scholarly journals Taming Energy Cost of Disk Encryption Software on Data-Intensive Mobile Devices

2016 ◽  
Vol 94 ◽  
pp. 83-90 ◽  
Author(s):  
Yang Hu ◽  
John C.S. Lui ◽  
Wenjun Hu ◽  
Xiaobo Ma ◽  
Jianfeng Li ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Energy Cost ◽  
Data Intensive ◽  
Disk Encryption

scholarly journals Taming energy cost of disk encryption software on data-intensive mobile devices

2020 ◽  
Vol 107 ◽  
pp. 681-691 ◽  
Author(s):  
Yang Hu ◽  
John C.S. Lui ◽  
Wenjun Hu ◽  
Xiaobo Ma ◽  
Jianfeng Li ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Energy Cost ◽  
Data Intensive ◽  
Disk Encryption

scholarly journals Enhancing Offloading Systems with Smart Decisions, Adaptive Monitoring, and Mobility Support

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Paulo A. L. Rego ◽  
Fernando A. M. Trinta ◽  
Masum Z. Hasan ◽  
Jose N. de Souza
Keyword(s):  
Mobile Devices ◽  
Performance Improvement ◽  
Mobile Cloud Computing ◽  
Mobility Support ◽  
Data Intensive ◽  
Adaptive Monitoring ◽  
Computationally Intensive ◽  
Processing And Storage ◽  
Reduced Power Consumption ◽  
And Storage

Mobile cloud computing is an approach for mobile devices with processing and storage limitations to take advantage of remote resources that assist in performing computationally intensive or data-intensive tasks. The migration of tasks or data is commonly referred to as offloading, and its proper use can bring benefits such as performance improvement or reduced power consumption on mobile devices. In this paper, we face three challenges for any offloading solution: the decision of when and where to perform offloading, the decision of which metrics must be monitored by the offloading system, and the support for user’s mobility in a hybrid environment composed of cloudlets and public cloud instances. We introduce novel approaches based on machine learning and software-defined networking techniques for handling these challenges. In addition, we present details of our offloading system and the experiments conducted to assess the proposed approaches.

scholarly journals Mobile Cloud Computing: Offloading Mobile Processing to the Cloud

2016 ◽  
Vol 9 (1) ◽  
pp. 90
Author(s):  
Sanjay P. Ahuja ◽  
Jesus Zambrano
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Response Times ◽  
Mobile Apps ◽  
Mobile Systems ◽  
Battery Life ◽  
Data Intensive ◽  
Computing Platforms ◽  
Computational Resources

<p class="zhengwen">The current proliferation of mobile systems, such as smart phones and tablets, has let to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device (such as phone + mp3 player + camera + Web browser + GPS + mobile apps + sensors). However, this conjunction penalizes the mobile system both with respect to computational resources such as processor speed, memory consumption, disk capacity, and in weight, size, ergonomics and the component most important to users, battery life. Therefore, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems.</p><p>Offloading mobile processing is an excellent solution to augment mobile capabilities by migrating computation to powerful infrastructures. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for offloading computation and data processing from mobile devices restricted by reduced resources. This research uses cloud computing as processing platform for intensive-computation workloads while measuring energy consumption and response times on a Samsung Galaxy S5 Android mobile phone running Android 4.1OS.</p>

scholarly journals Mobile devices and computing cloud resources allocation for interactive applications

2017 ◽  
Vol 27 (2) ◽  
pp. 293-307
Author(s):  
Henryk Krawczyk ◽  
Michał Nykiel
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Device ◽  
Heuristic Algorithm ◽  
Energy Cost ◽  
Optimization Problem ◽  
Resources Allocation ◽  
Interactive Applications ◽  
Application Execution ◽  
Cloud Resources

Abstract Using mobile devices such as smartphones or iPads for various interactive applications is currently very common. In the case of complex applications, e.g. chess games, the capabilities of these devices are insufficient to run the application in real time. One of the solutions is to use cloud computing. However, there is an optimization problem of mobile device and cloud resources allocation. An iterative heuristic algorithm for application distribution is proposed. The algorithm minimizes the energy cost of application execution with constrained execution time.

scholarly journals Do Groups Have Moral Standing in Unregulated mHealth Research?

2020 ◽  
Vol 48 (S1) ◽  
pp. 122-128 ◽  
Author(s):  
Joon-Ho Yu ◽  
Eric Juengst
Keyword(s):  
Mobile Devices ◽  
Citizen Science ◽  
Individual Autonomy ◽  
Community Based ◽  
The Public ◽  
Data Intensive ◽  
Grass Roots ◽  
Or Groups ◽  
Using Data ◽  
Science Movement

Biomedical research using data from participants’ mobile devices borrows heavily from the ethos of the “citizen science” movement, by delegating data collection and transmission to its volunteer subjects. This engagement gives volunteers the opportunity to feel like partners in the research and retain a reassuring sense of control over their participation. These virtues, in turn, give both grass-roots citizen science initiatives and institutionally sponsored mHealth studies appealing features to flag in recruiting participants from the public. But while grass-roots citizen science projects are often community-based, mHealth research ultimately depends on the individuals who own and use mobile devices. This inflects the ethos of mHealth research towards a celebration of individual autonomy and empowerment, at the expense of its implications for the communities or groups to which its individual participants belong. But the prospects of group harms — and benefits — from mHealth research are as vivid as they are in other forms of data-intensive “precision health” research, and will be important to consider in the design of any studies using this approach.

scholarly journals EnergyBox: Disclosing the wireless transmission energy cost for mobile devices

2014 ◽  
Vol 4 (2) ◽  
pp. 118-135 ◽  
Author(s):  
Ekhiotz Jon Vergara ◽  
Simin Nadjm-Tehrani ◽  
Mihails Prihodko
Keyword(s):  
Mobile Devices ◽  
Energy Cost ◽  
Wireless Transmission ◽  
Transmission Energy

The Ubiquitous Grid

2010 ◽  
pp. 35-43
Author(s):  
Patricia Sedlar
Keyword(s):  
Grid Computing ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Resource Availability ◽  
Visual Information ◽  
Location Based Services ◽  
Health Care Environment ◽  
Data Intensive ◽  
Research Activities ◽  
Ubiquitous Access

Grid computing is an emerging technology providing the possibility to aggregate resources for the solution of computation- or data-intensive scientific tasks. Taking the evolution of mobile computing into consideration, new Grid concepts are conceivable, fully exploiting the advantage of mobile devices and ubiquitous access. By decoupling resource availability from the core grid infrastructure and hardware, the user has always the same computational power, data or storage available, regardless of a device or location. Thus restricted capabilities of thin clients can be extended and new fields of application can be made accessible. The key concept is “The invisible grid” – the grid environment should just be there for the use of applications in science, business, health care, environment, or culture domains. Having this concept in mind, the following scenario is conceivable: Equipped with your mobile phone, which you always have with you, you are walking around and are taking a picture of an object you are interested in. You are sending the picture to the grid, where the visual information is extracted. After the analysis, information about the captured object is sent to you. Thus you have a search engine on a visual base at your permanent disposal, information captured as seen by your eyes – without the need of textural translations or the need to know the object’s name or ID in order to retrieve information about it. Realizing the scenario above, the user obtains a smart tool, easing information retrieval considerably by making use of ubiquity in combination with grid computing. But the scenario has even more potential in terms of pervasiveness. The use of mobile devices can provide a user with additional location bound information. With a portable device the user is able to access location-based services or to collect environmental information to be processed within a grid. At this stage research activities in the field of pervasive computing comeinto play. Pervasive computing pursues the goal to enhance the environment with sensors and smart objects in order to provide the user with suitable context-based and/or location-based services. Expanding the introduced setting with the capabilities from pervasive computing, the following scenario is conceivable: You are an invited speaker on a conference and you are moving through the rooms of the venue. All rooms are equipped with cameras covering all perspectives of view. You are looking at a person from whom you want to know the research interests. You flick with your finger, to capture the camera picture from your perspective. The picture is processed within the grid and the ambient display next to you shows the requested information.

scholarly journals An Efficient Agent Based Offloading Decision Maker For Mobile Cloud Computing

2021 ◽  
Author(s):  
SHANTHI THANGAM MANUKUMAR ◽  
Vijayalakshmi Muthuswamy ◽  
Bushra H
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Decision Maker ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Cost Effective ◽  
Vital Role ◽  
Mobile Cloud ◽  
Agent Based ◽  
Data Intensive

Abstract The usages of mobile devices are drastically increasing every day with high end support to the users. The high end configurations mobile devices such as smart phones, laptops, tablets, etc., computations are complex in these devices. Computation intensive and data intensive are plays a vital role in the mobile devices. The main challenges in the mobile devices are handling the mobile applications in the devices with high computation and high storage. The above mentioned challenges can be overcome by using mobile cloud computing. The limitations while handling the mobile cloud computing is offloading decision making, which part of computation should offload and which should execute in the mobile side. The proposed work provides the solution to the limitations and challenges mentioned earlier by providing agent based offloading decision maker for mobile cloud. The decision maker should decide which computation part is executed in the mobile side and the cloud side. The evaluation shows the mobile applications having high complexity get benefited over other high applications. The proposed system achieves the better response time, low latency, cost-effective and minimizes the energy consumed by data-intensive and computational-intensive mobile applications.

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