## 2 eHealth Monitoring System
### Multisensor System-on-Chip Platform  
The use of System-on-Chip (SoC) platforms is widespread in monitoring applications. In order to develop our eHealth monitoring system, we have used an SoC platform, named Raspberry Pi (RPi), and an interface stage for the different sensors. The RPi platform is based on Broadcom BCM2835 System on a Chip, including an ARM1176 JZF-S 700 MHz processor, a Graphic Processing Unit (GPU) and 512 MB of RAM, with a SD slot card memory [12]. We installed in the RPi a WiFi adapter TP-Link TL-WN725N with IEEE 802.11 b/g/n standard.  
The sensor interface is the eHealth Cooking Hacks monitoring shield, which allows physiological and medical applications to be conducted. This shield can connect different sensors, such as pulse and oxygen saturation in blood sensor (SPO2), airflow sensor (spirometer-based), body temperature sensor (PTC), electrocardiogram sensor (ECG), galvanic skin response sensor (skin conductance) (GSR), electromyography sensor (EMG), blood pressure (sphygromanometer) sensor, and position sensor (accelerometer).  
We installed in the RPi a Raspbian Operating System [12], which is a GNU/Linux version optimized for RPi hardware, based on a Debian distribution. These nodes are in charge of carrying out the standardization process. This process is necessary because it is very important that the data arrive as uniform as possible to be able to perform fusion tasks in real time in the cloud. These RPi hardware are connected to our cloud system using an infrastructure connection with APs (Access Points) or with wireless mesh network. This type of partial mesh network (see an example in Figure 2) introduces self-organization and self-configuration in the proposed system, which can reduce installation overhead. The ability to self-configure enables dynamic distribution of workloads, particularly in the event that a few nodes should fail. This in turn contributes to fault-tolerance and reduced maintenance costs. In order to implement the wireless mesh network, we used the Babel routing protocol, which is loop free, proactive and based on a distance vector algorithm. Babel is implemented in the Quagga routing software suite [14], with GPL license.  
We also configured a Cloud-based system, called ThingSpeak [15], in order to gather the information from the different sensors into one system, connected to the Internet. This system allows storing and automatic synchronization of the information from the different biosensors on each patient being monitored. Also allows data processing for data fusion with a Matlab interface.  
Also a PHP (Hypertext Preprocessor) webserver with MySQL database [16] was installed in another computer. This computer connects the patient information with the processed biosensing information coming from the IoT system.A PHP script on the server poses the appropriate query to ThingSpeak database in order to gather the biosensors' data, process, and then record and store these  
Figure 1: Scheme of our eHealth monitoring system.  
data on the database with the corresponding patient data. This information is available to authorized personnel. The database has been implemented on a Relation-Entity (R-E) scheme.