## 1 Introduction and State-of-the-Art
### State-of-the-Art  
In different wireless network structures, such as those found in the IoT, TCP retains its significance as a transport protocol owing to its dependable delivery characteristics and extensive prevalence on the Internet [19; 20]. Among the various attributes of the TCP, congestion control stands out as a key feature that has garnered sustained attention over time, remaining the focus of numerous research studies. Research on this topic typically concentrates on the following methods: active queue control, network feedback techniques, and alterations in a TCP's algorithm, as well as different combinations of these methods [21; 22; 23; 24]. This section provides a brief overview of studies that have tackled issues concerning TCP and the IoT. In the publication [25], a comprehensive comparative analysis of TCP's congestion control algorithms within the IoT context is outlined. This study assesses 14 different alternatives, delineating their respective benefits and drawbacks. The findings suggest that the primary challenges observed among the evaluated algorithms predominantly lie in issues related to data transfer rate and equity. With the proliferation of intercommunicating devices, congestion inevitably rises. In the realm of the IoT, this escalation is particularly pronounced, driven by the continual appearance of new devices able to join an existing network. In such a dynamic ecosystem, effective congestion control becomes imperative to accommodate a diverse range of devices, each potentially employing a different variation of a transport protocol. This involves adapting data transfer speeds in response to network congestion, all while ensuring fairness and sustaining throughput across the network [25]. Typically, proposals for modifications align with a standard TCP [26; 27] or involve particular improvements to the control algorithms of established versions. Within these alterations, protocols utilizing Round-Trip Time for measuring network congestion are categorized as proactive, while those relying on segment losses for congestion identification are deemed reactive. As noted by the authors of [28], even though reactive protocols effectively utilize the entire accessible data transfer capacity, they may not be well-suited for equipment operating with a limited data transfer capacity. Conversely, pre-emptive protocols aim to minimize data segment losses and retransmissions but may fall short of leveraging the entire network capacity. In light of these considerations, the study in [28] modifies the TCP that can function in either pre-emptive or proactive modes based on the network environments. This protocol initially operates in pre-emptive mode before congestion escalation is identified, at which point it transitions to proactive mode. The hybrid variant demonstrated commendable performance in terms of data transfer speeds and equity in contrast with the New Reno, CUBIC, HTCP, and FAST-FIT variants. Another recent study delving into modifications of transport protocols is outlined in [29]. CERL+ stands out as a version inspired by TCP Reno, with its primary aim being to optimize the data transfer rate while minimizing the adverse effects stemming from lost data segments. Their suggestion utilizes mean and minimum Round-Trip Time (RTT) estimates to discern connection bottleneck circumstances and identify instances of random data segment loss attributed to connection failures, a common occurrence in 802.11 networks. In comparison with various other TCP variants [12; 13; 14], including NewReno, mVeno, Westwood+, YeAh, CUBIC, and New Jersey+, the CERL+ suggestion exhibited a superior data transfer rate and lower data segment loss, mitigating bottleneck congestion across diverse wireless network structures. By integrating a blend of the aforementioned methodologies, the authors in [30] introduce FAIR+, a synthesis of Active Queue Management (AQM) mechanisms, network reactions, and TCP adjustments.