3,611 research outputs found
How to Place Your Apps in the Fog -- State of the Art and Open Challenges
Fog computing aims at extending the Cloud towards the IoT so to achieve
improved QoS and to empower latency-sensitive and bandwidth-hungry
applications. The Fog calls for novel models and algorithms to distribute
multi-service applications in such a way that data processing occurs wherever
it is best-placed, based on both functional and non-functional requirements.
This survey reviews the existing methodologies to solve the application
placement problem in the Fog, while pursuing three main objectives. First, it
offers a comprehensive overview on the currently employed algorithms, on the
availability of open-source prototypes, and on the size of test use cases.
Second, it classifies the literature based on the application and Fog
infrastructure characteristics that are captured by available models, with a
focus on the considered constraints and the optimised metrics. Finally, it
identifies some open challenges in application placement in the Fog
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
Secure Cloud-Edge Deployments, with Trust
Assessing the security level of IoT applications to be deployed to
heterogeneous Cloud-Edge infrastructures operated by different providers is a
non-trivial task. In this article, we present a methodology that permits to
express security requirements for IoT applications, as well as infrastructure
security capabilities, in a simple and declarative manner, and to automatically
obtain an explainable assessment of the security level of the possible
application deployments. The methodology also considers the impact of trust
relations among different stakeholders using or managing Cloud-Edge
infrastructures. A lifelike example is used to showcase the prototyped
implementation of the methodology
Ten Quick Tips for Using a Raspberry Pi
Much of biology (and, indeed, all of science) is becoming increasingly
computational. We tend to think of this in regards to algorithmic approaches
and software tools, as well as increased computing power. There has also been a
shift towards slicker, packaged solutions--which mirrors everyday life, from
smart phones to smart homes. As a result, it's all too easy to be detached from
the fundamental elements that power these changes, and to see solutions as
"black boxes". The major goal of this piece is to use the example of the
Raspberry Pi--a small, general-purpose computer--as the central component in a
highly developed ecosystem that brings together elements like external
hardware, sensors and controllers, state-of-the-art programming practices, and
basic electronics and physics, all in an approachable and useful way. External
devices and inputs are easily connected to the Pi, and it can, in turn, control
attached devices very simply. So whether you want to use it to manage
laboratory equipment, sample the environment, teach bioinformatics, control
your home security or make a model lunar lander, it's all built from the same
basic principles. To quote Richard Feynman, "What I cannot create, I do not
understand".Comment: 12 pages, 2 figure
Edge Computing for Extreme Reliability and Scalability
The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud
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