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Wireless Sensor Networks (WSNs) consist of miniature sensor nodes, capable to operate and capture events in human-inaccessible terrains. The data packets generated by these networks may either be continuous, event-based or query-driven, and are application-specific in nature. These packets need to be transmitted in an energy-efficient manner to the base station. In these networks, congestion occurs when the incoming traffic load exceeds the available capacity of the network. There are various factors that lead to congestion in WSNs such as buffer overflow, varying rates of transmission, many-to-one communication paradigm, channel contention and the dynamic nature of a transmission channel. Congestion leads to depletion of the nodes energy, deterioration of network performance and an increase in network latency and packet loss. As a result, energy-efficient congestion control protocols need to be designed to detect, notify and control congestion effectively. Furthermore, these protocols need to ensure a reliable delivery of data in resource-constrained WSNs. In this paper, we present a review of the latest state-of-the-art congestion control protocols. Depending on their inherent nature of control mechanism, these protocols are classified into three categories, i.e., traffic-based, resource-based and hybrid. Traffic-based protocols are further subdivided, based on their hop-by-hop or end-to-end delivery modes. Resource-based control protocols are further analyzed, based on their route establishment approach and efficient bandwidth utilization techniques. We also discuss the internal operational mechanism of these protocols for congestion alleviation. Finally, we provide a comprehensive analysis of these protocols in terms of various performance metrics to justify in which scenario a particular class of these protocols needs to be deployed. Based on the performance analysis, we conclude that the behaviour of each class of protocols varies with the type of deployed application and a single metric alone cannot precisely detect congestion of the network.

The transmission of satellite payload data is critical for services provided by aerospace ground networks. To ensure the correctness of data transmission, the TCP data transmission protocol has been used typically. However, the standard TCP congestion control algorithm is incompatible with networks with a long time delay and a large bandwidth, resulting in low throughput and resource waste. This article compares recent studies on TCP-based acceleration algorithms and proposes an acceleration algorithm based on the learning of historical characteristics, such as end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment’s deep data inspection, and random packet loss caused by various factors. The proposed algorithm is evaluated and compared with the TCP congestion control algorithms under both laboratory and ground network conditions. Experimental results indicate that the proposed acceleration algorithm is efficient and can significantly increase throughput. Therefore, it has a promising application prospect in high-speed data transmission in aerospace-ground service networks.

We introduce a novel multipath data transport approach at the transport layer referred to as ‘Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control’ (DDPG-MPCC), which leverages deep reinforcement learning to enhance congestion management in multipath networks. Our method combines DDPG with online convex optimization to optimize fairness and performance in simultaneously challenging multipath internet congestion control scenarios. Through experiments by developing kernel implementation, we show how DDPG-MPCC performs compared to the state-of-the-art solutions.

In this study, a new reaching law for sliding mode control of discrete time systems is proposed and applied to solve the problem of congestion avoidance in multi-source, connection oriented data transmission networks. Since the proposed reaching law does not require switching of the sliding variable between positive and negative values in each successive control step, it leads to chattering free operation, does not cause overshoots and helps achieve 100% exploitation of the bottleneck link available bandwidth. Furthermore, the proposed controller always generates bounded data transmission rates. The rates are limited by design parameters and they do not depend on the network initial conditions. The properties of the proposed controller are stated as three theorems, formally proved and verified in a simulation example.

Wireless Sensor Networks (WSNs) inherently are resource-constrained in terms of available energy, bandwidth, processing power and memory space. In these networks, congestion occurs when the incoming traffic load surpasses the available capacity of the network. There are various factors that lead to congestion in WSNs such as buffer overflow, varying rates of transmission, a many-to-one communication paradigm, channel contention and interference. Congestion leads to depletion of the nodes energy, deterioration of network performance and an increase in network latency and packet loss. As a result, energy-efficient and reliable state-of-the art congestion control protocols need to be designed to detect, notify and control congestion effectively. In this paper, we present a review of the latest state-of-the-art congestion control protocols. We analyze these protocols from various perspectives such as, their deployed environments, internal operational mechanisms, their advantages and disadvantages. Depending on their inherent nature of control mechanisms, these protocols are classified either as traffic-based congestion control or resource-based congestion control. Based on our analysis, we further subdivided these protocols based on their hop-by-hop and end-to-end delivery modes.

Software-Defined Wireless Body Area Network (WBAN)s have gained significance in emergency healthcare applications for remote patients. Prioritization of healthcare data traffic has a high influence on the congestion and delay in the WBAN routing process. Currently, the energy constraints, packet loss, retransmission delay and increased sensor heat are pivotal research challenges in WBAN. These challenges also degrade the network lifetime and create serious issues for critical health data transmission. In this context, a Priority-based Energy-efficient, Delay and Temperature Aware Routing Algorithm (PEDTARA) is presented in this paper using a hybrid optimization algorithm of Multi-objective Genetic Chaotic Spider Monkey Optimization (MGCSMO). This proposed optimized routing algorithm is designed by incorporating the benefits of chaotic and genetic operators to the position updating function of enhanced Spider Monkey Optimization. For the prioritized routing process, initially, the patient data transmission in the WBAN is categorized into normal, on-demand and emergency data transmissions. Each category is ensured with efficient routing using the three different strategies of the suggested PEDTARA. PEDTARA performs optimal shortest path routing for normal data, energy-efficient emergency routing for high priority critical data and faster but priority verified routing for on-demand data. Thus, the proposed PEDTARA ensures energy-efficient, congestion-controlled and delay and temperature aware routing at any given period of health monitoring. Experiments were performed over a high-performance simulation scenario and the evaluation results showed that the proposed PEDTARA performs efficient routing better than the traditional approaches in terms of energy, temperature, delay, congestion and network lifetime.

An increasing number of devices are connecting to the Internet via Wi-Fi networks, ranging from mobile phones to Internet of Things (IoT) devices. Moreover, Wi-Fi technology has undergone gradual development, with various standards and implementations. In a Wi-Fi network, a Wi-Fi client typically uses the Transmission Control Protocol (TCP) for its applications. Hence, it is essential to understand and quantify the TCP performance in such an environment. This work presents an emulator-based approach for investigating the TCP performance in Wi-Fi networks in a time- and cost-efficient manner. We introduce a new platform, which leverages the Mininet-WiFi emulator to construct various Wi-Fi networks for investigation while considering actual TCP implementations. The platform uniquely includes tools and scripts to assess TCP performance in the Wi-Fi networks quickly. First, to confirm the accuracy of our platform, we compare the emulated results to the results in a real Wi-Fi network, where the bufferbloat problem may occur. The two results are not only similar but also usable for finding the bufferbloat condition under different methods of TCP congestion control. Second, we conduct a similar evaluation in scenarios with the Wi-Fi link as a bottleneck and those with varying signal strengths. Third, we use the platform to compare the fairness performance of TCP congestion control algorithms in a Wi-Fi network with multiple clients. The results show the efficiency and convenience of our platform in recognizing TCP behaviors.

In recent years, Named Data Networking (NDN) has attracted researchers’ attention as a new internet architecture. NDN changes the internet communication paradigm from a host-to-host IP model to a name-based model. Thus, NDN permits the retrieval of requested content by name, from different sources and via multiple paths, and the use of caching in intermediate routers. These features transform the transport control model from sender to receiver and make traditional end-to-end congestion control mechanisms incompatible with NDN architecture. To deal with this problem, a reliable congestion control mechanism becomes necessary for a successful deployment of NDN. This paper presents a new hybrid congestion control mechanism for NDN, EC-Elastic (Explicit Congestion Elastic), which adopts the basic concept of Elastic-TCP to control the sending rates of the interest packets at the consumer nodes. In the intermediate nodes, a queue has been associated with the Controlled Delay-Active Queue Management CoDel-AQM to measure the packet sojourn time and notify the consumer to decrease its interest packet sending rate when it receives an explicit congestion signal. EC-Elastic was implemented in ndnSIM and evaluated with Agile-SD, CUBIC, and STCP in different scenarios. Simulation results show that EC-Elastic provides a significant improvement in bandwidth utilization while maintaining lower delay and packet loss rates.

In the European Telecommunication Standards Institute (ETSI) framework, the vehicle-to-vehicle (V2V) and the vehicle-to-roadside (V2R) communications on the 5 GHz frequency band must use the decentralised congestion control (DCC) algorithm standardised by the ETSI. The DCC algorithm distinguishes itself from other methods in that it simultaneously regulates no less than four parameters that all work to the identical effects. However, it is have claimed that this apparently reassuring feature is actually excessive and can lead DCC to perform sub-optimally. It is shown that it could be simplified to use fewer parameters by demonstrating that a physical layer data rate control, which is only one of the four used by the DCC, achieves a better result.

The transmission control protocol (TCP) has contributed to the tremendous success of the Internet but it also faces many challenges which are becoming more and more significant as the network grows. Although numerous congestion control algorithms have been proposed to improve the performance of TCP in heterogeneous networks, designing a congestion control algorithm that could achieve high utilisation, ensure fairness and maintain stability remains a great challenge. A novel congestion control algorithm named fair TCP (FTCP) has been proposed to mitigate these challenges. FTCP mitigates these challenges through the following strategies: First, increase the round trip time (RTT)-fairness by altering TCP's initial congestion control window (cwnd) and adjusting the cwnd's growth rate to make FTCP flows with different RTTs achieve the same throughput. Secondly, balance the transmission efficiency and TCP-friendliness by dynamically adjusting the aggressiveness of FTCP according to the congestion level of the link. Preliminary experimental evaluations verify that FTCP has obvious advantages in transmission efficiency, RTT-fairness and TCP-friendliness comparing to the state-of-the-art congestion control algorithms.