Retransmission Timeout Research Articles

МАТЕМАТИЧЕСКАЯ МОДЕЛЬ ПРОЦЕССА ДОСТАВКИ ТЕЛЕМЕТРИЧЕСКИХ СООБЩЕНИЙ В СИСТЕМЕ МЕДИЦИНСКОГО ИНТЕРНЕТА ВЕЩЕЙ

Currently, research and development are relevant aimed at increasing the efficiency of transmission of telemetry messages in wireless remote monitoring systems used by medical workers to provide timely assistance to patients under observation. The technical basis for transmitting such messages is provided by medical Internet of Things systems. The article is devoted to the development of a mathematical model of the process of delivering telemetric messages in such a system. In the course of the study, analytical expressions were obtained that make it possible to estimate the average delivery time of messages from the patient’s sensory device to the healthcare worker’s wireless device, depending on the response intervals of the retransmission timers installed in the transmitting and receiving elements of the system. The results obtained allow us to theoretically substantiate the choice of retransmission timeout values that ensure minimization of the average message delivery time at the current level of bit error intensity.

The analytical framework for optimizing TCP retransmission algorithm based on adjustment of its EWMA parameters

In this paper we derive mathematical description of TCP (Transmission Control Protocol) retransmission probability based on Jacobson’s smoothing algorithm that belongs to EWMA (Exponentially Weighted Moving Average) category. This description is parametrized on the probability density function (pdf) of RTT (Round Trip Time) samples and α, β – two primary parameters of Jacobson’s algorithm. Although it is not a close form expression, it is formulated as an effective algorithm that let us to explicitly calculate the values of RTO (Retransmission Time Out) probability as a function of α, β and the pdf of RTT samples. We achieve the effectiveness of this approach by applying smart discretization of the state space and replacement of continuous functions with discrete approximate equivalents. In this way, we mitigate the cardinality of discrete distributions we deal with that results in linear (n+m) instead of multiplicative (n⋅m) growth of computational complexity. We provide the evaluation of RTO probability for a wide set of α, β parameter values and differently shaped Normal and Laplace pdfs the RTT samples are taken from. The obtained numerical results let us to draw some conclusions regarding the choice of optimal values of α, β parameters as well as the impact of pdf the RTT samples are taken from.

An adaptive traffic engineering approach based on retransmission timeout adjustment for software-defined networks

An adaptive traffic engineering approach based on retransmission timeout adjustment for software-defined networks

AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones

As drones are widely employed in various industries and daily life, concerns regarding their safety have been gradually emerging. Denial of service (DoS) attacks have become one of the most significant threats to the stability of resource-constrained sensor nodes. Traditional brute-force and high-rate distributed denial of service (DDoS) attacks are easily detectable and mitigated. However, low-rate TCP DoS attacks can considerably impair TCP throughput and evade DoS prevention systems by inconspicuously consuming a small portion of network capacity, and whereas the literature offers effective defense mechanisms against DDoS attacks, there is a gap in defending against Low-Rate TCP DoS attacks. In this paper, we introduce AccFlow, an incrementally deployable Software-Defined Networking (SDN)-based protocol designed to counter low-rate TCP DoS attacks. The main idea of AccFlow is to make the attacking flows accountable for the congestion by dropping their packets according to their loss rates. AccFlow drops their packets more aggressively as the loss rates increase. Through extensive simulations, we illustrate that AccFlow can effectively safeguard against low-rate TCP DoS attacks, even when attackers employ varying strategies involving different scales and data rates. Furthermore, whereas AccFlow primarily addresses low-rate TCP DoS attacks, our research reveals its effectiveness in defending against general DoS attacks. These general attacks do not rely on the TCP retransmission timeout mechanism but rather deplete network resources, ultimately resulting in a denial of service for legitimate users. Additionally, we delve into the scalability of AccFlow and its viability for practical deployment in real-world networks. Finally, we demonstrate the effectiveness of AccFlow in safeguarding network resources.

UDP-RT: A UDP-based reliable transmission scheme for power WAPS

It is expected that TCP/IP networks take the place of point-to-point fiber channels for the communications of WAPS. In TCP/IP networks, TCP does not guarantee real-time while UDP does not guarantee reliability. An efficient method is demanded for WAPS to guarantee the real-time and reliability of messages transmitted in TCP/IP networks under congestion states. To address the challenge, we propose a UDP-based reliable transmission (UDP-RT) scheme, which achieves low latency by adopting UDP at the transport layer and high reliability by adding the mechanisms of error correction, error detection, resending and timeout retransmission at the application layer. The error correction mechanism employs TPCs, which has low complexity and good performance at high code rate, to correct errors in a message. A blocking rule for TPCs considering the features of communication channels and messages of WAPS is presented. The error detection mechanism is for detecting whether all errors in the message are corrected by the error correction mechanism. The resending mechanism and the timeout retransmission mechanism (optional) is for ensuring the reliability in the two cases of message loss and not all errors corrected. Additionally, algorithms for UDP-RT are presented, and their correctness are validated through experiment. Our analyses demonstrate that the proposed scheme can meet the real-time requirements of WAPS businesses when network congesting and has higher reliability than the TCP transmission scheme and other UDP transmission schemes.

R-AQM: Reverse ACK Active Queue Management in Multitenant Data Centers

TCP incast has become a practical problem for high-bandwidth, low-latency transmissions, resulting in throughput degradation of up to 90% and delays of hundreds of milliseconds, severely impacting application performance. However, in virtualized multi-tenant data centers, host-based advancements in the TCP stack are hard to deploy from the operators’ perspective. Operators only provide infrastructure in the form of virtual machines, in which only tenants can directly modify the end-host TCP stack. In this paper, we present R-AQM, a switch-powered reverse ACK active queue management (R-AQM) mechanism for enhancing ACK-clocking effects through assisting legacy TCP. Specifically, R-AQM proactively intercepts ACKs and paces the ACK-clocked in-flight data packets, preventing TCP from suffering incast collapse. We implement and evaluate R-AQM in NS-3 simulation and NetFPGA-based hardware switch. Both simulation and testbed results show that R-AQM greatly improves TCP performance under heavy incast workloads by significantly lowering packet loss rate, reducing retransmission timeouts, and supporting 16 times (i.e., 60 to 1000) more senders. Meanwhile, the forward queuing delays are also reduced by 4.6 times.

ANALYSIS OF NETWORK TRAFFIC THREATS ACROSS OSI MODEL LAYERS FOR DYNAMIC RTO CALCULATION IN THE CONTEXT OF COMBATING DDoS ATTACKS

This document provides an examination of current threats to network security, viewed through the lens of network traffic analysis at various OSI model layers. It delves into the different forms of Distributed Denial of Service (DDoS) attacks and their ramifications on the Transmission Control Protocol (TCP), with a specific focus on a critical parameter - the Retransmission Timeout (RTO). The text also divulges fundamental algorithms and techniques for calculating RTO, encompassing adaptive methodologies that harness machine learning and artificial intelligence for optimizing the TCP/IP stack. In particular, it offers insights into the functioning of the RTO calculation algorithm, a pivotal element ensuring the reliability of data transmission via TCP. The document elaborates on how this algorithm dynamically adjusts the RTO value based on network conditions and measured Round Trip Time (RTT) values. Furthermore, it furnishes formulas for computing RTO with diverse parameters. Moreover, the document explores the potential of employing machine learning and data analysis methodologies to detect and preempt DDoS attacks. It elucidates how contemporary technologies empower the use of these approaches to minimize false positives in identifying malicious traffic packets, thereby enhancing the effectiveness of safeguarding information systems. Additionally, it provides an illustration of software and hardware tools employed for the practical implementation of these algorithms in devices facilitating data transmission via Ethernet connections. In summary, this work offers insights into contemporary challenges and issues in the realm of network security, especially in the context of the escalating frequency of DDoS attacks. This information proves valuable for students and professionals engaged in the study of network security and the development of measures to fortify networks and systems.

Analysis of TCP Issues and Their Possible Solutions in the Internet of Things

The Internet of Things (IoT) is widely known as a revolutionary paradigm that offers communication among different types of devices. The primary goal of this paradigm is to implement efficient and high-quality smart services. It requires a protocol stack that offers different service requirements for inter-communication between different devices. Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) are used as transport layer protocols in IoT to provide the quality of service needed in various IoT devices. IoT encounters many shortcomings of wireless networks, while also posing new challenges due to its uniqueness. When TCP is used in an IoT system, a variety of challenging issues have to be dealt with. This paper provides a comprehensive survey of various issues which arises due to the heterogeneous characteristics of IoT. We identify main issues such as Retransmission Timeout (RTO) algorithm issue, congestion and packet loss issue, header overhead, high latency issue, link layer interaction issue, etc. Moreover, we provide several most probable solutions to the above-mentioned issues in the case of IoT scenarios. RTO algorithm issue has been resolved by using algorithms such as CoCoA, CoCoA+, and CoCoA++. Apart from these, the high latency issue has been solved with the help of a long lived connection and TCP Fast open. Congestion and packet loss issue has been resolved by using several TCP variants such as TCP New Reno, Tahoe, Reno, Vegas, and Westwood.

A Study of Transmission Overhead of a Hybrid Bundle Retransmission Approach for Deep-Space Communications

Bundle protocol (BP) was proposed as the core protocol of delay/disruption-tolerant networking for space. A hybrid reliable transmission mechanism based on integrating “proactive” retransmission and “reactive” retransmission of BP bundles has been presented as a means of achieving highly reliable file/data delivery in deep-space communications. Transmission overhead imposed by reliable delivery of data packets/bundles is consistently one of the key factors for efficient data delivery in space communications. It is especially important in deep-space networks, where the available electrical power on spacecraft for data delivery is a scarce resource but substantial energy is needed to propagate data over an extremely long deep-space channel. In this article, a detailed study of the transmission overhead for the hybrid retransmission approach of BP for reliable deep-space communications is presented. An analytical framework is presented for modeling the transmission overhead of BP as influenced by variations in channel quality, file size, bundle size, and retransmission timeout interval length. Its energy resource consumption is also briefly discussed. The analytical framework is validated by realistic file transfer experiments using a PC-based testbed infrastructure; the framework is found to be able to predict the performance with respect to the transmission overhead of BP.

Acknowledgment Mechanisms for Reliable File Transfer Over Highly Asymmetric Deep-Space Channels

Deep-space network channels are characterized “in part” by asymmetric channel rates. The effect of the asymmetric channel rates is that the slow acknowledgement (ACK) channel cannot effectively support transmission of the ACK segments generated at the data receiver. Increment of data block size and extension of the retransmission timeout (RTO) timer are widely used transmission mechanisms for eliminating the effect of highly channel-rate asymmetry. The aggregate custody signal (ACS) is recently proposed to resolve the effect of asymmetric channel rates in deep-space networks. In this article, these three acknowledgment mechanisms are studied, with a focus on a comparison of their effectiveness evaluation over highly asymmetric deep-space network channels accompanied by data loss. The study is conducted through realistic file delivery via an experimental testbed infrastructure. It is found that the ACS mechanism shows significant performance advantages over other two mechanisms for both the “normalized” goodput and total data amount in successful file delivery. In particular, the ACS mechanism makes it a better choice to operate with small data bundles without increasing the RTO timer length.

Performance modeling of Licklider transmission protocol (LTP) via proactive transmission of signaling segments in deep‐space network

SummaryDelay‐tolerant networking (DTN) bundle protocol is considered one of the best transmission protocols to be used in space communications by NASA. There are studies that evaluated the performance, measuring the total transfer time of a complete file from sender to receiver, of the DTN protocol via simulation or emulated experiments beyond the real space‐based experiences. In addition, there is a very few additional research works available for the modeling of the Licklider transmission protocol (LTP). However, these past studies on LTP modeling are implemented as a Logarithmic approach that is based on the calculation of loss segments in file delivery. In this work, the approach is investigated in a different manner. Specifically, the performance model was created based on the probability of the segment's successful delivery on the LTP. Moreover, the original model is enhanced via introducing a burst of transfer and equal interval distribution of signaling segments, specifically check point (CP), along with the retransmission time out (RTO). The results from the developed model for the original DTN protocol and enhanced versions align with the results obtained by PC‐based testbeds.

A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT

The lack of standardization and the heterogeneous nature of the Internet of Things (IoT) has exacerbated the issue of security and privacy. In literature, to improve security at the network layer of the IoT architecture, the possibility of using Software-Defined Networking (SDN) was explored. SDN is also plagued by network threats that affect conventional networks. One such threat to a network is the Low-Rate Denial of Service (LR DoS) attack, where the attacker sends precise traffic bursts that force a TCP flow to enter a retransmission timeout state. LR DoS attacks are difficult to detect as their attack signature is similar to benign network traffic. The existing AI-based detection algorithms in the literature are signature-based, and their efficacy in detecting unknown LR DoS attacks was not explored. In this work, an AI-based anomaly detection scheme called FeedForward–Convolutional Neural Network (FFCNN) is proposed to detect LR DoS attacks in IoT-SDN. The Canadian Institute of Cybersecurity Denial of Service 2017 (CIC DoS 2017) dataset is used for the study. An iterative wrapper-based feature selection using Support Vector Machine (SVM) is used to derive the significant features required for detection. The performance of FFCNN is compared to the machine learning algorithms-J48, Random Forest, Random Tree, REP Tree, SVM, and Multi-Layer Perceptron (MLP). The performance of the models is measured using the metrics accuracy, precision, recall, F1 score, detection time per flow, and ROC curves. The empirical analysis shows that FFCNN outperforms other machine learning algorithms on all metrics.

An improved retransmission timeout prediction algorithm for enhancing data transmission on internet of vehicles network

An improved retransmission timeout prediction algorithm for enhancing data transmission on internet of vehicles network

Wireless retransmission timeout algorithm for multi‐hop vehicular network

In multi‐hop vehicular links, Transmission Control Protocol (TCP) timer algorithm plagued forecasting during impulsive Round Trip Time (RTT) delay results in early timeout, adversely affecting TCP's throughput performance. The Wireless Retransmission Timeout Plus (WRTO+) algorithm proposed in this letter introduces an additional timer parameter and adaptive weights to fix future packet duration's timer value. During each iteration, the WRTO+ computes a variability factor to fix the weights. The WRTO+ simulation outcomes substantiate the considerable drop in the early timeout and improve average throughput, transmission efficiency, and packet latency performance of TCP traffic in vehicular networks.

An accurate retransmission timeout estimator for content-centric networking based on the Jacobson algorithm

Accurately estimating of Retransmission TimeOut (RTO) in Content-Centric Networking (CCN) is crucial for efficient rate control in end nodes and effective interface ranking in intermediate routers. Toward this end, the Jacobson algorithm, which is an Exponentially Weighted Moving Average (EWMA) on the Round Trip Time (RTT) of previous packets, is a promising scheme. Assigning the lower bound to RTO, determining how an EWMA rapidly adapts to changes, and setting the multiplier of variance RTT have the most impact on the accuracy of this estimator for which several evaluations have been performed to set them in Transmission Control Protocol/Internet Protocol (TCP/IP) networks. However, the performance of this estimator in CCN has not been explored yet, despite CCN having a significant architectural difference with TCP/IP networks. In this study, two new metrics for assessing the performance of RTO estimators in CCN are defined and the performance of the Jacobson algorithm in CCN is evaluated. This evaluation is performed by varying the minimum RTO, EWMA parameters, and multiplier of variance RTT against different content popularity distribution gains. The obtained results are used to reconsider the Jacobson algorithm for accurately estimating RTO in CCN. Comparing the performance of the reconsidered Jacobson estimator with the existing solutions shows that it can estimate RTO simply and more accurately without any additional information or computation overhead.

Geometric Series based effective RTO estimation Technique for CoCoA

Constrained Application Protocol (CoAP) is a standard data transfer protocol for Internet of Things. It has an in-built support for the basic congestion control mechanism that uses fixed Retransmission Time Out (RTO) for every transmission regardless of Round Trip Time (RTT), and performs Binary Exponential Backoff (BEB) when the packets get dropped. CoAP Simple Congestion Control/Advanced (CoCoA) is an enhanced congestion control mechanism over CoAP that adapts RTO based on RTT. It maintains Strong and Weak RTO estimators and uses a Variable Backoff Factor (VBF) instead of BEB when the packets get dropped. CoCoA uses an Exponential Weighted Moving Average (EWMA) to estimate the RTO for the next transmission. The weight used in EWMA is determined on the basis of whether the RTT estimated for the recent transmission was a Strong RTT or Weak RTT. However, the weights used to estimate the RTO are fixed (0.5 for Strong and 0.25 for Weak). These fixed weights lead to slow adaptation of RTO and affect the performance of the IoT applications. In this paper, we highlight the impact of having fixed weights while estimating the RTO in CoCoA. In particular, we show that the RTO in CoCoA fails to adapt quickly when the network conditions are lossless because it uses a fixed value for Strong RTO estimation (0.5). We propose a new algorithm called Geometric Series based effective RTO estimation Technique for CoCoA (GSRTC) to adapt the weight used in EWMA for estimating Strong RTO. GSRTC is integrated into CoCoA and validated against existing mechanisms using the Cooja simulator in Contiki OS and in a real testbed FIT/IoT-LAB. Our results show that GSRTC has lower Flow Completion Times (FCT), lesser retransmissions and better network throughput.

On the design of efficient congestion control for the Constrained Application Protocol in IoT

The Constrained Application Protocol (CoAP) is one of the main candidates for a lightweight communication protocol for the Internet-of-Things. CoAP provides a simple congestion control mechanism based on successive retransmissions and binary exponential timeouts. This simple mechanism significantly reduces CoAP performance especially in networks with high packet loss, and thus preventing an efficient deployment of the protocol. Enhanced mechanisms for CoAP were proposed in the literature. Some considered improving retransmission timeout estimation whereas others focused on augmenting the retransmission procedure. In this work, we analyze deeply main and recent proposals to highlight their shortcomings. Then, we propose and implement two congestion control algorithms IDC-CoAP and MBC-CoAP which improve retransmission timeout estimation for congestion detection, and adopt adequately a rate-based approach for congestion counteraction, while maintaining simplicity required by constrained devices. The two proposed algorithms are evaluated by means of pure simulations considering several network scenarios, and also using the realistic environment Cooja/Contiki. All results show that our algorithms achieve a much better tradeoff between goodput, reliability and overhead.

Machine Learning to Optimize TCP Communications Over High Frequency Communications Links

High Frequency communication is a proven method of beyond line of sight (BLOS) communications for decades. With the advent of Wideband HF (WBHF), the ability to communicate data and more specifically TCP over HF is being researched worldwide. HF-TCP, an optimised TCP for HF links provides improved TCP communication over challenging High Frequency (HF) communication systems. It relies on the modification of Forced Retransmission Timeout (fRTO) and Mean Segment Size (MSS) to improve the reliability of communication sessions. Calculating the fRTO for a communication session that would provide the optimum result over a communication link is a complex task due to the timers that manage the session and the HF channel characteristics. In this paper, we propose the use of Machine Learning (ML) techniques to dynamically predict fRTO and MSS to improve the fRTO calculation process for all communication instances. To achieve this, a Predictor Model is used to predict fRTO and MSS while an Optimiser model optimises the Predictor model’s output. Decision Tree Regression was proven to be the most accurate among the various ML algorithms tested with 82 percent prediction accuracy. The performance of HF-TCP with proposed predicted fRTO and MSS is compared with that of standard TCP and the performance of the Predictor and Optimiser models is also analysed. The results show 72 percent of instances have an improvement in link efficiency when using HF-TCP with predicted fRTO and MSS over standard TCP.

An XGB-Based Reliable Transmission Method in the mMTC Scenarios

Massive machine-type communications (mMTCs) for Internet of things are being developed thanks to the fifth-generation (5G) wireless systems. Narrowband Internet of things (NB-IoT) is an important communication technology for machine-type communications. It supports many different protocols for communication. The reliability and performance of application layer communication protocols are greatly affected by the retransmission time-out (RTO) algorithm. In order to improve the reliability and performance of machine-type communications, this study proposes a novel RTO algorithm UDP-XGB based on the user datagram protocol (UDP) and NB-IoT. It combines traditional algorithms with machine learning. The simulation results show that real round-trip time (RTT) is close to the RTO, which is obtained by this algorithm, and the reliability and performance of machine-type communications have improved.

Resource Consumption of a Hybrid Bundle Retransmission Approach on Deep-Space Communication Channels

Bundle protocol (BP) is the core protocol of delay/disruption-tolerant networking, which has been recognized as the only candidate networking technology that approaches the level of maturity required for deep-space mission communications. BP is intended to establish an overlay network for reliable data transfer over highly unreliable space links. A hybrid reliability strategy based on combining “proactive” retransmission and “reactive” retransmission of BP bundles is presented in this article as a means of achieving highly reliable file/data delivery in deep-space communication networks. Available electrical power on spacecraft is widely recognized as a scarce resource, especially for spacecraft in deep space where energy consumption is an important consideration for communications. In this article, the hybrid bundle retransmission approach is studied by means of a file transfer experiment over a PC-based testbed. The study focuses on variation in the transmission overhead and energy consumption of BP as a function of file size and retransmission timeout interval length.