## 4 The WBANs Communication Stack
### Routing Protocols
#### 4.3.2 QoS-Aware Routing Protocols  
Different energy efficient and QoS-aware routing protocols have been proposed for wireless sensor networks [82, 83], wireless multimedia sensor networks [84, 85] and MANETs [86, 87], which can be used for WBANs after incorporating some customized changes according to the specific requirements of WBANs. The Routing Service Framework [88] aims to provide user specific QoS support for WBANs. It uses a small-scale network of 20 nodes and results show that it performs well in terms of reliability and latency. However, it contains more communication and computation overhead due to control packets. Reinforcement learning based routing protocol with QoS support (RL-QRP) is proposed for \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multirow{2}{*}{**Objective**} & \multicolumn{6}{c}{**Performance Metrics**} \\ \cline{3-8}  & & **Energy** & **Load** & **No. of** & **Delay** & **PDR** & **Route Set** **up Time** \\ \hline \multirow{3}{*}{EEMR [77]} & To discover node-disjoint & & & & & & \\  & paths and properly distribute & & & & & & \\  & the load & & & & & & \\ \hline \multirow{3}{*}{EEAMR [79]} & To Achieve energy efficiency & & & & & & \\  & through adaptive low & & & & & & \\  & overhead routing & & & & & & \\ \hline MRMS [80] & To Achieve energy efficiency & Very High & High & Low & Low & Very & Low \\  & & & & & & High & \\ \hline N-to-1 [81] & To Create one route & & & & & & \\  & discovery for each node & & High & Low & Low & Medium & High & Medium \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the Performance of Multipath Routing Protocols. biomedical sensor networks in [89]. This QoS-aware routing protocol based on geographic information and distributed Q-learning algorithm. In RL-QRP, optimal routing policies can be found through experiences and rewards without requiring any precise network state information. Simulation results show that RL-QRP performs well in terms of a number of QoS metrics and energy efficiency. Further, it fits well in the dynamic environment under various networks traffic load and mobility. Like the Routing Service Framework [88], it also uses small-scale network of 20 nodes. The packet delivery ratio decreases as the mobility level increase and the average end-to-end delay increases with the network throughput enhancement. The authors in [90] proposed a LOCALized Multi-Objective Routing (LOCALMOR) protocol for WBANs to provide different QoS services according to the traffic type, while considering latency, reliability, residual energy, and transmission power. The protocol attempts for each packet to fulfill the required QoS metrics in a power-aware fashion. It employs memory and computation efficient estimators, and uses a multiple sink single-path approach to increase the reliability. Results show that the proposed approach performs well in terms of packet delivery ratio as well as packet delivery delay as compared to other state-of-the-art schemes. However, all data packets are blindly disseminated towards both the primary and secondary sinks. The network traffic increases due to duplicate transmission of many data packets. This issue was partly solved in [91] (Data-centric Multiobjective (DMQoS-aware) routing protocol) by sending the data packets through single sink and it performs better in comparison with [90] by reducing latency, improving reliability and decreasing energy consumption. However, its performance decreases due to increase in the network throughput. Based on the literature review of QoS-aware routing protocols, Table 11 provides a comparison of these protocols in terms of network size, network throughput, mobility, delay, packet delivery ratio,