A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs

Sunny, Albert; Panchal, Sumankumar D.; Vidhani, Nikhil; Krishnasamy, Subhashini; Anand, S.; Hegde, M. S.; Kuri, Joy; Kumar, Anurag

doi:10.1016/j.jnca.2017.05.006

Cited by 11 publications

(13 citation statements)

References 23 publications

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“…Moreover, the link capacity calculation was poor, the interval size needed to be calculated, bursty traffic occurred, and control traffic needed to be minimized. Sunny et al [5] proposed a generic controller that ensures the fair and efficient operation of Wireless Local Area Network (WLANs) with multiple co-channel access points, long-lived performance, TCP transfers in multi-AP WLAN, and improves performance issues of long-lived TCP transfers in multi-AP WLANs.…”

Section: Related Workmentioning

confidence: 99%

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Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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Section: Related Workmentioning

confidence: 99%

“…Congestion control shortage is one of the main causes of the decrease in the lifetime and energy of wireless sensor network (WSN) nodes [4,5]. This decrease leads to many other issues, such as delay, packet loss, and bandwidth degradation [6].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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“…ELASTIC provide a received video rate that oscillates around the fair share, with an increased number of video level switches. However, the main result involved long-lived TCP flows [42], where experimental evaluation showed that ELASTIC is able to get the fair share when competing with TCP long-lived flows.…”

Section: Literature Reviewmentioning

confidence: 99%

Variants of the Constrained Bottleneck LAN Edge Link in Household Networks

Khan¹,

Goodridge²

2019

IJANA

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Competition among adaptive video streaming players severely reduces user-QoE. This occurs as resource allocation becomes unfair. We refer to this as the bottleneck link problem. The resource imbalance creates severe user annoyance such as screen flickering and video freezes. However, there are numerous conditions that amplifies the problems. Some of these are well documented in the literature but we intend to highlight the major conditions at a bottleneck link in household LANs. These are time-varying bandwidth, TCP long-lived flows, players pausing, starting/re-starting and stopping and increases in player numbers. We explore these conditions and evaluate the performance of heuristic adaptive video players. ELASTIC and PANDA players are evaluated. Experimental setup includes the TAPAS player and emulated network conditions. The results show that players are well suited to specific conditions.

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“…www.ijacsa.thesai.org Wireless sensor networks (WSN) have been presumed a critical part of communication because of their extraordinary highlights (e.g., versatility and simplicity of association) that make them a significant transporter of information across networks [5][6][7]. The unique fundamental driver of the lessening in the lifetime of nodes in WSN and reduction in node's energy is due to lack of congestion control [8,9]. This lessening prompts numerous different issues, for example, delay, loss of packets, and transmission capacity deprivation [10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Congestion Window Algorithm for the Internet of Things Enabled Networks

Chappala¹,

Sri²

2021

IJACSA

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Heterogeneous constrained computing resources in the Internet of Things (IoT) are communicated, collected, and share information from the environment using sensors and other high-speed technologies which generate tremendous traffic and lead to congestion in the Internet of Things (IoT) networks. This paper proposes an Adaptive Congestion Window (ACW) algorithm for the Internet of Things. This algorithm is adapted to the traffic changes in the network. The main objective of this paper is to increase the packet delivery ratio and reduce delay while enhancing the throughput which can be attained by avoiding congestion. Therefore, in the proposed algorithm, the congestion window size is depending on the transmission rate of the source node, the available bandwidth of the path, and the receiving rate of the destination node. The congestion window size is altered when the link on the path needs to be shared/released with/by other paths of different transmission in the network. The proposed algorithm, ACW is simulated, evaluated in terms of packet delivery ratio, throughput, and delay. The performance of the proposed algorithm, ACW is compared with IoT Congestion Congrol Algorithm (IoT-CCA) and Improved Stream Control Transmission Protocol (IMP-SCTP) and proved to be better by 27.4%, 11.8%, and 33.7% than IoT-CCA and 44.1%, 22.6%, and 50% than IMP-SCTP concerning packet delivery ratio, throughput, and delay respectively. The variation in congestion window size with time is also projected.

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A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs

Cited by 11 publications

References 23 publications

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Variants of the Constrained Bottleneck LAN Edge Link in Household Networks

Adaptive Congestion Window Algorithm for the Internet of Things Enabled Networks

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