## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.11 EECMR [34]  
In [34], [PERSON] et al. proposed an energy efficiency clustering multi-hop routing protocol (EECMR). Here, the authors proposed clustering to increase the lifetime of nodes. For this, they assumed that the nodes are mobile and know their location; as a result, there is no fixed topology. For this protocol, a node can be either a cluster head (CH), a cluster relay (CR), or a cluster member (CM). CH changes based on the energy conserved. All \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **CH Criteria** & **Communication** & **Node Positioning** & **Simulation Tool** & **Localization** & **CH Election** \\ \hline \multirow{3}{*}{EERU-CA} & \multirow{3}{*}{Special node} & Within cluster single & \multirow{3}{*}{Layered} & \multirow{3}{*}{MATLAB} & \multirow{3}{*}{No} & \multirow{3}{*}{No} \\  & & hop. With other & & & & \\ \cline{1-1} \cline{5-6}  & & cluster multi-hop & & & & \\ \cline{1-1} \cline{5-6}  & \multirow{2}{*}{Based on} & Within cluster single & \multirow{2}{*}{Grid} & \multirow{2}{*}{NS2} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} \\ \cline{1-1} \cline{5-6}  & & residual energy & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of cluster-based routing protocols. the nodes are divided into layers depending on the transmission range of the node or sink. Every layer has several clusters. Each cluster has CH and CM. The nodes on the border of the layers act as relay nodes. The propagation of the nodes is vertical. Hence, the attenuation increases with distance. The protocol consists of two phases: the setup and the steady-state phases. The authors considered cluster formation as a sub-phase. The roles of the nodes are selected based on residual energy and distance from the sink. Hello packets are sent to detect and store the neighbor information and cluster formation. A predetermined timeout value ensures that there is no long waiting time. Nodes will elect themselves as CH depending on the weight of the neighbors. The CH then sends a join request to the neighbors to form the cluster. The neighbors will join if they receive only one join request. If they receive more, one join request will select either the nearest node or the one with the highest receiving power. If a node receives a join request from both the upper and lower layer, it becomes a relay node. Nodes send a \"RESPONSE\" message to confirm their cluster ID; the scheduling of packets uses time-division multiple access (TDMA). EECMR is compared with DBR. The simulation results show that EECMR has fewer dead nodes when the transmission range is increased. It also has significantly higher residual energy compared to DBR. Even when the transmission ranges are optimal, the received packet ratio is higher than DBR. Overall, it shows significant improvement over DBR. * Comparison  
Here, the surveyed protocols use Hello packet in the initiation phase. All of them use a layered-based system. All three protocols compared themselves with DBR, whereas EEDBR, an EEEDBR, is an upgraded version of DBR. EEDBR is a proactive protocol, EEEDBR is reactive, and EECMR uses clustering. All these protocols use depth information for node forwarding, showing a significant improvement over DBR in terms of residual energy and delivery success ratio. Table 6 illustrates the comparison of depth-based routing protocols. * RL-based routing protocols:  
Human interactions with the world inspire reinforcement learning (RL). The main focus of RL is on how intelligent agents behave in a complex environment. In RL, an agent achieves its goal through interacting and learning from its surroundings. RL learns about the environment, what to do, and how to define the present behavior's conditions to receive a numerical reward. The agent is rarely given instructions on which activities to take and must rely on trial and error to learn which acts provide the best results. One of the most common RL approaches is Q-learning. The agent in Q-learning makes decisions based on a certain Q-value.