![]() In recent years, Active Queue Management (AQM) mechanisms to improve the performance of TCP/IP networks have acquired a relevant role. In the simulation experiment of Network Simulator 3 (NS3), the results show that the BBRv2+ algorithm can improve intraprotocol fairness and RTT fairness and ensure bandwidth utilization and interprotocol fairness. BBRv2+ algorithm can avoid blind window constraints and selectively mark packets so that different flows can converge to fairness. Based on these problems, we analyze the root cause and proposed an improved algorithm BBRv2+, which uses flow-aware explicit congestion notification (ECN) to quantify queue information and feedback on the accurate congestion degree. However, when multiple BBRv2 flows enter the same link at different times, fair convergence cannot be achieved, and RTT fairness still exists. The BBRv2 evaluation results show that it can improve the coexistence with the loss_based algorithm and alleviate some of the shortcomings in BBRv1. The BBR v2 algorithm is a recently updated version by Google, which aims to improve some of the problems in the original BBR (BBRv1) algorithm, such as interprotocol fairness issues, RTT fairness issues, and excessive retransmissions. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. Google proposed a new congestion control algorithm (CCA) based on bottleneck bandwidth and round-trip propagation time (BBR), which is considered to open a new era of congestion control. Moreover, we give a detailed study of various congestion control algorithms based on rate adaption and traffic engineering schemes. We also present essential artifacts in the congestion taxonomy, depicting critical aspects of IoT congestion control. Moreover, we focus on the various granularities of congestion for the IoT network stack. We further present a detailed study of communication technologies for IoT devices. We summarize and compare the techniques prevalent among existing IoT networks. Congestion control within and among IoT networks is either (i) end-to-end, or (ii) hop-by-hop. This survey presents a detailed study of various congestion control schemes for reliable and unreliable communication, as well as evaluate “divergence” among IoT devices. IoT communication is categorized into reliable and unreliable, identical to the Internet-based systems. Hence, this survey focuses on congestion and related issues as the primary source causing significant performance issues in IoT networks. Constraints within smart devices are a prime cause of congestion among IoT networks, causing performance degradation and data loss. Although IoT devices generate enormous data, they are still constrained by low processing, power, and limited buffer size. Internet of Things (IoT) is a collection of billions of smart objects connected via different types of communication media. ![]()
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