# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 6 Discussion  
Before drawing conclusions, we first highlight the key points in our proposition. Then, we further discuss some design limitations and extensions of CoAR for future investigation.  
Based on the simulation results and performance analysis, it is clear that CoAR has overall superior performance in comparison with other protocols. The performance supremacy, in fact, comes from the congestion control strategy along with other factors, which can be summarized as follows:  
Figure 12: Impacts of bursty traffic arrival on reliability at the root node in the different time interval: (**a**) Packet receiving ratio; and (**b**) Average end-to-end delay during the burst period.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Event 1** & **Event 2** \\ \hline Burst 1 & 100 s\(\sim\)130 s & 120 s\(\sim\)150 s \\ Burst 2 & 200 s\(\sim\)230 s & 220 s\(\sim\)250 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Traffic bursts activated during different time intervals of the simulation period.  
* The advantage of routing metrics used in CoAR is to utilize the network resources effectively. _QU_ metric is considered to prefer the nodes that have more free space in their buffer, _ETX_ to provide reliability and _RE_ to select the node having high energy. The tie-breaking metric _NI_ is considered as a criterion to select a preferred parent on a greedy basis.
* CoAR adopts the congestion-aware objective function as the parent selection framework for the decision-making problem within the congestion. To rank the alternative parents of a node, it combines the multiple routing metrics and the decision problem is solved by using the TOPSIS method. In addition, trickle algorithm in CoAR is aware of congestion and thus reset the timer as soon as congestion is detected. Actually, to achieve a balance between control overhead and fast recovery, CoAR send the congestion notification using DIO message.
* The adaptive threshold significantly utilizes the buffer capacity of a node hence detects the congestion timely. Additionally, the other factors that enhance the efficacy of CoAR is an appropriate adaptation of the weighting of routing metrics which accommodates to the performance criteria of the intended IoT application of emergency scenario.  
While CoAR proved its efficiency in the simulation tests, there are still some aspects that need further investigation. In our congestion detection mechanism, the smoothing factor \(\alpha\) and \(\beta\) were constant for the outgoing and incoming traffic respectively. The smoothing factors are used to adjust the level of smoothing to traffic measurement. The selection of this factors could be optimized and made it dynamic using some latest techniques such as fuzzy logic. However, it should be reasonable for resource constrained devices. Even though IoT application of emergency scenario might consist of mobile nodes, CoAR is described considering only a static network topology. However, we leave the optimization of CoAR in a mobile environment for future investigations.  
Providing security in RPL based network is challenging because of the resource constrained devices and LLN characteristics. Many of the vulnerabilities of LLNs will create the transition to IoT, mainly with RPL attacks. The security issues in RPL has been analyzed by the research community mainly in security attacks, such as DODAG version attacks [37], Suppression attack [38], Routing attacks [39], and Hatchetman attack [40]. Even though security is an important issue in routing protocol, it is out of the scope in this work. However, in future work, we plan to investigate the DIO suppression attack because it can severely degrade the routing service in CoAR. This attack induces victim nodes to suppress the transmission of DIO, which are necessary to build the route to alleviate congestion.