# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
## 2 Related Work  
Nowadays, healthcare experts and researchers promote the need for automated health monitoring devices to enhance the patients' safety and to reduce the stress of the medical staff, enabling them to monitor their patients or interactive with them from anywhere at anytime [13; 14; 15]. Consequently, several recent works have addressed medical monitoring systems. Moreover, to better diagnose, forecast and characterize individuals' health, several models exploit machine learning in healthcare, as discussed in refs. [16; 17; 18].  
In ref. [19], the authors presented an IoT-based health monitoring approach in which medical sensor data are collected and sent to an analysis module through a LoRaWAN network infrastructure. LoRaWAN represents a valid and easy-to-deploy technology for monitoring patients in field hospitals during emergency situations, as explained in ref. [20]. However, although LoRa is an appealing technology to provide low power, long-range wireless connection [21; 22; 23], LoRa-based approaches, e.g., the one in ref. [24], cannot cope with monitoring applications that require high sample rates and a significant amount of exchanged data. This is because LoRa is intended for low data rate transmissions and is subject to duty-cycle restrictions, and therefore it cannot support the transmission of waveforms that entail a considerable amount of data sent per second.  
WiFi offers higher bandwidth than LoRa, and therefore it is a more suitable technology for the addressed purpose. The work in ref. [25] proposed a full Internet-based architecture that uses the oneM2M and openEHR standards, thus allowing interoperability between different devices and software from the WiFi-enabled wearable physiological sensors to the monitoring system. The work in ref. [26] presented a monitoring system made up of Raspberry Pi single-board computers that collect sensor data and send them through the Internet using Ethernet or WiFi. Other approaches for healthcare applications exploit the Software-Defined Networking (SDN) paradigm that, as discussed in ref. [27], allows handling the complexity of heterogeneous networks in a simple way and to provide priority-based mechanisms for the monitoring services [28; 29]. For example, the architecture proposed in [30] exploits different functional and security applications and services provided by SDN.  
Ref. [31] proposed a novel framework for the analysis of the human physiological detection system based on a biosensor, but it does not focus on communication aspects.  
One common feature of all the approaches discussed above as well as other ones (see, e.g., [32; 33; 34]) is that they are Internet-based. Conversely, one of the design requirements of the MEDIWARN system was to transmit sensitive data through a private network that is locally managed within the hospital, thus avoiding to transmit them to external servers. Moreover, to the best of the authors' knowledge, the above-mentioned state-of-the-art monitoring systems do not foresee a predictive approach able to anticipate the upcoming deterioration of a patient's condition and trigger alarms well before the patient starts to get worse.  
Conversely, MEDIWARN is intended for automatically monitoring the vital parameters of hospitalized patients in order to both take action in a proactive way whenever needed and provide a complete history of each patient's vital parameters during their hospitalization for data analysis. For this reason, in the following, the design and implementation of a communication system that is able to cope with all the MEDIWARN requirements is addressed.