7,715 research outputs found
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN
Mobile devices are rapidly becoming the primary computing device in people's
lives. Application delivery platforms like Google Play, Apple App Store have
transformed mobile phones into intelligent computing devices by the means of
applications that can be downloaded and installed instantly. Many of these
applications take advantage of the plethora of sensors installed on the mobile
device to deliver enhanced user experience. The sensors on the smartphone
provide the opportunity to develop innovative mobile opportunistic sensing
applications in many sectors including healthcare, environmental monitoring and
transportation. In this paper, we present a collaborative mobile sensing
framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on
smartphones capturing and sharing sensed data between multiple distributed
applications and users. MOSDEN follows a component-based design philosophy
promoting reuse for easy and quick opportunistic sensing application
deployments. MOSDEN separates the application-specific processing from the
sensing, storing and sharing. MOSDEN is scalable and requires minimal
development effort from the application developer. We have implemented our
framework on Android-based mobile platforms and evaluate its performance to
validate the feasibility and efficiency of MOSDEN to operate collaboratively in
mobile opportunistic sensing applications. Experimental outcomes and lessons
learnt conclude the paper
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
Towards a Smarter organization for a Self-servicing Society
Traditional social organizations such as those for the management of
healthcare are the result of designs that matched well with an operational
context considerably different from the one we are experiencing today. The new
context reveals all the fragility of our societies. In this paper, a platform
is introduced by combining social-oriented communities and complex-event
processing concepts: SELFSERV. Its aim is to complement the "old recipes" with
smarter forms of social organization based on the self-service paradigm and by
exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of
International Conference on Software Development and Technologies for
Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track
on Emergent Technologies for Ambient Assisted Living (ETAAL
Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud
Electronic Health (e-Health) technology has brought the world with
significant transformation from traditional paper-based medical practice to
Information and Communication Technologies (ICT)-based systems for automatic
management (storage, processing, and archiving) of information. Traditionally
e-Health systems have been designed to operate within stovepipes on dedicated
networks, physical computers, and locally managed software platforms that make
it susceptible to many serious limitations including: 1) lack of on-demand
scalability during critical situations; 2) high administrative overheads and
costs; and 3) in-efficient resource utilization and energy consumption due to
lack of automation. In this paper, we present an approach to migrate the ICT
systems in the e-Health sector from traditional in-house Client/Server (C/S)
architecture to the virtualised cloud computing environment. To this end, we
developed two cloud-based e-Health applications (Medical Practice Management
System and Telemedicine Practice System) for demonstrating how cloud services
can be leveraged for developing and deploying such applications. The Windows
Azure cloud computing platform is selected as an example public cloud platform
for our study. We conducted several performance evaluation experiments to
understand the Quality Service (QoS) tradeoffs of our applications under
variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green
Computing (CGC 2013
Publishing LO(D)D: Linked Open (Dynamic) Data for Smart Sensing and Measuring Environments
The paper proposes a distributed framework that provides a systematic way to publish environment data which is being updated continuously; such updates might be issued at speciļ¬c time intervals or bound to some environment- speciļ¬c event. The framework targets smart environments having networks of devices and sensors which are interacting with each other and with their respective environments to gather and generate data and willing to publish this data. This paper addresses the issues of supporting the data publishers to maintain up-to-date and machine understandable representations, separation of views (static or dynamic data) and delivering up-to-date information to data consumers in real time, helping data consumers to keep track of changes triggered from diverse environments and keeping track of evolution of the smart environment. The paper also describes a prototype implementation of the proposed architecture. A preliminary use case implementation over a real energy metering infrastructure is also provided in the paper to prove the feasibility of the architectur
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