Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications

Mobile devices are increasingly becoming an indispensable part of people\u27s daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given

TechNews digests: Jan - Mar 2010

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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

Can cloudlet coordination support cloud computing infrastructure?

Abstract The popularity of mobile applications on smartphones requires mobile devices to perform high-performing processing tasks. The computational resources of these devices are limited due to memory, battery life, heat, and weight dissipation. To overcome the limitations of mobile devices, cloud computing is considered the best solution. The major issues faced by cloud computing are expensive roaming charges and growing demand for radio access. However, some major benefits associated with cloud computing are fast application processing, fast transfer of data, and substantial reduction in the use of mobile resources. This study evaluated the association between the distance of cloud servers and cloudlets with and without coordination and data latency. Fast communication in the cloudlet environment is facilitated by coordinated cloudlets, which have a positive influence on the infrastructure of cloud computing. The coordinated cloudlets can be efficiently accessed in different areas, such as vehicular networks, vehicular fog computing, and fog computing

PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring

Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users’ conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.This work was partially supported by the Spanish CICYT Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476, and the FPU Spanish Grant AP2009-2244. This work was also supported in part by the INTERREG IV European Project WHM-Wireless Health Monitoring (I-1-02=091) and the European Commission Seventh Framework Programme FP7 Project OPENi-Open-Source, Web-Based, Framework for Integrating Applications with Social Media Services, and Personal Cloudlets under Grant no. 317883

A Study of Mobility Support in Wearable Health Monitoring Systems: Design Framework

International audienceThe aim of this work is to investigate main techniques and technologies enabling user's mobility in wearable health monitoring systems. For this, design requirements for key enabling mechanisms are pointed out, and a number of conceptual and technological recommendations are presented. The whole is schematized and presented into the form of a design framework covering design layers and taking in consideration patient context constraints. This work aspires to bring a further contribution for the conception and possibly the evaluation of health monitoring systems with full support of mobility offering freedom to users while enhancing their life qualit

Cloud Computing

In the recent years, Cloud Computing has become very popular and an interesting subject in the field of science and technology. The research efforts in the Cloud Computing have led to a number of applications used for the convenience in daily life. Cloud Computing is not only providing solutions at the enterprise level but it is also suitable in organizing a centralized database which is accessible from every corner of the world. It is said that, 10 to 15 years later when all the enterprises have adopted the Cloud Computing, there will be no more perception for the data center in the company. The aim of this Master’s thesis “Cloud Computing: Server Configuration and Software Implementation for the Data Collection with Wireless Sensor Nodes” was to integrate the Wireless Sensor Network with Cloud Computing in a such a way that the data received from the Sensor node can be access able from anywhere in the world. To accomplish this task, a Wireless Sensor Network was deployed to measure the environmental conditions such as Temperature, Light and the Sensor’s battery information and the measured values are sent to a web server from where the data can be accessed. The project also includes the software implementation to collect the sensor’s measurements and a Graphical User Interface (GUI) application which reads the values from the sensor network and stores it to the database.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Wireless body area network mobility-aware task offloading scheme

The increasing amount of user equipment (UE) and the rapid advances in wireless body area networks bring revolutionary changes in healthcare systems. However, due to the strict requirements on size, reliability and battery lifetime of UE devices, it is difficult for them to execute latency sensitive or computation intensive tasks effectively. In this paper, we aim to enhance the UE computation capacity by utilizing small size coordinator-based mobile edge computing (C-MEC) servers. In this way, the system complexity, computation resources, and energy consumption are considerably transferred from the UE to the C-MEC, which is a practical approach since C-MEC is power charged, in contrast to the UE. First, the system architecture and the mobility model are presented. Second, several transmission mechanisms are analyzed along with the proposed mobility-aware cooperative task offloading scheme. Numerous selected performance metrics are investigated regarding the number of executed tasks, the percentage of failed tasks, average service time, and the energy consumption of each MEC. The results validate the advantage of task offloading schemes compared with the traditional relay-based technique regarding the number of executed tasks. Moreover, one can obtain that the proposed scheme archives noteworthy benefits, such as low latency and efficiently balance the energy consumption of C-MECs