## 1 Introduction  
Wireless sensor networks (WSNs) find applications in numerous areas, including disaster management and prevention, agriculture, battlefield surveillance, home security, industrial control, traffic management, healthcare, and many more [1]. WSNs are infrastructures networks equipped with cost-effective sensor nodes that monitor the surrounding environment. These sensor nodes include sensors, a power supply, microcontrollers, and communicating devices. These units are responsible for data collection, processing, and transmission to the respective sensor nodes through communicating devices. The nodesare arbitrarily distributed and are battery powered, meaning their processing abilities are hugely constrained. Energy constraints, the communication radius, bandwidth problems, computation issues, and the storage capacity of the nodes are significant challenges that need to be addressed. Moreover, these constraints may lead to issues related to scheduling, synchronization, security, network connectivity, coverage, and localization [2]. Sensor nodes with non-rechargeable batteries retain energy for a certain period of time. Ensuring energy efficiency is the main objective, focusing on extending network lifetime by choosing optimal nodes as cluster heads (CHs) so as to support security-aware routing [3]. Several approaches are available to establish secured routing. To facilitate energy saving and the precise management of large-scale networks, clustering methods can be preferably used for achieving secure communication between sensor nodes. However, traditional clustering methods trust every sensor node and treat them as reliable, resulting in selecting malevolent nodes as CHs [4]. Malicious nodes chosen as CHs will influence the processing abilities and security of the network. Several routing schemes for routing data with improved security are available [5; 6; 7]. These routing methods are broadly categorized based on the structure of the network as location-, flat-, and hierarchy-based protocols. Location-based routing algorithms are based on sensor node location, while flat-based algorithms offer equal roles to every node depending on the functionality. In the case of hierarchy-based routing algorithms, every node occupies a dissimilar role. Traditional routing approaches forward traffic through unreliable routes, introducing complexities. Congestion detected from the source to the sink in the upstream direction is another challenge that can occur in cases where the speed of transmission and data processing time fall behind the incoming traffic speed. Network congestion may lead to increased latency, packet dropping, buffer overflows, energy wastage, reduced throughput, and decreased quality of service (QoS). The overall network functioning may drop in cases where there is an increase in network congestion. Hence, detecting these congestions and controlling them are basic requirements for WSNs. Though various congestion control protocols are presented, they are not ideal for resource-limited WSNs. So, energy-efficient and dependable congestion detection, control, and transport mechanisms are essential for optimizing QoS needs and network resources. Most of the presented congestion-controlling methods fail to examine the influence of attacks and the role of adversaries in network congestion. Most often, the low-cost nodes are susceptible to breakdown and occasionally become defective. Such nodes are said to be malicious as they operate smartly, giving rise to various security attacks in WSNs that cannot be easily detected. Some security attacks, like Sybil, flooding, and node replication attacks, as discussed in previous studies, have a direct influence on network congestion [8]. They give rise to congestion by creating intermittent network jams, initiating faulty node detection, creating traffic with hoax messages, and re-forwarding the same message multiple times. They cause increased communication overheads, computation time, and energy utilization, thus decreasing network lifetime.