# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 2 Review of Related Literature
### Security in WBAN/Iot  
A secure healthcare monitoring scheme is employed [36] to monitor and control the smart healthcare system. To design a secure scheme, the authors integrated two artificial intelligence algorithms, a fuzzy system and neural network classifiers. It decides the priority level of the data based on the collected sensor's information. From the patient body, collected sensor data are sent via the GSM module to the Azure IoT hub. Secure communications have not been achieved successfully, which increases the security loss of the network. A secure and efficient anonymous authentication scheme was presented in [37] to preserve the location privacy of WBAN patients. The author proposes the Chinese remainder theorem (CRT) to keep the user's location details more secret from unauthorized attackers. In this scheme, both doctors and patients are authenticated with their location to protect it. Experiments proved that the proposed work outperformed the impersonation attack, message modification attack, replay attack, man-in-the-middle attack and eavesdropping attack. To keep the location private for both the doctor and patient, the TA must be decentralized because when the TA fails to provide security, then the whole security is lost. A secure certificateless biometric authentication with group key management is presented in [38] for WBAN. However, the coordinator node plays a vital role in collecting the medical data because it acts as the personal controller for each WBAN. For authentication, electrocardiogram (ECG) records are used as the biometric feature. Then, group key management is executed for all validated sensor nodes in the WBAN. Lack of security and privacy due to ECC and one-way hash function is not suited for resource constrained IoT sensors.  
A two level lightweight method is presented [39] for determining anomaly data from massive sensor readings. First, the game theory approach is applied to find the spatial correlation between the sensor readings and determine the dynamic changes in the WBAN. Second, the Mahalanobis distance is presented in the local processing unit (LPU), which has a global view in the multivariate analysis. The overall trust for sensors is very poor, and there is higher energy consumption in fault detection.  
The authors in [40] introduced a secure framework for WBAN-IoT in healthcare systems. This work employs five layers, namely, the collection of the data layer from the WBAN sensors to the gateway agent, the second layer is responsible for data routing to the local gateway server from the gateway agent, the third layer is responsible for the routing of data to the distant server, and the fourth layer is responsible for data routing to the fog layer where controllers are deployed for providing security to the data and classify the data as critical and noncritical. Finally, all the data are stored in the private cloud. This work provides security to the data in the fog layer; however, the authenticity of the users was not considered, which leads to security threats and various attacks in the networks.