Impact Of Packet Loss Research Articles

Impact of network degradation on load frequency control of large interconnected power system

Due to the extensive geographical coverage of the power system, the load frequency control (LFC) performance is impacted by communication delays and packet loss that occur during the exchange of control and measurement signals. In the literature on LFC of large interconnected power systems, communication delays are often assumed to be arbitrary or time-varying values based on probability distributions or estimation models. Most studies neglect realistic delays based on the actual communication technology present in the system despite its practical significance. Furthermore, the impact of packet loss in LFC studies has been largely overlooked in the existing literature. This research work presents a comprehensive investigation into the impact of real-time communication delay and packet loss on LFC performance in large interconnected power systems. The simulation results demonstrate the significant impact of communication network impairments on LFC performance. The classical simulation results are validated using Real-Time Hardware-in-the-Loop (RT-HIL) simulation.

Enhancing MPTCP performance in wireless networks with PRS-MPTCP

In recent years, there has been a significant increasing demand for wireless networks, particularly mobile communication. Additionally, smart devices like mobile phones and tablets are able to use multiple interfaces simultaneously. In this regard, the concept of Multipath TCP (MPTCP) has been introduced, enabling the utilization of multiple interfaces for concurrent communication. However, packet loss and subflow heterogeneity, especially in wireless networks, cause an increase in Out-of-Order (OfO) packets at the receiver node, which leads to a decrease in the total MPTCP throughput. To address these performance-related challenges, numerous schedulers have been proposed. However, most existing methods have primarily focused on improving performance without adequately considering the impact of packet loss. This research paper provides a comprehensive overview of MPTCP schedulers. Subsequently, we propose a Practical and Robust Scheduler for MPTCP (PRS-MPTCP) with the specific aim of minimizing OfO packets to improve MPTCP performance in wireless systems. The PRS-MPTCP scheduler takes into account various characteristics of each subflow, including RTT, CWND, and the number of packet losses, to effectively decide which packets should be assigned to which subflow. By using Mininet-WiFi emulation, fair comparisons between PRS-MPTCP and the state-of-the-art schedulers have been conducted, considering throughput, the number of OfO packets, and retransmission rates as performance metrics. The evaluation results reveal the profound impact of selected parameters on the behavior of the schedulers and the overall performance of MPTCP. Ultimately, the results demonstrate that PRS-MPTCP guarantees acceptable throughput and achieves lower retransmission rates and fewer OfO packets compared to other methods. In the PRS-MPTCP, the number of OfO packets has decreased by 38 %, 37 %, 45 %, and 44 % compared to BLEST, ECF, RR, and the Default scheduler, respectively.

Vehicle lane-change intention recognition based on BiLSTM Attention model for the Internet of vehicles

In terms of lane-changing and other driver actions, precise identification of the intentions of nearby vehicles is crucial to autonomous vehicle performance safety. At present, research in this domain primarily focuses on ideal environments without considering data packet loss. Therefore, this paper considered the impact of packet loss in the Internet of Vehicles on the performance of the lane change intent recognition model. To achieve this, an enhanced BiLSTM Attention model, which combines the bidirectional long short-term memory network structure and attention mechanism, is proposed based on LSTM. The NGSIM (Next Generation Simulation) dataset was utilized to extract vehicle lane-change behaviors for model training and testing. A long short-term memory (LSTM) model was employed to conduct comparative experiments using various input frequencies and packet loss rates. The performance of the proposed BiLSTM Attention model was evaluated through ablation experiments. A comparison was made between the model’s performance in the absence of packet loss and its performance under a scenario with 30% packet loss. Additionally, the impact of continuous packet loss on the recognition of the lane-change intent model was analyzed. Experiments show that it outperforms basic LSTM and BiLSTM models, including the LSTM Attention method, with impressive improvements of 7.84%, 2.22%, and 4.89% (F1macro) and 2.83%, 1.03%, and 2.18% for the area under the receiver operating characteristic curve (AUC), respectively. Even under extreme (30%) packet loss conditions, the proposed model outperforms the same models by 8.23%, 2.68%, and 5.38% (F1macro) and 2.94%, 1.03%, and 2.29% (AUC), respectively. For 30% packet loss, the proposed model’s performance decreased by 0.108% (F1macro) and 0.102% (AUC); however, the LSTM, BiLSTM, and LSTM Attention model performances decreased by 0.468% and 0.209%, 0.554% and 0.103%, and 0.569% and 0.208% for F1macro and AUC, respectively. Thus, the proposed model is the least affected by packet loss.

Performance evaluation of mpeg-5 part 2 (lcevc): impact of packet loss

This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments

Optimized Visual Internet of Things in Video Processing for Video Streaming

The global expansion of the Visual Internet of Things (VIoT) has enabled various new applications during the last decade through the interconnection of a wide range of devices and sensors.Frame freezing and buffering are the major artefacts in broad area of multimedia networking applications occurring due to significant packet loss and network congestion. Numerous studies have been carried out in order to understand the impact of packet loss on QoE for a wide range of applications. This paper improves the video streaming quality by using the proposed framework Lossy Video Transmission (LVT) for simulating the effect of network congestion on the performance of encrypted static images sent over wireless sensor networks.The simulations are intended for analysing video quality and determining packet drop resilience during video conversations.The assessment of emerging trends in quality measurement, including picture preference, visual attention, and audio visual quality is checked. To appropriately quantify the video quality loss caused by the encoding system, various encoders compress video sequences at various data rates.Simulation results for different QoE metrics with respect to user developed videos have been demonstrated which outperforms the existing metrics.

Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices.

The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Performance Analysis of VCA-Based Target Detection System for Maritime Surveillance

In the latest years great importance and research efforts have been put into safety and security aspects concerning the vehicular framework. In this connection, maritime surveillance is playing an important role, since situation awareness proves fundamental to ensure safety conditions at sea. In this work, we propose a novel maritime surveillance system based on Video Content Analysis, where vessel detection is performed automatically by a remote Machine Learning based target identification algorithm. As performance study, we carry out experimental tests analyzing the impact of packet loss, compression rate and transport protocol type on ship-to-ground communication over a satellite link, and their joint effects on video quality, transmission time and vessel detection accuracy.

Impact of Packet Loss Rate on Quality of Compressed High Resolution Videos.

Video delivered over IP networks in real-time applications, which utilize RTP protocol over unreliable UDP such as videotelephony or live-streaming, is often prone to degradation caused by multiple sources. The most significant is the combined effect of video compression and its transmission over the communication channel. This paper analyzes the adverse impact of packet loss on video quality encoded with various combinations of compression parameters and resolutions. For the purposes of the research, a dataset containing 11,200 full HD and ultra HD video sequences encoded to H.264 and H.265 formats at five bit rates was compiled with a simulated packet loss rate (PLR) ranging from 0 to 1%. Objective assessment was conducted by using peak signal to noise ratio (PSNR) and Structural Similarity Index (SSIM) metrics, whereas the well-known absolute category rating (ACR) was used for subjective evaluation. Analysis of the results confirmed the presumption that video quality decreases along with the rise of packet loss rate, regardless of compression parameters. The experiments further led to a finding that the quality of sequences affected by PLR declines with increasing bit rate. Additionally, the paper includes recommendations of compression parameters for use under various network conditions.

Effects of Sound Quality on the Accuracy of Telephone Captions Produced by Automatic Speech Recognition: A Preliminary Investigation.

Automatic speech recognition (ASR) is commonly used to produce telephone captions to provide telecommunication access for individuals who are d/Deaf and hard of hearing (DHH). However, little is known about the effects of degraded telephone audio on the intelligibility of ASR captioning. This research note investigates the accuracy of telephone captions produced by ASR under degraded audio conditions. Packet loss, delay, and repetition are common sources of degradation in sound quality for telephone audio. Eleven sets of wideband filtered sentences were degraded by high and low levels of simulated packet loss, delay, and repetition. These sets, along with a clean set of sentences, were submitted to ASR, and the accuracy of the resulting output was evaluated using three metrics: a word recognition score, word error rate, and word information loss. The resulting pattern of data indicated the relative impact of each degraded condition on message intelligibility. The high and low packet loss conditions had the largest effect on message intelligibility. This finding was interpreted to indicate that packet loss can have a substantial impact on the accuracy of telephone captions produced with ASR. The results of this investigation point to a potential area of improvement in service quality that could have a substantial impact on telecommunication services for consumers who are DHH. Further research in this area is needed to provide additional information concerning the scope and impact of packet loss on the accuracy of telephone captioning produced by ASR. https://doi.org/10.23641/asha.21699557.

Compensation-Based Output Feedback Control for Fuzzy Markov Jump Systems With Random Packet Losses.

This article is concerned with the problem of compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. The phenomenon of packet losses is assumed to randomly occur in the feedback channel, which is modeled by a Bernoulli process. Employing the single exponential smoothing method as a compensation scheme, the missing measurements are predicted to help offset the impact of packet losses on system performance. Then, an asynchronous output feedback controller is designed by the hidden Markov model. Based on the mode-dependent Lyapunov function, some novel sufficient conditions on the controller existence are derived such that the closed-loop system is stochastically stable with strict dissipativity. Besides, an algorithm for determining the optimal smoothing parameter is proposed. Finally, the validity and advantages of the design approach are manifested by some simulation results.

Reducing tail latency with coding-based packet spraying in edge datacenters

Modern cloud computing applications have stringent low-latency and high-throughput requirements to meet the increasingly diverse demands from customers. In edge datacenters, the popular load balancing scheme based on packet spraying, i.e., random packet spraying (RPS), makes good use of multiple equal-cost paths to ensure high link utilization and efficient transmission. However, RPS performs poorly under the bursty traffic scenario due to packet loss or even timeout. Therefore, to reduce the tail latency caused by retransmission, we design a coding-based random packet spraying named CRPS. Specifically, the source host transmits forward error correction (FEC) encoded packets and dynamically adjusts the data redundancy based on the packet loss rate. Then the switch randomly sprays the encoded packets across all equal-cost multiple paths to implement parallel transmission. In this way, once enough encoded packets from any parallel paths arrive at the destination host, the original packets can be decoded immediately to address the adverse impact of packet loss. The NS-2 simulation results show that CRPS effectively reduces the probability of timeout and significantly improves the tail flow completion time (FCT) by up to 72% compared with the state-of-the-art multipath transmission schemes.

Fault tolerant compensation strategy for stochastic distribution systems with communication constraints

A new type of fault tolerant compensation strategy on the basis of sliding mode predictive control is presented for stochastic distribution systems (SDSs) in case of time-delay and packet dropout. The random packet loss process from the sensor to the controller is described as the independent Bernoulli distribution and a packet loss compensation strategy is introduced to compensate for the impact of packet loss on the system. To obtain information about the unknown fault, the adaptive fault diagnosis (FD) observer is devised on the basis of residual information. A fault tolerant control (FTC) strategy that combines the sliding mode control (SMC) and model predictive control (MPC) is put forward to make SDSs with fault still have good tracking performance. The proposed algorithm is applied to the fiber length distribution control in the papermaking process, and the simulation results prove the efficiency of the scheme.

Research on a Control Strategy for a Distributed Economic Dispatch System in an Active Distribution Network, Considering Communication Packet Loss

The benefits of a distributed economic dispatch system include significant resilience, uniform processing and communication pressure, and good scalability, but, currently, there is limited in-depth research on the impact of packet loss on the communication links of a distributed economic dispatch system. To analyze how communication packet loss affects a distributed economic dispatch system and to develop appropriate scheduling approaches, in this paper, we design a fully distributed economic dispatch strategy for an active distribution network (ADN) in leader-follower mode, we use the Bernoulli stochastic process to describe the communication packet loss phenomenon, and we give sufficient conditions for the system in the form of linear matrix inequality (LMI) to be able to achieve stochastic stability. The communication packet loss probability affects the convergence performance of the system, and a high packet loss probability can cause the system to lose stability. By simulating various situations in the IEEE-14 and IEEE-39 systems, in this study, we confirm the validity and efficacy of the distributed economic dispatch control strategy and the system stability analysis method.

Trading hardware with medium reservation to tackle scalability in low‐cost, single‐channel LoRa networks

LoRa is actually one of the most popular LPWAN technologies for IoT applications, due to its low‐power and long‐range transmissions. A single low‐cost, single‐channel LoRa Gateway is able to cover a large number of End‐Devices spread over a wide area. Gateway diversity is traditionally used to reduce the impact of packet losses: adding more Gateways can increase both delivery ratio and goodput, even when using a pure‐ALOHA access policy. However, such solution can be cost‐expensive and the adoption of control‐based medium access strategies, without violating the duty‐cycle constraints, can be, in some situations, a better option. In this letter, we compare the effectiveness of Gateway diversity against a medium access protocol with channel reservation. We evaluate if and in which scenarios, relatively to delivery ratio and goodput, in a single communication channel, it is better to add more Gateways to the system (hardware) or adopt a reservation protocol (software) for tackling the scaling‐up of the number of End‐Devices.

Loss-Aware Throughput Estimation Scheduler for Multi-Path TCP in Heterogeneous Wireless Networks

Multi-path TCP (MPTCP) is increasingly popular with the widespread usage of multihomed devices. MPTCP allows data streams to be delivered across multiple simultaneous connections, providing higher bandwidth aggregation and throughput in comparison with single-path TCP. However, due to the path heterogeneity and packet losses, the occurrence of Out-of-Order (OFO) packets is inevitable for MPTCP. Although many approaches have been proposed to mitigate OFO, most of them focused on compensating path delay differences but not considered the impact of packet loss. In this paper, we take the first step towards analyzing the impact of packet loss on OFO, and propose Loss-Aware Throughput Estimation scheduler, LATE. LATE comprehensively considers each subflow's path characteristics and protocol parameters including Round Trip Time (RTT), congestion window (cwnd), and loss rate, to predict the data amount that can be sent over each subflow at a given time and determine wisely which segments should be allocated to which subflows. Experimental results show that LATE achieves a gain of 5.13% in mean goodput with long-lasting flows while reducing the completion time of short flows by about 26.68% compared to the state-of-the-art scheduler for MPTCP.

HTTP Adaptive Streaming on Improved LISP-MN Architecture

The increasing consumption of video content using mobile devices calls for a more efficient techniques of handling the enormous data sent and received across wireless Internet. The Loc/ID split architecture is a group of proposals geared towards enhancing the mobility of nodes on the Internet. Although Loc/ID protocols guarantee handover, they suffer excessive packet loss and long service disruption time during the process. In this paper, we posit that redirecting incoming packets to a nearby buffer node, while a handover is taking place, and forwarding the packets to the mobile device after it reconnects will reduce the negative impact of packet loss on user experience. Improvement in the performance of video services is one manifestation that could be observed. In line with this, we propose an improved architecture for one of the most popular Loc/ID protocols, Locator-Identifier Separation Protocol Mobile Node (LISP-MN) by adding a new node, termed loc-server, to LISP-MN components. Loc-server caches and forwards packets to a mobile device during its handover process. To evaluate the performance of the improved architecture, we implemented the system on a laboratory tested and developed a number of MPEG-DASH compliant players. The performance of the players streaming over the proposed architecture is then compared against vanilla LISP-MN architecture. Results of the evaluation show improved performance when running the players on top of the new architecture.

Error sensitivity model based on spatial and temporal features

Packet loss and error propagation induced by it are significant causes of visual impairments in video applications. Most of the existing video quality assessment models are developed at frame or sequence level, which can not accurately describe the impact of packet loss on the local regions in one frame. In this paper, we propose an error sensitivity model to evaluate the impact of a single packet loss. We also make full use of the spatio-temporal correlation of the video and analyze a set of features that directly impact the perceptual quality of videos, based on the specific situation of video packet loss. With the aid of the support vector regression (SVR), these features are used to predict the error sensitivity of the local region. The proposed model is tested on six video sequences. Experimental results show that the proposed model predicts sensitivity of videos to different packet loss cases with certain reasonable accuracy, and provides good generalization ability, which turns out outperform the state-of-art image and video quality assessment methods.

Data Repair Without Prior Knowledge Using Deep Convolutional Neural Networks

In recent years, the use of wireless sensor networks has become increasingly widespread. Because of the instability of wireless networks, packet loss occasionally occurs. To reduce the impact of packet loss on data integrity, we take advantage of the deep neural network's excellent ability to understand natural data and propose a data repair method based on a deep convolutional neural network with an encoder-decoder architecture. Compared with common interpolation algorithms and compressed sensing algorithms, this method obtains better repair results, is suitable for a wider range of applications, and does not need prior knowledge. This method adopts measures such as preparing training set data as well as the design and optimization of loss functions to achieve faster convergence speed, higher repair accuracy, and better stability. To fairly compare the repair performance of different signals, the mean squared error, relative peak-to-peak average error, and relative peak-to-peak max error are adopted to quantitatively evaluate the repair results of different signals. Comparative experiments prove that this method has better data recovery performance than traditional interpolation and compressed sensing algorithms.

Network Performance Criteria for Telecommunication Traffic Types driven by Quality of Experience

A common reason for changing the chosen service provider is the users' perception of service. Quality of Experience (QoE) describes the end user's perception of service while using it. A frequent cause of QoE degradation is inadequate traffic routing, where, other than throughput, selected routes do not satisfy minimum network requirements for the given service or services. In order to enable QoE-driven routing, per traffic type defined routing criteria are required. Our goal was to obtain those criteria for relevant services of a telecom operator. For the purpose of identifying services of interest, we first provide short results of user traffic analysis within the telecom operator network. Next, our work presents testbed measurements which explore the impact of packet loss and delay on user QoE for video, voice, and management traffic. For video services, we investigated separately multicast delivery, unicast HTTP Live Streaming (HLS), and unicast Real Time Streaming Protocol (RTSP) traffic. Applying a threshold to QoE values, from the measured dependencies we extracted minimum network performance criteria for the investigated different types of traffic. Finally, we provide a comparison with results available in the literature on the topic.

Spatiotemporal Data Gathering Based on Compressive Sensing in WSNs

In wireless sensor networks (WSNs), sensor readings have spatiotemporal correlation, and wireless links are unreliable. To balance the energy-performance trade-off and reduce the impact of packet loss, a novel approach is proposed that combines Kronecker compressed sensing (KCS) and cluster topology to exploit spatial and temporal correlations simultaneously. The head nodes generate sparse sub-measurement matrices based on gathered data to avoid measuring nodes that cannot successfully transmit readings. The sink constructs those matrices as a block diagonal matrix (BDM) to utilize the spatial correlation among clusters. Numerical results show that this scheme effectively balances the energy-performance trade-off and maintains a high reconstruction accuracy with packet loss.