Round Trip Time Estimation Research Articles

Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables.

Received signal strength (RSS) has been one of the most used observables for location purposes due to its availability at almost every wireless device. However, the volatile nature of RSS tends to yield to non-reliable location solutions. IEEE 802.11mc enabled the use of the round trip time (RTT) for positioning, which is expected to be a more consistent observable for location purposes. This approach has been gaining support from several companies such as Google, which introduced that feature in the Android O.S. As a result, RTT estimation is now available in several recent off-the-shelf devices, opening a wide range of new approaches for computing location. However, RTT has been traditionally addressed to multilateration solutions. Few works exist that assess the feasibility of the RTT as an accurate feature in positioning methods based on classification algorithms. An attempt is made in this paper to fill this gap by investigating the performance of several classification models in terms of accuracy and positioning errors. The performance is assessed using different AP layouts, distinct AP vendors, and different frequency bands. The accuracy and precision of the RTT-based position estimation is always better than the one obtained with RSS in all the studied scenarios, and especially when few APs are available. In addition, all the considered ML algorithms perform pretty well. As a result, it is not necessary to use more complex solutions (e.g., SVM) when simpler ones (e.g., nearest neighbor classifiers) achieve similar results both in terms of accuracy and location error.

Analytical RTT Estimation of a 3-D Elevator System by Exact Stop Positions - Extension to Multi-floor and Non-uniform Population Applications

3-dimensional (3-D) elevator systems will be the industrial trend. Traditionally, designers first perform a calculation on traffic analysis to get an overall concept and then go ahead to carry out simulation to obtain details. This tradition is still maintained throughout the world. In one previous article, a “Scanner” approach was adopted to convert a 2-D or 3-D path of scanning to a 1-dimensional line and a sophisticated origin/destination stops matrix was generated to calculate the round trip time (RTT). A probability matrix was generated with an assumption that only one floor was served in one round trip. In a more recent article, by using order statistics, a method to break down a series of repeatable stops in a 3-D system into several series of non-repeatable stops was developed for easy reasoning by designers. Here, a uniform population distribution of all potential stops and a single floor service were assumed. In this article, by following the concept of the most recent article, RTT calculation could be extended to non-uniform population distribution of stops and multi-floor applications which may be the most general approach to calculate the RTT of a truly 3-D elevator system.

Round-Trip Time and Available Bandwidth Estimation Based Congestion Window Reduction Algorithm of MPTCP in Lossy Satellite Networks

A significant enhancement in recent communication systems is Satellite Communications. The performance improvement of the transmission control protocol (TCP) in satellite communications has been an active research area. Multipath TCP (MPTCP) ensures a robust solution than the regular TCP. However, the performance degradation in satellite networks due to the link asymmetry, high loss and high latency cannot distinguish the packet losses caused by network congestions from the losses properly. In this paper, a round-trip time detection-based algorithm is proposed using available bandwidth estimation (ABE) to mitigate the issue of blind decreasing of congestion window upon detecting the packet loss over lossy satellite networks. Experiments are done using the NS-2 network simulator to compare the various congestion control algorithms of both TCP and MPTCP over satellite networks. Simulations results from the experiments show that our proposed algorithm outperforms the existing algorithms which are mentioned in this article.

Improving Congestion Control of TCP for Constrained IoT Networks.

For smooth integration with middleboxes on the Internet, TCP (Transmission Control Protocol) is favorably considered as a transport-layer protocol for IoT (Internet of Things) networks. In constrained networks, TCP tends to perform well with a small window size. For example, the uIP (micro IP) TCP/IP stack sets the TCP window size to one segment by default. In such a case, managing the retransmission timer is a primary approach to congestion control. In this paper, we examine the congestion control mechanism of TCP in the uIP stack using grid topology networks. In the preliminary test using the Cooja network simulator, the results show that the original uIP TCP causes lots of retransmissions when a radio duty cycling mechanism such as ContikiMAC is used. One main reason is that, once retransmission is deemed to be necessary, the original uIP TCP sets the retransmission timer based on the fixed RTO (retransmission timeout) before performing a retransmission. Since ContikiMAC may cause large RTT (round-trip time) variations due to the hidden terminal problem, the retransmission timer based on the fixed RTO value may cause lots of retransmissions. To address the problem, we propose a new scheme for managing the retransmission timer which adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Simulation results show that our proposed scheme reduces retransmissions while enhancing throughput and fairness when an RDC (radio duty cycling) mechanism is used.

Analytical round-trip time estimation of a three-dimensional elevator system based on exact stopping position identification

Two-dimensional (2-D) as well three-dimensional (3-D) elevators are expected to become a vital part of the future building transportation systems. In order to carry out an elevator traffic system design for a 3-D system, it is necessary evaluate the round-trip time. A ‘scanning’ approach has been previously developed to find the value of the round trip time in 2-D and 3-D systems. The 3-D path of scanning could be linearized into a 1-D line as in a conventional elevator system. It was later found that the RTT is insensitive to the exact stopping positions in a 1-D system, but extremely sensitive in a 2-D or 3-D system. Furthermore, in the conventional 1-D traffic analysis, the average number of stops is non-integral because stops are repeatable within trips, causing a great trouble in estimating RTT in a 3-D system. In this article, with the help of order statistics, the method to break down a series of repeatable stops in a 3-D system into several series of non-repeatable stops is described in detail. And the number of stops of all these non-repeatable series becomes integral, making it suitable for analytical calculation. Then, the RTT of each “non-repeatable” series is separately estimated analytically, with the overall RTT with repeatable stops given by the weighted sum of all these “non-repeatable” series. Methods to estimate the average RTT of each non-repeatable series, the probability density function of each series, and the final weighted average are discussed. Finally, the results are verified using the Monte Carlo simulation method. The method described in this article can give designers a much clearer understanding of the overall concept of RTT estimation by calculation in a multi-dimensional system.

Joint image formation and two-dimensional autofocusing for synthetic aperture radar data

Imaging via synthetic aperture radar (SAR) is a well-established technique for effective scene reconstruction, with resolution up to a few centimeters. The measurement process requires the round trip time for the electromagnetic waves to travel to the scene and return back to the sensing mechanism. While hypothetically the round trip time can be exactly determined, in practice this distance can only be approximated, leading to errors in the round trip time estimates. These errors manifest as phase errors on the data and produce defocused imagery, making information extraction difficult. This investigation develops an autofocusing technique that exploits the correlation of the phase error on both the azimuth angle and spatial (cycles/meter) frequencies while also enforcing the piecewise smooth nature of the image. Our method estimates the phase error correction and the image through a joint optimization procedure. Specifically, our method incorporates a phase synchronization technique to estimate the unknown two-dimensional phase error. High order regularization is used in the optimization procedure, which helps to reduce speckle in the SAR image.

Adaptive RTO for handshaking-based MAC protocols in underwater acoustic networks

Underwater acoustic networks (UANs) are attracting interest in recent decades. The unique characteristics of the underwater acoustic channel, such as long propagation delay, delay variance, and high bit error rate, present challenges for the medium access control (MAC) protocol design in UANs. Most existing medium access control protocols ignore the delay variance which prevents the accurate estimation of round trip time (RTT). The expected RTT value can be used to compute the Retransmission Time-Out (RTO) or the waiting time in MAC. The estimation of RTT is also meaningful for Automatic Repeat re-Quest (ARQ) scheme because the system should ensure reliable data transmissions in the presence of high bit error rate in the underwater acoustic channel. By analyzing the impact of RTO on throughput under the effect of delay variance, we conclude that the fixed RTO is inefficient and RTO should be adaptively set to improve the throughput. We present a novel approach of predicting the RTT using a Bayesian dynamic linear model, and then adjust RTO adaptively according to the predicted values. Simulation results show that the predicted values can adapt quickly to the sample RTT values. Under the effect of RTT fluctuations, the Bayesian algorithm offers performance gains in terms of throughput and prediction performance, comparing with Karn’s algorithm. Our study highlights the value of predicting the RTT using Bayesian approach in underwater acoustic networks.

Invs: A New Congestion Control Algorithm for Heterogeneous Networks

The available bandwidth, round trip time (RTT) and packet loss rate in the Internet can vary over many orders of magnitude, which is known as the heterogeneity of the Internet. To cope with the heterogeneity in Internet Congestion Control, we propose a new TCP protocol, namely Invs, that has three key components. First, it employs a novel increase function of congestion window with an adaptive increase factor that adjusts through estimation of path bandwidth-delay product (BDP) and minimum RTT. The increase function can modulate the window growth rate to fit with the path condition. Second, Invs adopts a novel scheme that adaptively adjusts the queue threshold in loss classification, which distinguishes non-congestion loss from congestion loss. Third, Invs introduces a new back-off scheme to enable quick convergence of congestion window. The performance analysis and evaluation show that Invs achieves good fairness, friendliness, RTT fairness and utilization in heterogeneous networks.

CoAP congestion control for the internet of things

CoAP is a lightweight RESTful application layer protocol devised for the IoT. Operating on top of UDP, CoAP must handle congestion control by itself. The core CoAP specification defines a basic congestion control mechanism, but it is not capable of adapting to network conditions. However, IoT scenarios exhibit significant resource constraints, which pose new challenges on the design of congestion control mechanisms. In this article we present CoCoA, an advanced congestion control mechanism for CoAP being standardized by the Internet Engineering Task Force CoRE working group. CoCoA introduces a novel round-trip time estimation technique, together with a variable backoff factor and aging mechanisms in order to provide dynamic and controlled retransmission timeout adaptation suitable for the peculiarities of IoT communications. We conduct a comparative performance analysis of CoCoA and a variety of alternative algorithms including state-of-the-art mechanisms developed for TCP. The study is based on experiments carried out in real testbeds. Results show that, in contrast to the alternative methods considered, CoCoA consistently outperforms the default CoAP congestion control mechanism in all evaluated scenarios.

Modeling RTT for DTN Protocol Over Asymmetric Cislunar Space Channels

Extensive work has been done in the performance evaluation of delay-/disruption-tolerant networking (DTN) technologies in cislunar and deep-space communications using simulation or emulation file transfer experiments. However, little work has been seen for studying in an analytical manner the performance of DTN protocols for space communications characterized by asymmetric channel rates. Accurate estimate of file delivery latency [i.e., round trip time (RTT)] is essential for efficient transmission control, reliable delivery, and bandwidth usage optimization of a protocol. In this paper, we present a performance analysis of the main DTN convergence layer adapter (CLA), i.e., Licklider transmission protocol (LTP) CLA (LTPCL adapter), over cislunar space channels, focusing on RTT estimates, in the presence of asymmetric channel rates. Analytical models are built for the RTT estimate of LTPCL transmissions with the effect of delay caused by space channel-rate asymmetry, channel impairment, and link disruption taken into consideration. The models are validated by file transfer experiments using a PC-based test bed. It is found that a smaller LTP data block size results in a longer delay in acknowledgement (ACK) transmission and thus a longer RTT. With a block size equal to or larger than a calculated threshold size, the ACK delay is avoided; thus, their RTT is consistently very low, leading to significantly shorter file delivery time and goodput advantage.

실감교류를 위한 비디오 재생 버퍼 관리 방안

본 논문에서는 실감교류 멀티미디어 서비스에 적합한 버퍼 관리 방안을 제안한다. 수신 버퍼크기가 왕복 시간 추정에 따라 달라질 수 있도록 전형적인 지연 최적화 환경을 고려한다. 이러한 환경에서, 버퍼 크기 단축 시 버퍼 내에 I/P/B 프레임을 드롭하는 경우 발생할 수 있는 정보 손실을 최소화하기 위한 최적화 기법을 제안한다. 근사 해를 찾기 위해 동적 프로그래밍을 이용하는 Knapsack Problem으로 문제를 모델링한다. 제안된 기법은 기존의 버퍼 관리 기법과 비교된다. 시뮬레이션 연구를 통해, 제안하는 접근 방식은 비디오 품질에 중요한 PSNR을 증가시킬 수 있음을 확인하였다.

Refining TCP's RTT dependent mechanism by utilizing link retransmission delay measurement in Wireless LAN

SummaryNetwork utilization by legacy transmission control protocol (TCP) is determined by its round trip time (RTT) dependent mechanism for the growth of its sending rate. The RTT does not always reflect the actual network conditions, especially in the case of wireless local area network (WLAN). Consequently, it influences the RTT‐dependent mechanism falsely. This paper proposes a novel cross‐layer scheme between TCP and the Institute of Electrical and Electronics Engineers (IEEE) 802.11 medium access control (MAC) that compensates for any diminished growth of TCP's sending rate because of the inclusion of non‐congestion delay component in RTT, if any. The proposed scheme has two refinements. The first refinement is at the MAC layer that notifies the additional propagation delay on account of link retransmission of a TCP packet. The second refinement is at the TCP layer in which the sender adapts the sending rate by relating the aforementioned additional propagation delay to the RTT estimation. To examine the benefits, the proposed scheme is evaluated after implementing it on top of representative TCP variants; Newreno and Westwood+ (TCPW). Simulation results showed that with the proposed scheme, network utilization by native TCP is improved by more than 35% in a dedicated WLAN environment, while the performance remained protected in a shared WLAN environment. Copyright © 2015 John Wiley & Sons, Ltd.

Providing explicit congestion control and multi-homing support for content-centric networking transport

Content-centric networking (CCN) adopts a receiver-driven, hop-by-hop transport approach that facilitates in-network caching, which in turn leads to multiple sources and multiple paths for transferring content. In such a case, keeping a single round trip time (RTT) estimator for a multi-path flow is insufficient as each path may experience different round trip times. To solve this problem, it has been proposed to use multiple RTT estimators to predict network condition. In this paper, we examine an alternative approach to this problem, CHoPCoP, which utilizes explicit congestion control to cope with the multiple-source, multiple-path situation. In addition, a parallelized version of CHoPCoP, pCHoPCoP, is designed to support bandwidth aggregation on multi-homed hosts. We have implemented CHoPCoP/pCHoPCoP on the ORBIT testbed and conducted experiments under various network and traffic settings. The evaluation shows that CHoPCoP can effectively deal with congestion in the multipath environment, and pCHoPCoP can maximize the network utilization for a multi-homed host.

A Study on Impacts of RTT Inaccuracy on Dynamic Bandwidth Allocation in PON and Solution

The circle travelling delay between OLT (Optical Line Terminal) and ONU (Optical Network Unit) is one of most important items in dynamic bandwidth allocation (DBA) algorithms in PON, called RTT (Round Trip Time). The RTT is taken into account when OLT assigns the start times for upstream bandwidth grants. In most case, RTT is estimated before making bandwidth allocation decisions in dynamic bandwidth allocation algorithms. If the estimated RTT is incorrect, the bandwidth allocation decisions are not matched with bandwidth requests of channels. Thus, performance of PON can get worse by deviation of RTT. There are several reasons that cause the RTT to be varying, such as processing delay, distance of OLT and ONU, changing in fiber refractive index resulting from temperature drift, and degree of accuracy of RTT estimation methods. In this paper, we evaluate the impacts of RTT inaccuracy on performance of DBA and identify levels of collision and waste of bandwidth. By this way, we propose a method to remedy the performance degradation encountered by the situation

A machine learning framework for TCP round-trip time estimation

Abstract In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the experts framework. In our proposal, each of several ‘experts’ guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference between the estimated and actual RTT. Through extensive simulations, we show that the proposed machine-learning algorithm adapts very quickly to changes in the RTT. Our results show a considerable reduction in the number of retransmitted packets and an increase in goodput, especially in more heavily congested scenarios. We corroborate our results through ‘live’ experiments using an implementation of the proposed algorithm in the Linux kernel. These experiments confirm the higher RTT estimation accuracy of the machine learning approach which yields over 40% improvement when compared against both standard transmission control protocol (TCP) as well as the well known Eifel RTT estimator. To the best of our knowledge, our work is the first attempt to use on-line learning algorithms to predict network performance and, given the promising results reported here, creates the opportunity of applying on-line learning to estimate other important network variables.

무선 네트워크에서 스트리밍 서비스의 품질향상을 위한 Cross-layer 기반 적응적 전송률 조절 기법

무선 네트워크에서 TFRC (TCP-Friendly Rate Control)는 불안정한 채널 특성으로 인해 발생하는 손실을 모두 혼잡 손실로 인지할 뿐만 아니라, 링크 계층에서의 재전송 기법과 채널 경쟁 오버헤드로 인해 증가된 RTT(Round Trip Time)를 적용할 경우 전송 성능이 저하된다. 본 논문에서는 무선 네트워크 환경에서 스트리밍 서비스의 품질 향상을 위한 Cross-layer 기반의 적응적 전송률 조절 기법을 제안한다. 제안하는 기법은 TFRC의 전송률 향상을 위해 새로운 RTT 측정 및 패킷 손실 구별 기법을 적용하였다. 실험을 통해 제안한 기법을 적용한 TFRC의 전송 성능이 향상됨을 확인하였다. TFRC(TCP-Friendly Rate Control) has a performance degradation in wireless networks because it performs congestion control by judging all the losses occurred in wireless networks as a congestion indicator. It is also degraded by the increased Round Trip Time(RTT) due to packet retransmission and contention overhead in the link layer. In this paper, we propose an adaptive rate control scheme based on cross-layer to improve the quality of streaming services in the wireless networks. It provides new RTT estimation and loss discrimination methods to improve transmission rate of TFRC. The simulation results show that the proposed scheme can improve the performance of TFRC.

$QRTT$: Stateful Round Trip Time Estimation for Wireless Embedded Systems Using $Q$-Learning

Wireless embedded systems such as sensor nodes and smartphones highlight the importance of reliable data transmission in their advanced applications. Such reliable transmission frequently exhibits a performance below expectation due to inherent resource constraints of the systems. Accurate estimation of round trip time, an important parameter of reliable data transmission, has the potential to significantly improve the performance withstanding the resource constraints. However, contemporary round trip time estimation schemes cannot significantly improve the performance using their stateless estimation schemes as two different states of transmission, success and failure, are equally prominent over wireless mediums. To address this key fact, we propose a novel stateful round trip time estimation scheme for wireless embedded systems, incorporating a light-weight artificial intelligence method. We apply the proposed scheme in a retransmission timeout mechanism of TCP and compare the efficacy of the scheme against that of contemporary estimation schemes. Exhaustive simulation and a testbed experiment reveal that our scheme significantly improves network throughput and average energy per bit compared to other schemes.

Estimation of missing RTTs in computer networks: Matrix completion vs compressed sensing

We estimate the missing round trip time (RTT) measurements in computer networks using doubly non-negative (DN) matrix completion and compressed sensing. The major contributions of this paper are the following: (i) an iterative DN matrix completion that minimizes the mean square estimation error; (ii) mathematical conditions for the convergence of the algorithm; (iii) systematic and detailed experimental comparison of DN matrix completion and compressed sensing for estimating missing RTT estimation in computer networks. To our knowledge, this is the first work that compares the pros and cons of compressed sensing and DN matrix completion for RTT estimation using actual Internet measurement data. Results indicate that compressed sensing provides better estimation in networks with sporadic missing values while DN completion of matrices is more suitable for estimation in networks which miss blocks of measurements. Our proposed DN matrix completion method is one of the first approaches to matrix completion, that minimizes the estimation error.

Real-time video streaming over multipath in multi-hop wireless networks

Given the limited wireless link throughput, high loss rate, and varying end-to-end delay, supporting video applications in multi-hop wireless networks becomes a challenging task. Path diversity exploits multiple routes for each session simultaneously, which achieves higher aggregated bandwidth and potentially decreases delay and packet loss. Unfortunately, for TCP-based video streaming, naive load splitting often results in inaccurate estimation of round trip time (RTT) and packet reordering. As a result, it can suffer from significant instability or even throughput reduction, which is also validated by our analysis and simulation in multi-hop wireless networks. To make real-time TCP-based streaming viable over multi-hop wireless networks, we propose a novel cross-layer design with a smart traffic split scheme, namely, multiple path retransmission (MPR). MPR differentiates the original data packets and the retransmitted packets and works with a novel QoS-aware multi-path routing protocol, QAOMDV, to distribute them separately. MPR does not suffer from the RTT underestimation and extra packet reordering, which ensures stable throughput improvement over single-path routing. Through extensive simulations, we further demonstrate that, as compared with state-of-the-art multi-path protocols, our MPR with QAOMDV noticeably enhances the TCP streaming throughput and reduces bandwidth fluctuation, with no obvious impact to fairness.

Available and safe message freshness detection algorithm

The detection of messages delayed more than a given threshold – called 'message freshness detection' – is an important requirement in many distributed critical real-time systems. In this paper, a solution for addressing this requirement is described and deeply analysed. Our solution, the 'available and safe freshness detection algorithm (ASFDA)', was proposed for a distributed infrastructure for railway control, but it fits also to different systems with similar characteristics. ASFDA allows the detection of 'old' messages: violations of real-time requirements. ASFDA is based on round trip time estimation technique, a well known method used in distributed systems with real-time requirements. In the paper, we demonstrate that ASFDA can detect all the messages older than allowed; we also demonstrate, through an availability analysis performed by simulation, that the availability penalty reached by the proposed freshness detection algorithm is practically negligible if compared with its theoretical limits.