Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Amulet: An Energy-Efficient, Multi-Application Wearable Platform

Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage.\r\n\r\nWe introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime

Machine Learning for the Prediction of App Energy Consumption from Appstore Data

The mobile market has seen tremendous development throughout the past few years both in terms of hardware and the software that is available for the devices. Despite this, the batteries that power these devices have not seen major improvements and have been unable to accompany the progress seen in this field. Due to this phenomenon, researchers have been showing a growing interest in the development of green computing solutions in order to spend the least amount of energy possible when using mobile devices. This as presented itself in a plethora of ways, from the accurate evaluation of the energy consumption of applications through the use of energy models and profilers to the assessment and development of better coding practices with energy conservation as the main focus. However, there have been few to no studies regarding the development of user-side solutions to help solve this problem. In order to fill this gap in research this study focuses on providing a machine learning solution with the intent of identifying links between the information available in the store page of an application and its energy consumption to develop an a priori method for the classification and certification of mobile applications. Hence the main contribution of this project resides on the previously mentioned machine learning model, adapted to the Aptoide appstore and mainly targeting applications that belong to the games category, given that these have the highest volume of downloads and interest by the users of the appstore.O mercado dos dispositivos móveis tem visto um tremendo desenvolvimento nos últimos anos tanto em termos de hardware como de software que é disponibilizado para os dispositivos. Apesar disto, as baterias que abastecem estes dispositivos não têm tido melhorias e têm sido incapazes de acompanhar o progresso desta área. Devido a este fenómeno, os investigadores têm vindo a mostrar um interesse cada vez maior no ramo de soluções para computação verde de modo a gastar o mínimo de energia possível com dispositivos móveis. Isto gerou uma variedade de respostas, desde determinar o consumo energético de uma aplicação de forma acertada com recurso a modelos e profilers energéticos até ao desenvolvimento de práticas de codificação adequadas para a conservação da energia dos dispositivos. No entanto, têm havido poucos estudos realizados sobre soluções destinadas aos utilizadores que ajudem a resolver este problema. De modo a preencher esta lacuna, este estudo foca-se no desenvolvimento de uma solução de aprendizagem automática que determine as conexões entre a informação sobre uma aplicação na appstore e o seu consumo energético. Sendo assim, o principal contributo deste projeto reside na aprendizagem automática mencionada anteriormente, adaptada para a appstore Aptoide e com principal foco nas aplicações pertencentes à categoria de jogos sendo que estas compõem grande parte do volume de descarregamentos da plataforma