Towards of a real-time Big Data architecture to intensive care

These days the exponential increase in the volume and variety of data stored by companies and organizations of various sectors of activity, has required to organizations the search for new solutions to improve their services and/or products, taking advantage of technological evolution. As a response to the inability of organizations to process large quantities and varieties of data, in the technological market, arise the Big Data. This emerging concept defined mainly by the volume, velocity and variety has evolved greatly in part by its ability to generate value for organizations in decision making. Currently, the health care sector is one of the five sectors of activity where the potential of Big Data growth most stands out. However, the way to go is still long and in fact there are few organizations, related to health care, that are taking advantage of the true potential of Big Data. The main target of this research is to produce a real-time Big Data architecture to the INTCare system, of the Centro Hospitalar do Porto, using the main open source big data solution, the Apache Hadoop. As a result of the first phase of this research we obtained a generic architecture who can be adopted by other Intensive Care Units."This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013." This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-00002

Towards In-Transit Analytics for Industry 4.0

Industry 4.0, or Digital Manufacturing, is a vision of inter-connected services to facilitate innovation in the manufacturing sector. A fundamental requirement of innovation is the ability to be able to visualise manufacturing data, in order to discover new insight for increased competitive advantage. This article describes the enabling technologies that facilitate In-Transit Analytics, which is a necessary precursor for Industrial Internet of Things (IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things (iThings-2017), Exeter, UK, 201

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

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture

Making cities smarter help improve city services and increase citizens' quality of life. Information and communication technologies (ICT) are fundamental for progressing towards smarter city environments. Smart City software platforms potentially support the development and integration of Smart City applications. However, the ICT community must overcome current significant technological and scientific challenges before these platforms can be widely used. This paper surveys the state-of-the-art in software platforms for Smart Cities. We analyzed 23 projects with respect to the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, Internet of Things, Big Data, and Cloud Computing. Based on these results, we derived a reference architecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges, and discussed future opportunities. This survey gives important references for helping application developers, city managers, system operators, end-users, and Smart City researchers to make project, investment, and research decisions.Comment: Accepted for publication in ACM Computing Survey