# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 2 Related Works
Additionally, the authors introduced DELAY_ROOT, a new routing metric that minimizes the average delay towards the DAG root. CA-RPL alleviates the congestion by distributing a traffic to different paths. However, the protocol degrades the network reliability because it does not aware the packet overflow that occurs at the nodes' buffer. It also does not have a strategy to detect the congestion when it occurs. A new objective function based on fuzzy logic (OF-FL) has been proposed in [22], that provides the QoS guarantees for RPL. The main purpose of OF-FL is to provide a better characterization of the best path. It combines several routing metrics and provides a more comprehensive description of the quality of neighbor nodes in order to select the best forwarding candidate. OF-FL uses link quality, hop count, residual energy and latency as the metrics in the process of path optimization. However, it is not clear which metric should be optimized for the specific purpose. In [23], the authors analyzed the impact of objective function on the network topology using the link quality objective function (LLQ-OF) and OF0. However, the LLQ-OF does not consider the congestion problem. The authors studied the congestion control schemes and explained how the buffer state is monitored by the different congestion detection mechanisms in [24]. The authors further stated that monitoring the buffer state can be achieved either through a buffer threshold or through a periodic buffer where a threshold can be dynamic or static. Recently, an adaptive parent selection mechanism in RPL for advanced metering infrastructure network named energy and congestion-aware routing metric (ECRM) has been proposed in [25]. ECRM considers the residual energy and queue utilization of neighboring nodes as a metric for parent selection criteria. It minimizes the power consumption and improves the packet delivery ratio of the network. The scheme adopts the resource control-based method to alleviate the congestion. Moreover, it also uses different metrics and constraints to calculate the rank and selecting the best parent. However, in ECRM, the congestion detection scheme is based on the static threshold. Additionally, in dynamic or bursty traffic scenario, such as industrial monitoring IoT applications, it might be prone to congestion situation due to its consecutive buffer overflow-based parent change policy. The routing protocols mentioned in the literature are aimed at enhancing network performance in congestion scenarios. These protocols employed different congestion control method to obtain the application requirements. The majority of the protocols are focused on monitoring buffer capacity for deciding the occurrence of congestion, which may not be reasonable for event-driven IoT application. Therefore, the routing protocol should also observe the traffic trend to satisfy the aforementioned application objective. In order to alleviate the congestion, all the protocols have focused on either alternative route selection or rate adjustment method. In the rate adjustment method, the node limits the source traffic volume. The most challenging part of this method is in determining the right amount of rate reduction. This challenge becomes more critical for the LLN based IoT applications. In addition, a single routing metric for route selection has a severe effect of overloading in the network, that resulting in resource depletion. Therefore, the routing protocol should be concerned with utilizing the multiple metrics in LLNs. Based on this understanding, in this paper, we are encouraged to implement the resource control-based congestion alleviation and adaptive threshold-based congestion detection mechanism for emergency scenario IoT application. Furthermore, to facilitate the analysis of static threshold-based congestion detection scheme with the resource control-based method under different traffic scenario, we compare our proposed protocol along with RPL as well as with the ECRM.