## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.8 Comparative Study of QoS-Aware Routing Protocols  
Recently, different energy efficient and QoS-aware routing protocols have been proposed for wireless sensor networks [40; 41; 42; 43], wireless multimedia sensor networks [44; 45; 46] and MANETs [47; 48], which cannot be directly used for WBSNs because of their unique constraints. However, they can be incorporated after customization according to the dynamics of WBSNs. Furthermore, the QoS with dynamic wireless condition make the job more challenging [49]. Based on the literature review of QoS-aware routing protocols, which are discussed above, Table 1 provides a comparison in terms of network size, network throughput, mobility, delay, packet delivery ratio, and energy consumption with other state-of-the-art schemes. The Routing Service Framework [32] aims to provide user specific QoS support uses a small scale network of 20 nodes and performs well in terms of reliability and latency, but it does not consider the energy consumption which is one of the major constraints of wireless sensor networks. Furthermore, it incurs more communication and computation overhead due to control packets. RL-QRP [33] is QoS-aware routing protocol based on geographic information and distributed Q-learning algorithm. As compared to QoS-AODV discussed in [50], RL-QRP performance is bad at the start due to learning process but becoming better with the passage of time. Like Routing Service Framework, it also uses small scale network of 20 nodes. The packet delivery ratio decreases as the mobility level increase and \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{6}{c}{**Characteristics**} \\ \cline{2-9}  & \multirow{2}{*}{**Goal**} & **Network** & **Network** & \multirow{2}{*}{**Mobility**} & **Delay** & **PDR** & **Energy** \\  & & **Size** & **Throughput** & & & & **Cons.** \\ \hline \multirow{2}{*}{Routing Service} & To provide priority based & & & & & & \\  & routing \& user specific & Small & N/A & Yes & N/A & Medium & N/A \\  & QoS Support & & & & & & & \\  & To achieve high packet & & & & & & \\  & delivery ratio and low & Small & Low & Yes & High & High & N/A \\  & end-to-end delay & & & & & & & \\  & LOCALMOR & To provide QoS support & & & & & & \\  & based on nature of Data & & & & & & & \\  & DMQoS & To provide Data Centric & & & & & & \\  & QoS support & & & & & & \\  & EPR & To reduce network traffic & & & & & & \\  & and energy consumption & & & & & & \\  & QPRD & To reduce end-to-end & & & & & & & \\  & delay & & & & & & & \\  & QPRR & To enhance end-to-end & & & & & & & \\  & reliability & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: QoS-aware routing protocols. the average end-to-end delay increases with the increase in network throughput. Furthermore, this scheme does not consider the energy consumption. LOCALMOR [25] is a modular approach that divides that patient's vital information into four categories. It performs well in terms of end-to-end delay, packet delivery ratio, and packet received within deadline (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 sending too many duplicate data packets. To solve the issue of two sinks, each coordinator, which they called as body sensor more, of DMQoS [26] sends the data packets towards a single sink. The different modules of DMQoS perform better to reduce latency, improve reliability, and decrease operational energy overloads as compared to other state-of-the-art approaches given in [25, 51, 52]. However, its performance decreases due to increase in the network throughput. Next, the use of the LO technique to optimize the trade-off between geographic information and residual energy is not an efficient way, because it ignores the less important function if the more important result in a unique solution.