# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
## 2 Related Works  
Congestion is one of the most common problems in wireless sensor networks (WSNs) due to the computing, storage, transmission, and energy supply constraints of wireless sensor network nodes. In the past, many scholars have researched WSN congestion and have already presented a considerable number of research results. In one study, the media access control (MAC) layer access mechanism of the WSN is analyzed, and a survivability aware channel allocation (SACA) algorithm is developed based on a fuzzy method to prioritize access strategies [18]. The algorithm reduces packet collisions during congestion, improving network throughput and transmission reliability. Furthermore, [19] analyzes network congestion caused by cascading failure among the nodes of WSNs, designs a route-based WSN cascading model, and enhances the congestion resistance of the network. However, these two congestion control algorithms relate to the physical layer of the WSNs and lack adaptability to most WSNs in which the IEEE 802.15.4 standard specification is applied to the physical layer. The WSN layer feature provides a path from the source node to the destination node, along which packets propagate. It is easier to match the underlying hardware from the network layer to identify and solve congestion problems. As a result, many studies provide solutions to congestion from routing protocols. For example, the decentralized predictive congestion control (DPCC) [20] protocol uses the node's hop-by-hop feedback information and embedded channel estimation algorithm to evaluate channel quality based on queue utilization. As a result, the data flow can adaptively select the route, reducing network congestion to a certain extent. Based on the idea of actively avoiding congestion, a multi-path optimizing routing protocol based on predicting congestion (MOPC) protocol [21]developed a path satisfaction model. It realizes the optimal route selection based on congestion prediction, remaining energy, and the lowest number of hops. As a result, MOPC improves reliability and real-time performance. According to the overall energy consumption and network lifetime requirements, a scheme of priority of energy congestion relief (PECTR) [22] builds an energy priority congestion detection and mitigation mechanism. It takes the residual energy as an essential parameter, periodically detects the cache queue occupancy rate, and attempts to establish a temporary optimal path around the congestion node for congestion control. Thus, PECR effectively extends the network lifetime and alleviates network congestion. The congestion-aware routing (CoAR) protocol [23] is specific to the application of WSNs in low power and lossy networks. It detects congestion based on current queue capacity and observation of historical traffic and selects alternative parent nodes to alleviate congestion, effectively alleviating network congestion in LLN (Low-power and Lossy Networks). However, the above algorithms are designed for wireless sensor networks with limited energy, emphasizing the development of node lifetime through an energy-saving method. In contrast, the wireless sensor networks studied in this paper have a continuous energy supply and generally do not need to consider energy consumption during routing selection. The optimizing routing algorithm based on congestion control (CCOR) [24] algorithm constructs the network queuing model to detect the congestion degree and selects route probability based on the water flow principle. The traffic-aware and energy-efficient routing (TER) [25] has built a pipe model that considers both spatial relationships and network loads, flowing packets to the convergence node as quickly as possible with minimal energy consumption and avoidance of congestion. The gravitation theory-based routing (GTR) algorithm [26] defines the concepts of \"space gravity\" and \"gravitational pull\" so that packets can avoid congestion areas as much as possible in transit and prevent unavoidable congestion.