability ◽  
Combined Impact

Widespread availability and affordability of devices and easy accessibility of internet has accelerated the pace of ubiquitous computing. Connectivity to the internet has resulted in an exponential growth in terms of content and traffic available on the internet. When the barriers such as type of devices, location, time, and format have become meaningless in the era of ubiquitous computing; the issue of energy consumption and resultant carbon emission is a matter of concern. Energy consumption is an issue in ubiquitous computing, as the majority of the devices involved will be mobile devices that depend on the limited power offered by the battery inside the device. Carbon emission is a concern due to the combined impact made by the devices hooked over to the internet. This chapter discusses the issues related to energy consumption for various activities when the services offered by the internet are availed. The chapter also discusses the challenges to be overcome to achieve conservation of energy consumed by the internet and devices.

Blackout Avoidance and Energy Saving Services with Alert Aggregation

2012 ◽  
Vol 3 (4) ◽  
pp. 40-56
Author(s):  
Erica Fong ◽  
Dickson K.W. Chiu ◽  
Haiyang Hu ◽  
Yi Zhuang ◽  
Hua Hu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Real Time ◽  
Mobile Devices ◽  
Critical Energy ◽  
The Internet ◽  
Monitor System ◽  
Energy Usage ◽  
Huge Number ◽  
Smart Meters

Peak electricity demands from huge number of households in a mega-city would cause contention, leading to potential blackout. This paper proposes bi-directional collaboration via a Smart Energy Monitor System (SEMS) between consumers and energy suppliers, exchanging real-time energy usage data with smart meters over the Internet and mobile devices for well-informed decisions and even predictions. The authors further propose the use of an Alert Management System (AMS) to monitor and aggregate critical energy consumption events for this purpose.

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