Table 5.

Edge based IoT devices

Reference	Process	Sensors used	Devices used(fog/edge)	Technology	Advantage/ Disadvantage
Abdellatif et al. [39]	Classification and detection of ECG	ECG sensor	Not mentioned	Employed stacked auto encoders	All data processing tasks are done at a web server
Uddin et al. [40]	Activity detection	ECG, accelerometer, gyroscope	GPS based fog server	RNN model	Few numbers of the subject are included
Greco et al. [41]	To find the real time anomaly data	Gyroscope, accelerometer	Raspberry pi	Distributed edge framework and HTM algorithm	The device did not exploit contextual information
Kaur et al. [42]	Heart rate and temperature of the body checking	Pulse rate sensor, body temperature sensor	Raspberry pi	Blue mix technique	All information processing tasks performed at the cloud
Satija et al. [43]	ECG monitoring system	ECG sensor	Arduino and microcontroller	DFT	Only use single channel ECG data
Mathur et al. [44]	Gait detection, limp rehabilitation	Hand movement and temperature sensor	Raspberry pi, Arduino, smart phone	MATLAB	Illumination and motion variance
Muhammad et al. [45]	Voice pathology detection	Voice, temperature, ECG, humidity	Smartphone	ELM technique	Only use one type of data for detecting
Sood et al. [46]	Chikungunya virus disease	Environmental sensors and wearable smart sensors	No edge support	Analysis was done on the fog node while processing done on the cloud	Less specificity in the case of the detector and highly efficient
Vaneeta Bhardwaj et al. [47]	To monitor blood sugar, blood pressure, oxygen level and heart rate	Blood pressure sensor, temperature sensor and pulse oximeter	Raspberry Pi	Python	The system can deploy in non-COVID-19 patients, and the cost of the system is relatively low
Filippos Pelekoudas-Oikonomou et al. [48]	To secure the system based on blockchain technology	IoMT sensors and actuators	Raspberry Pi devices	Hyperledger Fabric platform	It has a lightweight implementation that is suitable for IoMT networks