# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 2 Review of Related Literature
### QoS Achievement in WBAN/Iot  
The authors in [28] present a WBAN architecture with dual sink nodes. Line of sight (LoS) and non-line of sight (NLoS) based clusters were formed, and then two sinks were placed. The major objective of this paper was to minimize the energy consumption of the network. After the data are sensed, critical data are sent to the sink directly, and periodical data are sent through the next forwarders. Equal time slots are assigned for all sensors deployed on a patient, which must be slotting time according to the node priority. An IoT-based BAN network architecture is presented to monitor human health [29]. Initially, sensed data were classified and assigned a priority level. Based on the priority level, data are routed to the sink node. The THE (temperature heterogeneity energy) protocol is tested for a WBAN with eight different sensors, including a temperature sensor, insulin pump, SPO2 sensor, EMG sensor, CGM sensor, BP sensor, EEG sensor and ECG sensor. To transmit periodical data, two hops are used as the relay nodes, but an optimal selection of relay nodes is important. In [30], the authors aimed to achieve energy efficiency, reliability and transmission efficiency. To achieve these aims, a multiobjective based routing protocol is designed by considering residual energy, bandwidth, transmission rate and hop count to the sink node. Routing is executed based on the maximum benefit function. This weight function is adjusted according to data priority. In this paper, the next forwarder is dynamically selected for data transmission. Data classification is significant to perform before data routing because successful data transmission shows high QoS performance. A priority-based MAC protocol is designed [31] to show better performance achievement for WBAN. A prior knowledge-based weighted routing algorithm is presented to choose the optimum route, and it is computed by residual energy, distance, delay and link stability. To further reduce energy consumption, graph-based sleep scheduling is proposed. In the coordinator node, a split and map-based NN is elaborated to perform packet classification. Then, packets are sent to the respective sink node based on the packet size, data traffic type and \(TTL\). To forward data packets to the corresponding sink node, QoS constraints such as delay, bandwidth and data rate are needed. A queuing game approach is proposed to transmit delay-sensitive medical data beyond WBAN [32]. The proposed approach addresses the transmission of random arrival of medical packets on each gateway. At each gateway, packets are classified into multiple classes: one class specifies the emergency alarm, and other classes specify the nonmedical classes. Due to the consideration of several QoS constraints, packets are queued according to their classes. At each smart gateway, running the game theory approach consumes more time and causes more waiting time to send the critical data packets. Network modeling with BSs and multiple gateways leads to high energy consumption and packet loss ratios. The authors in [33] focus on beyond WBAN communications to handle the massive medical data packets from each WBAN. A queuing system is designed to classify large medical data packets into multiple classes. It is based on the time and priority of the data packets. To address a relaxed queuing problem, an optimal scheduling scheme is presented. The drawbacks in this work are:  
* It is complex to design a large scale WBAN environment since medical packets are gathered at each gateway in a random way, which leads to large end-to-end delay. * Some of the medical packets are necessary to meet the QoS requirements, especially delay, which must be met by the predefined period of time. In [34], two significant parameters are considered for the best next hop selection to route medical data packets to the BS. The link quality and energy utilization rate are computed for each on-body sensor. A sensor with high link quality and residual energy isselected as the next hop.