Effect Of Packet Loss Research Articles

Comparative study of novel packet loss analysis and recovery capability between hybrid TLI-µTESLA and other variant TESLA protocols

Analyzing packet loss, whether resulting from communication challenges or malicious attacks, is vital for broadcast authentication protocols. It ensures legitimate and continuous authentication across networks. While previous studies have mainly focused on countering Denial of Service (DoS) attacks' impact on packet loss, our research introduces an innovative investigation into packet loss and develops data recovery within variant TESLA protocols. We highlight the efficacy of our proposed hybrid TLI-µTESLA protocol in maintaining continuous and robust connections among network members, while maximizing data recovery in adverse communication conditions. The study examines the unique packet structures associated with each TESLA protocol variant, emphasizing the implications of losing each type on the network performance. We also introduce modifications to variant TESLA protocols to improve data recovery and alleviate the effects of packet loss. Using Java programming language, we conducted simulation analyses that illustrate the adaptability of variant TESLA protocols in recovering lost packet keys and authenticating previously buffered packets, all while maintaining continuous and robust authentication between network members. Our findings also underscore the superiority of the hybrid TLI-µTESLA protocol in terms of packet loss performance and data recovery, alongside its robust cybersecurity features, including confidentiality, integrity, availability, and accessibility. Additionally, we demonstrated the efficiency of our proposed protocol in terms of low computational and communication requirements compared to earlier TESLA protocol variants, as outlined in previous publications.

Effects of Two-Channel Noise and Packet Loss on Performance of Information Time Delay Systems.

The performance limitations of multiple-input multiple-output (MIMO) information time delay system (ITDS) with packet loss, codec and white Gaussian noise (WGN) are investigated in this article. By using the spectrum decomposition technique, inner-outer factorization, and partial factorization techniques, the expression of performance limitations is obtained under the two-degree-of-freedom (2DOF) compensator. The theoretical analysis results demonstrate that the system performance is related to the time delay, non-minimum phase (NMP) zeros, unstable zeros and their directions in a given device. In addition, WGN, packet loss and codec also impact the performance. Finally, the theoretical results are verified by simulation examples. Simulation results show that packet loss rate and encoding and decoding have a greater impact on system performance.

Adaptive event-triggered path following control for networked autonomous vehicles under aperiodic denial-of-service attacks

ABSTRACT This paper studies the adaptive event-triggered (AET)-based path following control problem for networked autonomous vehicles subject to aperiodic denial-of-service (DoS) attacks. Firstly, considering the strong dependence between scheduling parameters, a polytopic linear parameter varying system is established to represent the nonlinear path following system. Secondly, to save limited communication bandwidth and guarantee a desired path following control performance effectively, a novel AET mechanism with nonmonotonic characteristics is designed, and a compensation strategy is presented to tackle the effect of packet losses caused by DoS attacks on the system. Thirdly, sufficient conditions are developed for the output feedback controller gain matrix and the AET mechanism weighting matrix such that the closed-loop system is guaranteed to be stable and an performance is satisfied. Finally, simulation results verify the proposed control strategy.

Modeling Video Streaming Quality of Experience using Taguchi and Fuzzy Logic Methods

The popularity of mobile video streaming has increased significantly in recent years, and is expected to account for two-thirds of global internet traffic in the near future. However, determining accurately end-users' satisfaction based on network parameters remains a challenge. Existing research often uses network parameters, such as packet loss, delay, and jitter, to estimate users' Quality of Experience (QoE). However, most models present QoE estimates in Mean Opinion Scores (MoS), which are not easily understood by the customers. In this study, we used the Taguchi approach to conduct QoE experiments over a wireless tested. We investigated the simultaneous effects of packet loss, corruption, delay, and jitter on video streaming QoE, as well as their interaction effects. Furthermore, we developed a Fuzzy logic model in MatlabR2016a to establish the relationship between input variables and video streaming QoE. The model presents the results in an easily understandable linguistic terms such as excellent, good, average, bad, and poor. Additionally, the proposed model achieves a correlation of 0.875 between the predicted and user scores, with a Root Mean Square Error of 0.344.

Fuzzy H∞ robust control for T-S aero-engine systems with network-induced factors under round-robin-like protocol

Communication behavior characterization, signal scheduling and robust control are the main challenges for networked control applications in aero-engine. To address these difficulties, this paper proposes a synthesis and analysis scheme for Takagi–Sugeno (T-S) aero-engine systems subject to scheduling strategies and network-induced factors, in which the multi-node round-robin protocol (MN-RRP) is introduced to coordinate communication resources. Considering the effects of time delay and packet loss, the whole signal transmission model is established to describe the nonlinear characteristics of aero-engine networked systems. Then, a novel H∞ fuzzy proportional-integral-derivative-like (PID-like) controller is developed to improve the response performance without sacrificing robustness. Sequentially, the stability conditions are derived in terms of the token-dependent Lyapunov function. Moreover, an optimization algorithm is designed to intelligently solve the controller parameters, which allows the user to flexibly adjust performance requirements. The numerical simulations show that the designed synthesis scheme has good robustness to flight envelope changes and incomplete signals, and ensures that the controlled aero-engine system achieves rapid response, disturbance suppression and considerable tracking performance simultaneously.

Distributed Nonlinear Trajectory Optimization for Multi-Robot Motion Planning

This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control (NMPC) formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an onboard computation unit to solve a local control problem and communicate with neighboring autonomous robots via a wireless network. The difference between the two proposed methods is in the way the robots exchange information to coordinate. The information exchange can occur in a following: 1) synchronous or 2) asynchronous fashion. By relying on the theory of the nonconvex alternating direction method of multipliers (ADMM), we show that the proposed solutions converge to a (local) solution of the centralized problem. For both algorithms, the communication exchange preserves the safety of the robots; that is, collisions with neighboring autonomous robots are prevented. The proposed approaches can be applied to various multi-robot scenarios and robot models. In this work, we assess our methods, both in simulation and with experiments, for the coordination of a team of autonomous vehicles in the following: 1) an unsupervised intersection crossing and 2) the platooning scenarios.

Smart algorithm in wireless networks for video streaming based on adaptive quantization

SummaryNowadays, smart multimedia network services have become crucial in the healthcare system. The network parameters of Quality of Service (QoS) are widely affecting the efficiency and accuracy of multimedia streaming in wireless environments. This paper proposes an adaptation framework model, which makes a relation between the QP (quantization parameter) in H.264 and H.265 codecs and the QoS of 5G wireless technology. Besides, the effect of QP and packet loss have been studied because of their impact on video streaming. Packet loss of 5G wireless network characteristic is emulated to determine the impact of QP on the received video quality using objective and subjective quality metrics such as PSNR (peak signal to noise ratio), SSIM (structure similarity), and DMOS (differential mean opinion score). In this research, a Testbed is implemented to stream the encoded video from the server to the end users. The application model framework has automatically evaluated the QoE (Quality of Experience). Accordingly, the model detects the defect of network packet loss and selects the optimum QP value to enhance the QoE by the end‐users. The application has been tested on low and high video motions with full high definition (HD) resolution (1920 × 1080) which were taken from ( https://www.xiph.org/downloads/). Test results based on the objective and subjective quality measurements indicate that an optimal QP = 35 and QP = 30 have been chosen for low and high motion respectively to satisfy user QoE requirements.

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.

Detecting deception attacks in cyber‐physical linear parameter varying systems with packet loss

SummaryThe increased connectivity of cyber‐physical systems (CPS) to enterprise networks raises challenging security concerns. Detecting attacks on CPS is a vital step to improving their security. Most of the existing attack detectors are for CPS with linear dynamics. In this study, an investigation is made for designing a detector of deception attacks in a cyber‐physical linear parameter varying (LPV) system in the presence of packet loss. A model‐based attack detector is designed to detect deception attacks on output measurements, actuators, or schedule variables. With an unreliable network, the LPV attack detector enhances detection capacity and attenuates disturbances on the detector module to improve detection accuracy. During communication, packet loss results in network unreliability, which is modeled by the Bernoulli process. Stochastic stability is used to determine LPV attack detector parameters. A residual signal is built by comparing the detector's output and the actual sensor measurement and provides the deception attack's essential data. However, the packet loss causes some impulse signals in the residual signal, resulting in false alarms. Therefore, the detector module is equipped with a median filter to suppress packet loss's effect on evaluating the residual signal. Tests and validations of the proposed approach are performed on a two‐tank system and a continuous stirred tank reactor. According to evaluation results conducted on two testbeds, the proposed method accurately detects deception attacks even when there is packet loss.

Packet loss concealment method based on interpolation in packet voice coding

This paper proposes a packet loss concealment (PLC) method based on redundant information to enhance speech quality under severe network conditions as well as a new model to simulate packet loss effect, end-to-end transmission delay, and jitter on the concealment process. Our proposed method increases the efficiency of the standard ITU-T G.722.2 by improving the quality of the decoded speech under random frame loss conditions over packet-switched networks. The new method achieves a MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) value exceeding 48.7 at a 20% packet loss ratio (PLR). Numerical analysis indicates that our method outperforms existing alternatives and can be successfully used, even in severe propagation scenarios at the expense of an increase in processing requirements.

Dynamic signal recovery in distribution grids using compressive lossy measurements

Distribution system state estimation requires reliable aggregation of the measured data. However, the large volume of the measured data imposes a significant stress on the underlying communication infrastructure. With the challenges associated with measurement availability, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. In this paper, we evaluate the robustness of an updated Kalman filtered modified compressive sensing (KF-ModCS) technique that dynamically estimates the grid states using a small fraction of measured data. In practice, measurements used for sparsity based state estimation may also be intermittent due to communication network induced losses. To understand the effect of packet losses on KF-ModCS, we provide an upper bound for the expected variances of the state estimation error for a given rate of information loss. This upper bound is further improved if the support set of the sparse signal that characterizes the state dynamics does not change over time and/or the reduced model is observable. Simulations based on two practical data sets collected from actual customers in a distribution grid validate the theoretical results.

Hybrid error correction in fragmented IoT media streams

AbstractWireless personal area networks (WPANs), as most IoT networking technologies, rely on constrained low power devices that support a trade‐off between signal coverage and transmission rates. WPANs are characterized for medium to low transmission rates over low distances. The transmission rates, however, are high enough to support maximum transmission unit (MTU) sizes that are large enough to enable the transmission of IPv6 datagrams. One such WPAN technologies is associated with the IEEE 802.15.4 physical layer. Because IEEE 802.15.4 does not natively support IP encapsulation, an adaptation mechanism known as IPv6 over low‐power wireless personal area networks (6LoWPAN) is used to this end. For large datagrams like those generated when transmitting speech frames, 6LoWPAN fragments them in order to comply with the 127‐byte IEEE 802.15.4 MTU size. Fragmentation, however, aggravates the effects of network packet loss. This article explores this problem and attempts to find a mechanism that overcomes these limitations. Specifically, it introduces a hybrid error correction algorithm that dynamically adjusts the number of fragments that are transmitted in order to balance application layer loss and transmission rate.

Formation Control of Automated Guided Vehicles in the Presence of Packet Loss.

This paper presents the formation tracking problem for non-holonomic automated guided vehicles. Specifically, we focus on a decentralized leader–follower approach using linear quadratic regulator control. We study the impact of communication packet loss—containing the position of the leader—on the performance of the presented formation control scheme. The simulation results indicate that packet loss degrades the formation control performance. In order to improve the control performance under packet loss, we propose the use of a long short-term memory neural network to predict the position of the leader by the followers in the event of packet loss. The proposed scheme is compared with two other prediction methods, namely, memory consensus protocol and gated recurrent unit. The simulation results demonstrate the efficiency of the long short-term memory in packet loss compensation in comparison with memory consensus protocol and gated recurrent unit.

Bandwidth estimation in high mobility scenarios of MANET using NSGA-II optimized fuzzy inference system

Integration of mobility to sensor node enhanced the scalability of Ad-hoc networks. Bandwidth estimation in scenarios where nodes move randomly and frequently changes its link may depend on factors other than congestion in network traffic. The event of link failure due to the high mobility behavior of nodes in the path from source to destination is one of the dominating cause. However, in both cases, TCP’s new-Reno and its variants reduce the congestion-window size to half or 1-MSS, which can cause bandwidth underutilization in the event of a packet drop due to link failure. The proposed approach distinguishes the link failure from network congestion and estimates bandwidth based on link and path stability matrix. The contribution includes the identification of node mobility fuzziness in unicast and broadcast scenarios of IEEE 802.15.4 based MANET. The mobility-fuzziness formulation by the proposed NSGA-II optimized fuzzy-inference-system imitates the node’s mobility behavior and, in the event of packet loss, is employed to realize the path-stability metric to estimate the congestion window size. The thorough evaluation with current state-of-the-art techniques shows that the proposed path stability metric allows the estimation of congestion window size to the current congestion window in the event of a packet loss due to link failure. Improving the bandwidth utilization metric in high mobility scenarios from 53–61% for existing approaches to above 83% in proposed approach.

TCP-WBQ: a backlog-queue-based congestion control mechanism for heterogeneous wireless networks

In heterogeneous wireless networks, random packet loss and high latency lead to conventional TCP variants performing unsatisfactorily in the case of competing communications. Especially on high-latency wireless links, conventional TCP variants are unable to estimate congestion degrees accurately for fine-grained congestion control because of the effects of random packet loss and delay oscillations. This paper proposes a TCP variant at the sender side to identify congestion degrees, namely TCP-WBQ, which quickly responses to the real congestion and effectively shields against random packet loss and oscillations of latency time. The proposed algorithm of congestion control firstly constructs a backlog-queue model based on the dynamics of the congestion window, and deduces the two bounds of the model which delimit oscillations of the backlog queue for non-congestion and random packet loss respectively. TCP-WBQ detects congestion degrees more accurately and thus implements the corresponding schemes of adjusting the congestion window, maintaining a tradeoff between high throughputs and congestion avoidance. The comprehensive simulations show that TCP-WBQ works efficiently in bandwidth utilization with single and multiple bottleneck scenarios, and achieves high performance and competitive fairness in heterogeneous wireless networks.

Understanding The Effect of Physical Parameters on Packet Loss in Veins VANET Simulator

Setting up the physical parameters of a simulator is key to achieving realistic results. Setting those parameters requires an understanding of how they affect the simulation results. Packet loss is a key performance metric in communication and networks fields. In this paper, we present an overview of how Veins VANET simulator simulates WiFi packet loss. This work explains the physical parameters set by the user in Veins and how Veins then uses these parameters to simulate the effects of attenuation and packet loss.

Gradient Descent for Resource Allocation with Packet Loss*

This paper studies the effect of packet loss during the application of the weighted gradient descent to solve a resource allocation problem with piecewise quadratic cost functions in a multi-agent system. We define two performance metrics that measure, respectively, the deviation from the constraint and the error on the expected cost function. We derive upper bounds on both metrics: both bounds are proportional to the difference between the initial cost function and the cost function evaluated at the minimizer. Then, we extend the analysis of the constraint violation to open multi-agent systems where agents are replaced: based on a preliminary result and simulations we show that the combination of replacements and losses makes the constraint violation error diverge with time.

A Packet Loss Monitoring System for In-Band Network Telemetry: Detection, Localization, Diagnosis and Recovery

Network measurement provides rich data for network monitoring, control, and management. In-band network telemetry (INT) is a new network measurement technology that uses normal data packet to collect network information hop-by-hop. However, the design and implementation of INT protocol cannot do anything about packet loss: (1) The end-to-end telemetry mechanism makes INT unable to detect packet loss; (2) Since data packets may be lost due to various reasons, INT telemetry information will inevitably be lost. In summary, INT system by itself is unreliable. Incomplete telemetry data will seriously affect the performance of upper-layer network telemetry applications. In this paper, we present our successful experience in INT packet loss monitoring. We design, implement, and open source a powerful packet loss monitoring system for INT, called LossSight. The functions of LossSight include the detection of packet loss events, the deduction of the time and location of the losses, the diagnose of the root cause of the losses, and the recovery of the lost INT information. Experiment results show that LossSight provides excellent performance and extremely low overhead, including detection accuracy and diagnostic precision close to 100%, and detection latency of just milliseconds. In particular, LossSight uses a generative adversarial network to recover lost telemetry information, with excellent accuracy and reliability. LossSight has been running stably in the supercomputing interconnection environment of the National Supercomputing Center in Jinan. We suggest that all INT applications that require reliable telemetry information should be implemented based on LossSight.

Detecting cyber attacks with packet loss resilience for power systems

Phasor measurement units (PMUs) are critical for modern power systems to monitor the operation status in near real-time. Spoof attacks present increasing challenges to the trustworthy of the PMU data. Machine learning models can be used to detect spoofed signals in a timely manner. However, the effectiveness of these methods can be degraded by the unpredictable packet loss during the transmission of the PMU data. The negative impact of the packet loss on machine learning based spoof detection models have not been well studied. In this paper, the exact granular effects of packet loss on spoof detection models is quantified and examined. Several mitigation strategies are considered and recommended when dealing with packet loss in the data stream. Finally, a Dynamic Window Size Algorithm (DWSA) is developed to minimize the amount of packet loss the models are passed for each data point. The positive and negative effects of using DWSA to mitigate the effects of packet loss are also explored.

Packet Loss Measurement Based on Sampled Flow

This paper is devoted to further strengthening, in the current asymmetric information environment, the informed level of operators about network performance. Specifically, in view of the burst and perishability of a packet loss event, to better meet the real-time requirements of current high-speed backbone performance monitoring, a model for Packet Loss Measurement at the access network boundary Based on Sampled Flow (PLMBSF) is presented in this paper under the premise of both cost and real-time. The model overcomes problems such as the inability of previous estimation to distinguish between packet losses before and after the monitoring point, deployment difficulties and cooperative operation consistency. Drawing support from the Mathis equation and regression analysis, the measurement for packet losses before and after the monitoring point can be realized when using only the sampled flows generated by the access network boundary equipment. The comparison results with the trace-based passive packet loss measurement show that although the proposed model is easily affected by factors such as flow length, loss rate, sampling rate, the overall accuracy is still within the acceptable range. In addition, the proposed model PLMBSF, compared with the trace-based loss measurement is only different in the input data granularity. Therefore, PLMBSF and its advantages are also applicable to aggregated traffic.