MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

Due to the lossy nature of wireless links, it is difficult to determine if packet losses are due to wireless-induced effects or from malicious discarding. Many prior efforts on detecting malicious packet drops rely on evidence collected via passive monitoring by neighbor nodes; however, they do not analyze the cause of packet losses. In this paper, we ask: (a) Given certain macroscopic parameters of the network (like traffic intensity and node density) what is the likelihood that evidence exists with respect to a transmission? and, (b) How can these parameters be used to perform a forensic analysis of the reason for the losses? Towards answering the above questions, we first build an analytical framework that computes the likelihood that evidence (we call this transmission evidence or TE for short) exists with respect to transmissions, in terms of a set of network parameters. We validate our analytical framework via both simulations as well as real-world experiments on two different wireless testbeds. The analytical framework is then used as a basis for a protocol within a forensic analyzer to assess the cause of packet losses and determine the likelihood of forwarding misbehaviors. Through simulations, we find that our assessments are close to the ground truth in all examined cases, with an average deviation of 2.3% from the ground truth and a worst case deviation of 15.0%.

Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.

The Internet of things, including Internet technology, including wired and wireless networks. Internet of Things and the Internet is the relationship between the parent and the child. In this paper, we aim to study the Investigation on the network packet loss’s long-range dependence and QOE and gain a good result and conclusion. In order to better establish no-reference video quality assessment model considering the network packet loss and further gain a better QoE evaluation, so we build NS2 + MyEvalvid simulation platform to study the scale characteristic of the network packet loss, scale characteristic of packet loss through the influence of packet loss rate to influence QoE. The experimental results show that, packet loss processes have long-range dependence, the number of superimposed source N, shape parameter, Hurst parameter, the output link speed have impacts on long-range dependence. We came to the conclusion that when superimposed source N is more, the shape parameter is smaller, Hurst parameter is bigger, the output link speed is smaller, packet loss’s long range dependence is larger, packet loss rate is high.

Wireless networks are prone to packet loss making it strenuous to differentiate if data leakage is due to the physical nature of wireless networks or from malicious packet loss.All previous experiments were made on utilizing nodes which are part of network to monitor packet loss , a method deployed in passive detection.Due to high levels of interference the likelihood of classifying malicious packet loss from wireless induced packet loss is less probable.In this paper using certain transmission parameters like traffic intensity ,node density and transmission evidence we perform forensic analysis. By using an analytical framework we compute the transmission evidence.We validate our analytical framework via both simulation and wireless test-beds.The analytical framework is then used as a basis for a protocol within a forensic analyser to assess the cause of packet loss and determining the likelihood of forwarding misbehaviours.

Online video transmissions over wireless networks are rising in popularity and have already become part of our daily life. In the meantime, it is necessary to address a number of challenges ranging from the scarce resources, time-varying, and high error rates, to the fluctuating bandwidth, unveiling the need for an adaptive mechanism to ensure a good video transmission. Adaptive Forward Error Correction (FEC) techniques with Quality of Experience (QoE) assurance are appropriate to deliver QoE-aware video data to wireless users in dynamic and high error rates networks. This paper proposes an adaptive Video-aware FEC and Fuzzy Logic-based mechanism to shield realtime video transmissions against packet loss in wireless networks, improving both user experience and the usage of resources. The benefits and drawbacks of the proposed mechanism compared with exiting work are demonstrated through simulations and assessed with QoE metrics.

In the last two decades, Multi-Cast schemes in mobile ad-hoc networks have been widely studied and lots of research results have been put into use and changed our everyday life. However, packet loss for Multi-Cast schemes in mobile ad-hoc networks has not been well studied yet. The influence of node density and relay schemes on packet loss is not clear. Thus, we study the packet loss problem for Multi-Cast schemes in MANETs in this paper. First, we present a general Multi-Cast probabilistic model to get a better understanding of the packet loss problem. Second, based on the general model and Chernoff Bounds, we prove that the up'er bound of the general packet loss for Multi-Cast is Ω(me <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">- 2</sup> ), where n is the node number and m denotes number of hops. Thus we can conclude that large node density decreases the packet loss rate by exponential factor, and bigger hops increase packet loss rate linearly. In addition, we present a reliable Multi-Cast protocol(RMP). Through an ACK Aware Tree to complete packet acknowledgement and repair host selection, RMP is able to minimize the impact of packet loss on the Multi-Cast throughput in the simulation.

This paper proposes a learning-based loss differentiation method (LLD) for wireless congestion control. LLD uses a neural network to distinguish between wireless packet loss and congestion packet loss in wireless networks. It can work well in combination with classical packet loss-based congestion control algorithms, such as Reno and Cubic. Preliminary results show that our method can effectively differentiate losses and thus improve throughput in wireless scenarios while maintaining the characteristics of the original algorithms.

One of the challenging tasks of current generation wireless networks consist of supporting service continuity when users roam around different networks such as Wi-Fi, Bluetooth, and Cellular networks. This happens especially when dealing with applications such as multimedia streaming that distribute time continuous data with stringent Quality of Service (QoS) requirements like bandwidth, jitter, delay and packet loss. Session handover can solve this problem by ensuring service continuity through minimization of flow interruptions when users are mobile. Building upon a new Bayesian Belief framework, this paper proposes a context-aware handoff decision process that uses a multi-criteria decision making model to support QoS-based mobility in wireless networks. Using the widely deployed Hugin Expert Bayesian belief support tool, we illustrate its deployment in a network environment where mobility decisions are taken to roam around Wi-Fi or Bluetooth networks.

Collision and fading are the two main sources of packet loss in wireless local area networks (WLANs) and as such, both are affected by the packetization at the medium access control (MAC) layer.While a larger packet is preferred to balance protocol header overhead, a shorter packet is less vulnerable to packet loss due to channel fading errors or staggered collisions in the presence of hidden terminals. Direct collisions due to backoff are not affected by packet size. Recently, Krishnan et. al. have developed a new technique for estimating probabilities of various components of packet loss, namely, direct and staggered collisions and fading. Motivated by this work, in this paper, we exploit ways in which packetization can be used to improve throughput performance of WLANs. We first show analytically that the effective throughput is a unimodal function of the packet size when considering both channel fading and staggered collisions. We then develop a measurement-based algorithm based on golden section search to arrive at an optimal packet size for MAC-layer transmissions. Our simulations demonstrate that packetization based on our search algorithm can greatly improve the effective throughput of sensing-limited nodes, and reduce video frame transfer delay in WLANs.

&lt;p&gt;Video Streaming is a method to deliver multimedia content that allow files to be used or watched directly without the need to download and save it. Video streaming also include image processing, in image processing there is a component to compare the value of an image, that is PSNR. This research will compare the reactive and proactive routing protocol performance of AOMDV and DSDV using PSNR, Packetloss, and Throughput parameters in Wireless Mesh Network (WMN) for video streaming. The simulation uses 4, 16, 36, 64, and 100 nodes. The results of this research is AOMDV routing protocol with the greater variation in the number of nodes and the distance between nodes resulting in higher packet loss, decreasing throughput and decreasing values of PSNR. While in DSDV routing protocol with the greater variation in the number of nodes and distance between nodes has an impact on the increase of packetloss value, a very drastic decrease throughput value compared with the AOMDV routing protocol, a decrease in PSNR value and in some scenarios does not produce PSNR values. The comparison of PSNR values between the two routing protocols are also influenced by variations in the number of nodes and distances between nodes along with packetloss and throughput parameters. The greater packetloss will decrease the PSNR value generated.&lt;/p&gt;

This paper addresses the problem of non-optimal performance of Transmission Control Protocol (TCP) over Wi-Fi networks. TCP considers packet loss as a signal for congestion leading to reduced sending rate. This idea works well for the wired networks where majority of the packet losses are due to congestion. However, in wireless networks, packets are mostly lost because of transmission errors and mobility of the end hosts. Also, when packets are lost due to disassociation of the receiver from the network during mobility, sender remains idle for a significant period even after the receiver is re-associated. In this paper, we present the design and implementation of Software Defined Network (SDN) assisted TCP for wireless networks. In our approach, mobility induced packet losses are tackled smartly to avoid reduction in sending rate. Further, the data transfer starts immediately as soon as the receiver is re-associated to the network during mobility. Unlike existing solutions, our SDN assisted TCP does not violate the end-to-end semantics and no modification is required in the TCP protocol running at the end hosts. We have implemented and evaluated our approach using Mininet Wi-fi emulation platform. The results establish the applicability of our approach.

The packet loss classification has always been a hot and difficult issue in TCP congestion control research. Compared with the terrestrial network, the probability of packet loss in LEO satellite network increases dramatically. What's more, the problem of concept drifting is also more serious, which greatly affects the accuracy of the loss classification model. In this paper, we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks, named LDM-Satellite, which consists of three modules: concept drift detection, lost packet cache and hybrid integration classification. As far, this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks. We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier. And a new weight algorithm for these classifiers is given. In ns-2 simulation, LDM-Satellite has a better AUC (0.9885) than the single-model machine learning classification algorithms. The accuracy of loss classification even exceeds 98%, higher than traditional TCP protocols. Moreover, compared with the existing protocols used for satellite networks, LDM-Satellite not only improves the throughput rate but also has good fairness.

The automatic assessment of perceived quality of VoIP communications requires a subtle characterization of packet loss processes. This is more critical when delivery channels offer a time-varying performance, such as mobile and wireless “best effort” IP channels. Typically, the characterization of packet loss processes is done using theoretical models calibrated at runtime. At the end of a monitoring interval, a set of metrics is extracted and used by parameter-based quality models to estimate degradation caused losses. We design a new packet loss model that explicitly differentiates loss instances as a function of their perceptual effects. Moreover, our packet loss model considers the context where packet loss instances happen inside a given loss pattern. Furthermore, it enables simultaneously capturing and characterizing in a precise way long- and short-term behavior of packet loss processes. The characterization metrics extracted from the model can be used to build reliable parameter-based quality assessment models.

In wired networks, congestion is the main reason for most of the packet losses and the original TCP is the best option to implement. However, in wireless networks, these losses will arise for reasons other than congestion. TCPW has been proved to work better in performance and bandwidth utilization than Reno and New Reno in wireless networks. Our research mainly focuses on the slow-start phase of TCPW and develops an algorithm to divide the phase into two parts. In this paper, we propose a simple refinement of slow-start algorithm of TCP Westwood (TCPW), which is a TCP variant focused on to achieve higher performance in wireless and wired networks. We show by results that the new theme could reduce possibility of packet loss in wireless networks. In the first half of slow-start, we use the exponential increase just like the original slow-start. Moreover, we grow the congestion window gradually to decrease packet burstiness in the second half of slow-start.

Online monitoring of multimedia networks is required to ensure seamless and ubiquitous delivery of services to the end users. Quality of multimedia content, such as video streams, often gets degraded due to network losses such as packet loss. Frame freezing artifacts are introduced in a video stream when packet loss or packet delay takes place. Estimating the perceptual impact of these artifacts on quality of experience of end users helps service providers to maintain quality of service. In this paper, we have presented a novel no-reference video quality metric, which measures the impact of frame freezing due to packet loss and delay in video streaming networks. The proposed metric is based on several features that directly impact the quality of experience of end users. These features, including motion characteristics of videos, are calculated using the temporal information between video frames and then combined mathematically to form a video quality metric. Different weights are assigned to different features for better performance of the proposed metric. With detailed experiments, we have shown that our method outperforms other contemporary methods in terms of high accuracy and low computation time in frame freeze detection, low root mean square values, high coefficient of determination, and high correlation between subjective and objective measurements. We have used five video databases for our model's evaluation and validation. Furthermore, we have shown that our method is statistically superior to the other models in comparison.