TCP-LoRaD: A Loss Recovery and Differentiation Algorithm for Improving TCP Performance over MANETs in Noisy Channels

Sarkar, Nurul I.; Ho, Ping-Huan; Gul, Sonia; Zabir, Salahuddin Muhammad Salim

doi:10.3390/electronics11091479

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…TCP-wireless environment, link losses, and congestion packet loss models (TCP-WELCOME) [16] distinguish packet loss by observing the history of RTT sample evolution on the link and the data packet loss trigger. The loss differentiation algorithm, loss recovery, and differentiation (TCP-LoRaD) [17] is modified based on the TCP-WELCOME algorithm to calculate RTT. TCP Westwood [7] relies on end-to-end bandwidth estimates to determine the cause of packet loss.…”

Section: Threshold Determination-based Loss Differentiationmentioning

confidence: 99%

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

Wei,

Guo,

Yang

et al. 2023

Entropy

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In light of the high bit error rate in satellite network links, the traditional transmission control protocol (TCP) fails to distinguish between congestion and wireless losses, and existing loss differentiation methods lack heterogeneous ensemble learning models, especially feature selection for loss differentiation, individual classifier selection methods, effective ensemble strategies, etc. A loss differentiation method based on heterogeneous ensemble learning (LDM-HEL) for low-Earth-orbit (LEO) satellite networks is proposed. This method utilizes the Relief and mutual information algorithms for selecting loss differentiation features and employs the least-squares support vector machine, decision tree, logistic regression, and K-nearest neighbor as individual learners. An ensemble strategy is designed using the stochastic gradient descent method to optimize the weights of individual learners. Simulation results demonstrate that the proposed LDM-HEL achieves higher accuracy rate, recall rate, and F1-score in the simulation scenario, and significantly improves throughput performance when applied to TCP. Compared with the integrated model LDM-satellite, the above indexes can be improved by 4.37%, 4.55%, 4.87%, and 9.28%, respectively.

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Section: Threshold Determination-based Loss Differentiationmentioning

confidence: 99%

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

Wei,

Guo,

Yang

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…Hence, all sensor nodes within a network can possess an independent and sensible system, delivering resolutions via an onboard microcontroller [ 29 ]. LoRa signifies the entirety of alternate interaction modules that implement settings to machine-to-machine interfaces that are still external from the presence of networks such as 3G/4G [ 7 , 30 ]. A battery-based optimal communication environment has been similarly suggested as an optimal environment.…”

Section: Related Workmentioning

confidence: 99%

A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study

Sarkar

Kavitha

Ali

2022

Sensors

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Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor and store IoT data in the cloud. This paper aims to report on the design, analysis, and performance evaluation of a low-cost LoRa transceiver interface unit (433 MHz band) for the real-time monitoring and storing of IoT sensor data in the cloud. We designed and analyzed a low-cost LoRa transceiver interface unit consisting of a LoRa communication module and Wi-Fi module in the laboratory. The system was built (prototype) using radially available hardware devices from the local electronics shops at about USD 150. The transmitter can securely exchange IoT sensor data to the receiver node at about 10 km using a LoRa Wi-Fi module. The receiver node accumulates the sensor data and stores it in the cloud for processing. The performance of the proposed LoRa transceiver was evaluated by field experiments in which two transmitter nodes were deployed on the rooftop of Auckland University of Technology’s Tower building on city campus (New Zealand), and the receiver node was deployed in Liston Park, which was located 10 km away from the University Tower building. The manual incident field tests examined the accuracy of the sensor data, and the system achieved a data accuracy of about 99%. The reaction time of the transmitter nodes was determined by the data accumulation of sensor nodes within 2–20 s. Results show that the system is robust and can be used to effectively link city and suburban park communities.

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On Minimizing TCP Traffic Congestion in Vehicular Internet of Things (VIoT)

Jude¹,

Diniesh²,

Shivaranjani³

et al. 2022

Wireless Pers Commun

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TCP-LoRaD: A Loss Recovery and Differentiation Algorithm for Improving TCP Performance over MANETs in Noisy Channels

Cited by 4 publications

References 27 publications

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study

On Minimizing TCP Traffic Congestion in Vehicular Internet of Things (VIoT)

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