127,409 research outputs found
An empirical study of power consumption of Web-based communications in mobile phones
Currently, mobile devices are the most popular
pervasive computing device, and they are becoming the primer way for Web access. Energy is a critical resource in such pervasive
computing devices, being network communication one of the primary energy consuming operations in mobile apps. Indeed, web-based communication is the most used, but also energy demanding. So, mobile web developers should be aware of how much energy consumes the different web-based communication alternatives. The goal of this paper is to measure and compare the
energy consumption of three asynchronous Web-based methods in mobile devices. Our experiments consider three different Web applications models that allow a web server to push data to a browser: Polling, Long Polling and WebSockets. The obtained
results are analyzed to get more accurate understanding of the impact in energy consumption of a mobile browser for each
of these three methods. The utility of these experiments is to show developers what are the factors that influence the energy consumption when different web-based asynchronous communication
is used. With this information mobile web developers
could reduce the power consumption of web applications on
mobile devices, by selecting the most appropriate method for
asynchronous server communication.MUniversidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech
Mobile Cloud Computing in Healthcare Using Dynamic Cloudlets for Energy-Aware Consumption
Mobile cloud computing (MCC) has increasingly been adopted in healthcare
industry by healthcare professionals (HCPs) which has resulted in the growth of
medical software applications for these platforms. There are different
applications which help HCPs with many important tasks. Mobile cloud computing
has helped HCPs in better decision making and improved patient care. MCC
enables users to acquire the benefit of cloud computing services to meet the
healthcare demands. However, the restrictions posed by network bandwidth and
mobile device capacity has brought challenges with respect to energy
consumption and latency delays. In this paper we propose dynamic energy
consumption mobile cloud computing model (DEMCCM) which addresses the energy
consumption issue by healthcare mobile devices using dynamic cloudlets
Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing
In this letter, we investigate an energy efficiency with performance guaranteed problem in mobile-edge computing. The mobile users desire lower energy consumption with better performance of tasks, for that we propose an energy minimizing optimization problem for mobile-edge cloud computing. We apply KKT conditions to solve it, and also present a request offloading scheme for this issue. In particular, the offloading scheme is determined by energy consumption and bandwidth capacity at each time slot. Numerical results demonstrate that our proposed offloading scheme outperforms local computing and entirely offloading method on energy consumption and performance on delay
Run-time Energy Management for Mobiles
Due to limited energy resources, mobile computing requires an energy-efficient a rchitecture. The dynamic nature of a mobile environment demands an architecture that allows adapting to (quickly) changing conditions. The mobile has to adapt d ynamically to new circumstances in the best suitable manner. The hardware and so ftware architecture should be able to support such adaptability and minimize the energy consumption by making resource allocation decisions at run-time. To make these decisions effective, a tradeoff has to be made between computation , communication and initialization costs (both time and energy). This paper describes our approach to construct a model that supports taking such decisions
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning
Unmanned Aerial Vehicles (UAVs) have been recently considered as means to
provide enhanced coverage or relaying services to mobile users (MUs) in
wireless systems with limited or no infrastructure. In this paper, a UAV-based
mobile cloud computing system is studied in which a moving UAV is endowed with
computing capabilities to offer computation offloading opportunities to MUs
with limited local processing capabilities. The system aims at minimizing the
total mobile energy consumption while satisfying quality of service
requirements of the offloaded mobile application. Offloading is enabled by
uplink and downlink communications between the mobile devices and the UAV that
take place by means of frequency division duplex (FDD) via orthogonal or
non-orthogonal multiple access (NOMA) schemes. The problem of jointly
optimizing the bit allocation for uplink and downlink communication as well as
for computing at the UAV, along with the cloudlet's trajectory under latency
and UAV's energy budget constraints is formulated and addressed by leveraging
successive convex approximation (SCA) strategies. Numerical results demonstrate
the significant energy savings that can be accrued by means of the proposed
joint optimization of bit allocation and cloudlet's trajectory as compared to
local mobile execution as well as to partial optimization approaches that
design only the bit allocation or the cloudlet's trajectory.Comment: 14 pages, 5 figures, 2 tables, IEEE Transactions on Vehicular
Technolog
Computation Offloading and Scheduling in Edge-Fog Cloud Computing
Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.
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