# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.6 Health/Medical  
The IoT technology is also being leveraged in the healthcare monitoring system to provide excellent emergency assistance to patients. It is also utilized as an E-health application in a variety of areas, including early identification of medical problems, emergency alerts as well as computer-assisted rehabilitation. Smartphones have become an important part of people's everyday lives, and they are linked with sensors to monitor the subject's health [124]. Crowdsourcing-based IoT paradigm enables the collection of large amounts of health-related data from patients, hospital equipment, environment, and so on, and is used in several applications for quality healthcare delivery.  
Using city air quality data generated via meteorological sites, IoT sensing, MCS, and user's body signal, [PERSON] [125] introduced a city healthcare system called UH-BigDataSys to offer urban residents exhaustive and correct air quality services and real-time physiological index monitoring. An air quality indicator (AQI) sensing is implemented at three levels of the network, and a multi-dimensional AQI (M-AQI) big data integration method is proposed to integrate data at the three levels. Crowdsourced AQI data are gathered at the first level, while AQI data fusion is considered at the edge-cloud level. Lastly, the AQI data were uploaded to a remote cloud or meteorological super computing platform. Furthermore, the physiological big data and M-AQI big data are processed and analyzed by the system to offer health advisory support to users on respiratory diseases, sleep quality control, outdoor travel, among other things. Figure 39 shows the system architecture of UH-BigDataSys.  
[PERSON] [126] designed and implemented an IoT-enabled system to improve commercial Continuous Glucose Monitor (CGM) for remote monitoring of patients and notifying them of potentially dangerous situations. The proposed system employed smartphones to gather blood glucose data from CGMs and transmit them to either a distributed fog or remote cloud computing nodes. The system incorporated a decentralized storage system for receiving, processing and storing of allocated data to ensure a reliable, trustworthy and cybersecure exchange of data with medical doctors, scientists and caretakers. The system also incorporated a digital crypto currency called GlucoCoin that utilizes blockchain to execute smart contracts so as to automate CGM sensor purchases as well as reward users who contribute data to the system. Experimental results showed the effectiveness of the system.  
[PERSON] [127] designed and prototyped Medical Monkeys, a collaborative crowdsourcing web application for annotating medical images. The platform comprised of medical students, researchers, online gamers, and patients under a virtual and collective advantageous collaboration for enhancing machine learning algorithms on medical images. The result of the system testing promised improved automatic segmentation and detection algorithms, as well as improved clinical decision support systems and a reduction in human-based inaccuracy in diagnostic and therapeutic evaluation.  
[PERSON] [128] leveraged the concept of crowdsourcing and IoT-based network to design a medical emergency system to improve existing e-Health monitoring system. They created a mathematical model to represent the uplink and downlink scenarios and evaluated the proposed system. Moreover, a mobile application was developed to practically simulate the system in the Bangladesh context.  
Different application areas require the use of different kinds of sensing data, and also encounter many domain constraints in gathering data. Researchers should acquaint themselves of application domains' constraints to sensing data as this would guide in choosing the appropriate techniques, sensing devices, communication network, and so on, to deploy in any implementation.