Internet of Things (IoT) aims to connect the real world made up of devices, sensors and actuators to the virtual world of Internet in order to interconnect devices with each other generating information from the gathered data. Devices, in general, have limited computational power and limited storage capacity. Cloud Computing (CC) has virtually unlimited capacity in terms of storage and computing power, and is based on sharing resources. Therefore, the integration between IoT and CC seems to be one of the most promising solutions. In fact, many of the biggest companies that offer Cloud Services are focusing on the IoT world to offer services also in this direction to their users. In this paper we compare the three main Cloud Platforms (Amazon Web Services, Google Cloud Platform and Microsoft Azure) regarding to the services made available for the IoT. After describing the typical architecture of an IoT application, we map the Cloud-IoT Platforms services with this architecture analyzing the key points for each platform. At the same time, in order to conduct a comparative analysis of performance, we focus on a service made available by all platforms (MQTT middleware) building the reference scenarios and the metrics to be taken into account. Finally, we provide an overview of platform costs based on different loads. The aim is not to declare a winner, but to provide a useful tool to developers to make an informed choice of a platform depending on the use case.
The availability of an objective clinical evaluation in the diagnosis and monitoring of parkinson's disease is a primary importance objective in neurology. Furthermore, in many patients next to resting tremor typical of the disease are also found other types of tremor as kinetic and postural tremor so making the diagnosis difficult. The ability to classify the different types of tremor specific for each patient through an examination of the instrumental, non-invasive and very simple and fast is a great tool to aid the clinical diagnosis of the disease.Our system meets the above requirements. It consists of an inertial sensor that allows the acquisition of the quantities of interest, and by a series of algorithms able to provide an objective and quantitative assessment of the type and severity of tremor in patients with Parkinson's disease. The availability of an objective report on the severity of the disorder developed according to a strict correlation with the valuation provided by the UPDRS scale is a good starting point towards the personalization of care as well as being a useful tool in the analysis of the course of the disease.
Nowadays society is moving to a scenery where autonomous elderly live alone in their houses. An automatic remote monitoring system using wearable and ambient sensors is becoming even more important, and is a challenge for the future in WSNs, AAL, and Home Automation areas. Relating to this, one of the most critical events for the safety and the health of the elderly is the fall. Lot of methods, applications, and stand-alone devices have been presented so far. This work proposes a novel method based on the Support Vector Machine technique and addressed to Android low-cost smartphones. Our method starts from data acquired from accelerometer and magnetometer, now available in all the low-end devices, and uses a set of features extracted from a processing of the two signals. After an initial training, the classification of fall events and non-fall events is performed by the Support Vector Machine algorithm. Since we have decided to use the smartphone as monitoring device, the use of other invasive wearable sensors is avoided, and the user have simply to hold the phone on his pocket. Moreover, we can use the cellular network for the eventual sending of notifications and alerts to relatives in case of falls. Actually, our tests show a good performance with a sensitivity of 99.3% and a specificity of 96%.
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