Drop Probability Research Articles

DIRED: Dual Indicator Random Early Detection for Congestion Control in WSN

SummaryWireless Sensor Networks (WSNs) find applications in diverse fields such as environmental monitoring, healthcare, and military surveillance. Nonetheless, one of the primary challenges encountered by WSNs is congestion. Congestion arises in WSNs when there is a high volume of traffic on the network, leading to significant repercussions. These repercussions encompass packet loss, heightened latency durations, and diminished network efficiency. This paper presents an innovative congestion control mechanism named Dual Indicator Random Early Detection (DIRED). DIRED leverages two indicators, namely queue length and packet loss rate, to dynamically adjust the dropping probability of packets, thereby mitigating congestion and enhancing network performance. For the successful execution of the DIRED model, a congestion control algorithm is introduced. This strategy efficiently prevents the deterioration of network performance that can commonly arise from extremely low packet drop probabilities. Simulations are conducted to evaluate the proposed DIRED model, comparing it with the KACO and KFOA techniques. The results demonstrate that DIRED outperforms KACO and KFOA in terms of network performance, achieving a more optimal balance between performance metrics and packet‐dropping probability.

Identifying steady and pulsatile two-phase flow regimes with pressure drop signals and acoustic emissions

Identifying two-phase flow regimes is vital for understanding phase-change heat and mass transfer processes. Traditionally, flow regime identification has relied on subjective flow visualization studies, which are then converted to flow regime maps applicable to specific operating conditions. However, these conventional, largely subjective, maps fall short in predicting abrupt changes in flow patterns that often occur during regular operation of vapor compression and absorption heat pumps and other thermal systems. Conventional flow regime maps are also inadequate for addressing the transient, intermittent, or periodic flow regimes that occur in boiling and condensation enhanced using acoustic and other emerging techniques. Therefore, there is a pressing need for alternative flow regime identification techniques that can adapt and reliably track rapid and local changes in two-phase hydrodynamics. Promising candidates for dynamic flow pattern classification include high-resolution pressure drop signals and acoustic emission spectra, which can provide insights into the local hydrodynamics within a flow channel. To assess the feasibility of these measurement techniques, differential pressure and condenser microphone measurements are recorded alongside high-speed videos of a two-phase flow of saturated R134a. The total mass flux and vapor quality are varied to understand the effects of phase velocity and liquid inventory on wave propagation in the channel. Additionally, forced oscillations are introduced to a steady two-phase flow to analyze their impact on flow patterns and their corresponding pressure drop and acoustic signals. Statistical analyses, including Gaussian mixture modeling, are employed to reveal characteristic pressure drop probability associated with each flow pattern, which form the basis for developing a model capable of predicting flow regimes in both steady and oscillating flows. The resulting framework introduces a new flow regime identification technique that can adapt to dynamic operating conditions, benefiting a wide range of thermal systems and phase-change processes.

DESiRED — Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry

Active Queue Management (AQM) is a mechanism employed to alleviate transient congestion in network device buffers, such as routers and switches. Traditional AQM algorithms use fixed thresholds, like target delay or queue occupancy, to compute random packet drop probabilities. A very small target delay can increase packet losses and reduce link utilization, while a large target delay may increase queueing delays while lowering drop probability. Due to dynamic network traffic characteristics, where traffic fluctuations can lead to significant queue variations, maintaining a fixed threshold AQM may not suit all applications. Consequently, we explore the question: What is the ideal threshold (target delay) for AQMs? In this work, we introduce DESiRED (Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise network feedback from In-band Network Telemetry (INT) to feed a Deep Reinforcement Learning (DRL) model. This model dynamically adjusts the target delay based on rewards that maximize application Quality of Service (QoS). We evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH service. Our findings demonstrate up to a 90x reduction in video stall and a 42x increase in high-resolution video playback quality when the target delay is adjusted dynamically by DESiRED.

Drop and hump behaviors in robotic arc-directed energy deposition with vertical position

Arc-directed energy deposition (Arc-DED) with the vertical position offers significant potential for in-situ fabrication of overhang structures. However, the vertical position application is seriously hindered by drop and hump defects since Arc-DED possesses serious heat accumulation resulting from multi-layer depositions. The novelty of this study is to reveal the formation mechanisms of drop and hump defects in vertical Arc-DED of low-carbon steel walls. Force models of the molten pool in vertical-down and vertical-up Arc-DED are established. Two indicators, i.e., the molten pool area and the molten pool dimensional ratio, are proposed to evaluate the probability of drop and hump occurrence, respectively. Furthermore, how the wire feed speed (WFS), travel speed (TS), and the constant ratio of WFS to TS affect the molten pool behavior is explored and discussed. In the vertical-down deposition, decreasing the TS or increasing the WFS will increase the possibility of drop defects occurring when the molten pool area exceeds 45 mm². In the vertical-up deposition, increasing the TS or the WFS will increase the possibility of hump defects occurring when the molten pool dimensional ratio exceeds 4.1. Maintaining the WFS/TS constant, the probabilities of hump and drop defects increase as the WFS and the corresponding TS increase. This study offers a better understanding of the formation mechanisms underlying the drop and hump phenomena and lays a solid foundation for the practical application of vertical Arc-DED to fabricate overhang features.

A Unified Prediction Framework for Signal Maps: Not All Measurements are Created Equal

Signal maps are essential for the planning and operation of cellular networks. However, the measurements needed to create such maps are expensive, often biased, not always reflecting the performance metrics of interest, and posing privacy risks. In this paper, we develop a unified framework for predicting cellular performance maps from limited available measurements. Our framework builds on a state-of-the-art random-forest predictor, or any other base predictor. We propose and combine three mechanisms that deal with the fact that not all measurements are equally important for a particular prediction task. First, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quality-of-service functions (<inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>)</i> , including signal strength (RSRP) but also other metrics of interest to operators, such as number of bars, coverage (improving recall by 76%-92%) and call drop probability (reducing error by as much as 32%). By implicitly altering the loss function employed in learning, quality functions can also improve prediction for RSRP itself where it matters ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> MSE reduction up to 27% in the low signal strength regime, where high accuracy is critical). Second, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weight functions</i> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$W$</tex-math></inline-formula> ) to specify the relative importance of prediction at different locations and other parts of the feature space. We propose re-weighting based on importance sampling to obtain unbiased estimators when the sampling and target distributions are different. This yields improvements up to 20% for targets based on spatially uniform loss or losses based on user population density. Third, we apply the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data Shapley</i> framework for the first time in this context: to assign values ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> ) to individual measurement points, which capture the importance of their contribution to the prediction task. This can improve prediction ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> from 64% to 94% in recall for coverage loss) by removing points with negative values and storing only the remaining data points ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> as low as 30%), which also has the side-benefit of helping privacy. We evaluate our methods and demonstrate significant improvement in prediction performance, using several real-world datasets.

Statins and cognitive decline in patients with Alzheimer’s and mixed dementia: a longitudinal registry-based cohort study

BackgroundDisturbances in brain cholesterol homeostasis may be involved in the pathogenesis of Alzheimer’s disease (AD). Lipid-lowering medications could interfere with neurodegenerative processes in AD through cholesterol metabolism or other mechanisms.ObjectiveTo explore the association between the use of lipid-lowering medications and cognitive decline over time in a cohort of patients with AD or mixed dementia with indication for lipid-lowering treatment.MethodsA longitudinal cohort study using the Swedish Registry for Cognitive/Dementia Disorders, linked with other Swedish national registries. Cognitive trajectories evaluated with mini-mental state examination (MMSE) were compared between statin users and non-users, individual statin users, groups of statins and non-statin lipid-lowering medications using mixed-effect regression models with inverse probability of drop out weighting. A dose-response analysis included statin users compared to non-users.ResultsOur cohort consisted of 15,586 patients with mean age of 79.5 years at diagnosis and a majority of women (59.2 %). A dose-response effect was demonstrated: taking one defined daily dose of statins on average was associated with 0.63 more MMSE points after 3 years compared to no use of statins (95% CI: 0.33;0.94). Simvastatin users showed 1.01 more MMSE points (95% CI: 0.06;1.97) after 3 years compared to atorvastatin users. Younger (< 79.5 years at index date) simvastatin users had 0.80 more MMSE points compared to younger atorvastatin users (95% CI: 0.05;1.55) after 3 years. Simvastatin users had 1.03 more MMSE points (95% CI: 0.26;1.80) compared to rosuvastatin users after 3 years. No differences regarding statin lipophilicity were observed. The results of sensitivity analysis restricted to incident users were not consistent.ConclusionsSome patients with AD or mixed dementia with indication for lipid-lowering medication may benefit cognitively from statin treatment; however, further research is needed to clarify the findings of sensitivity analyses.

A predictive TDM-PON resource allocation using the ANN method based on equilibrium points of discrete dynamical systems

Resource allocation is one of the challenges in the time division multiple access (TDM) passive optical network (PON) area. Much research has been done to find an efficient resource allocation method. Using machine learning (ML) and finding proper ML algorithms in predictive dynamic bandwidth allocation remains an open research question. This paper aims to predict resource allocation accurately and efficiently based on historical requests. Artificial neural networks (ANN) with historically allocated bandwidth from different time series are used to build the prediction model. This model is constructed by the normal equation method. The proposed model needs a small dataset for its training phase, while its accuracy is higher than other models. The discrete dynamical system builds to find an equilibrium point, which is a stable point in the system. The proposed model can be implemented as a pre-training, or it can be dynamically refined when the model is running by using some of its output as feedback. The system evaluation verifies that the proposed DBA (EQ-ANN) elevates the quality of service (QoS) metrics, such as system throughput, packet drop probability, and packet means delay. The results show that the system throughput improves by up to 5%, and packet loss improves by up to 15%. Moreover, the prediction accuracy of the system reached 99.48%.

Simulating MIMT Attackers' Strategies in VANET to Secure ITS in Smart Cities via Multiverse Optimization-based Hybrid Routing Approach

The connection between moving vehicles and stationary Road Side Units is made possible by VANET technology, which is an essential component of Intelligent Transportation Systems. Vanet based intelligent transportation system (ITS) security is major issue in present days. MIMT Attackers' Strategies are taken into account to test the security of VANET based ITS system in present research paper. The major objective of research paper is to evaluate the potential of security model in case of different type of message dropping and delay attack. It is observed that there is probability of message delay, message drop, and message tampering attack in VANET based ITS system. Due to such attacks there is huge impact on content delivery ratio, packet delay and dropping. Thus proposed hybrid routing approach that is considering Multi-verse optimization has been used to simulate the Quantifying MIMT attacks. In present research, Vanet security in case of intelligent transportation system in smart cities has been considered.

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Active Queue Management (AQM) methods significantly impact the network performance, as they manage the router queue and facilitate the traffic flow through the network. This paper presents a novel fuzzy-based AQM method developed with a computationally efficient precise fuzzy modeling optimized using the Genetic Algorithm. The proposed method focuses on the concept of symmetry as a means to achieve a more balanced and equitable distribution of the resources and avoid bandwidth wasting resulting from unnecessary packet dropping. The proposed method calculates the dropping probability of each packet using a precise fuzzy model that was created and tuned in advance and based on the previous dropping probability value and the queue length. The tuning process is implemented as an optimization problem formulated for the b0, b1, and b2 variables of the precise rules with an objective function that maximizes the performance results in terms of loss, dropping, and delay. To prove the efficiency of the developed method, the simulation was not limited to the common Bernoulli process simulation; instead, the Markov-modulated Bernoulli process was used to mimic the burstiness nature of the traffic. The simulation is conducted on a machine operated with 64-bit Windows 10 with an Intel Core i7 2.0 GHz processor and 16 GB of RAM. The simulation used Java programming language in Apache NetBeans Integrated Development Environment (IDE) 11.2. The results showed that the proposed method outperformed the existing methods in terms of computational complexity, packet loss, dropping, and delay. As such, in low congested networks, the proposed method maintained no packet loss and dropped 22% of the packets with an average delay of 7.57, compared to the best method, LRED, which dropped 21% of the packets with a delay of 10.74, and FCRED, which dropped 21% of the packets with a delay of 16.54. In highly congested networks, the proposed method also maintained no packet loss and dropped 48% of the packets, with an average delay of 16.23, compared to the best method LRED, which dropped 47% of the packets with a delay of 28.04, and FCRED, which dropped 46% of the packets with a delay of 40.23.

Real-time natural gas explosion modeling of offshore platforms by using deep learning probability approach

Natural gas explosion of offshore platform is prone to cause accidental disaster such as platform collapse and casualties etc. Real-time natural gas explosion consequence reconstruction is essential to support a quick accidental emergency response planning to prevent the accidental escalation to disaster. The widely-used CFD is computationally intensive and thereby has a significant delay. Machine/deep learning-based models offer a potential real-time alternative, which however are not able to quantify the uncertainty of spatial overpressure prediction. This study aims to propose a hybrid deep learning probability model to real-time predict spatial explosion overpressure of offshore platform by using sparsely-observed overpressures. In this hybrid model, Variational Bayesian inference is incorporated into deep learning backbone. Both natural gas explosion experimental and numerical modeling of offshore platform are conducted to construct the benchmark dataset. By using this benchmark dataset, sensitivity analysis of Monte Carlo sampling number N, drop probability p on model's performance is also conducted. The results demonstrated our model exhibits high accuracy with R2 = 0.955 and real-time capability with inference time of 2.9s. Compared to the state-of-the-art model, the additional uncertainty estimation improves the accuracy and robustness of spatial overpressure prediction, which contributes to the reliable explosion accidental emergency decision-making. Overall, this study provides a reliable alternative for constructing digital twin emergency management system to effectively manage natural gas explosion risk of offshore platforms.

An Improved Handoff Algorithm for Seamless Connectivity in Heterogeneous Networks

Twenty-first century is an era of cutting-edge technologies. From artificial intelligence to nanotechnology, modern life has gathered the pace to match up with the shrinking time and fast-moving world. Likewise, a Heterogeneous Network finds its own space in this century of inventions. Every individual, industry, and institute needs to be connected to the internet cloud. Mobile applications have changed the dynamics of every individual’s life. From personal management to professional proficiency, mobile applications play an important role in almost every aspect. Numerous applications available these days demand continuous bandwidth connection. To remain always connected, there is a need for an effective handoff procedure. In this paper, we have proposed a convex optimization technique for optimizing the call drop probability, remaining energy and handoffs in heterogeneous networks. The proposed algorithm outperformed the already existing M-OPTG and M-WO algorithms.

Five-Year Visual Field Outcomes of the HORIZON Trial

To compare visual field (VF) progression between glaucoma patients receiving cataract surgery alone (CS) or with a Hydrus microstent (CS-HMS). Post hoc analysis of VF data from the HORIZON multicenter randomized controlled trial. A total of 556 patients with glaucoma and cataract were randomized 2:1 to either CS-HMS (369) or CS (187) and followed up for 5 years. VF was performed at 6 months and then every year after surgery. We analyzed data for all participants with at least 3 reliable VFs (false positives < 15%). Average between-group difference in rate of progression (RoP) was tested using a Bayesian mixed model and a 2-sided Bayesian P value <.05 (main outcome). A multivariable model measured the effect of intraocular pressure (IOP). A survival analysis compared the probability of global VF sensitivity dropping by predefined cutoffs (2.5, 3.5, 4.5, and 5.5 dB) from baseline. Data from 352 eyes in the CS-HMS arm and 165 in the CS arm were analyzed (2966 VFs). The mean RoP was -0.26 dB/y (95% credible interval -0.36, -0.16) for CS-HMS and -0.49 dB/y (95% credible interval -0.63, -0.34) for CS. This difference was significant (P=.0138). The difference in IOP only explained 17% of the effect (P < .0001). Five-year survival analysis showed an increased probability of VF worsening by 5.5 dB (P=.0170), indicating a greater proportion of fast progressors in the CS arm. CS-HMS has a significant effect on VF preservation in glaucoma patients compared with CS alone, reducing the proportion of fast progressors.

Two Different Exits: Prediction and Performance of Stocks that are About to Stop Trading

This paper predicts the two most common stock market exits — mergers and drops — using logit models based on firm-level variables and analyzes the returns of stocks that have high exit probabilities. Such analysis is important for investors given that frequent exits are partly responsible for the large US listing gap (Doidge et al., 2017). High merger probability stocks have positive 3-factor alphas and lower-than-average volatility. Firms with high drop probabilities have anomalously negative 3-, 4-, and 5-factor alphas between [Formula: see text]% and [Formula: see text]% per month. Results are robust to controlling for the effects of skewness, volatility, and turnover on returns.

Mathematical Framework for Mixed Reservation- and Priority-Based Traffic Coexistence in 5G NR Systems

Fifth-generation (5G) New Radio (NR) systems are expected to support multiple traffic classes including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) at the same air interface. This functionality is assumed to be implemented by utilizing the network slicing concept. According to the 3rd Generation Partnership Project (3GPP), the efficient support of this feature requires statistical multiplexing and, at the same time, traffic isolation between slices. In this paper, we formulate and solve a mathematical model for a class of Radio Access Network (RAN) slicing algorithms that simultaneously include resource reservation and a priority-based service discipline allowing us to incur fine granularity in the service processes of different traffic aggregates. The system is based on a queueing model and allows parametrization by accounting for the specifics of wireless channel impairments. As metrics of interest, we utilize K-class session drop probability, K-class session pre-emption probability, and system resource utilization. To showcase the capabilities of the model, we also compare performance guarantees provided for URLLC, eMBB, and mMTC traffic when multiplexed over the same NR radio interface. Our results demonstrate that the performance trade-off is dictated by the offered traffic load of the highest priority sessions: (i) when it is small, mixed reservation/priority scheme outperforms the full reservation mechanism; (ii) for overloaded conditions, full reservations provides better traffic isolation. The mixed strategy is beneficial to traffic aggregates with short-lived lightweight sessions, such as URLLC and mMTC, while the reservation only scheme works better for elastic eMBB traffic. The most important feature is that the mixed strategy allows resource utilization to be improved up to 95%, which is 10–15% higher compared to the reservation-only scheme while still providing isolation between traffic types.

On the Influence of AQM on Serialization of Packet Losses.

We study the influence of the active queue management mechanism based on the queue size on the serialization of packet losses, i.e., the occurrences of losses in long, consecutive series. We use a traffic model able to mimic precisely the autocorrelation function of traffic, which is known to be far from zero in packet networks. The main contribution is a theorem on the burst ratio parameter, describing the serialization of losses, proven for an arbitrary function assigning drop probabilities to queue sizes. In numerical examples, we show the impact of the autocorrelation strength, drop probability function, and load of the link, on the serialization of losses.

One-Hop Listening Based ARQs for Low-Latency Communication in Multihop Networks

Inspired by emerging applications in vehicular networks, we address the problem of achieving high-reliability and low-latency communication in multi-hop wireless networks. We propose a new family of Automatic-Repeat-Requests (ARQs) based cooperative strategies wherein high end-to-end reliability is obtained using packet re-transmissions at each hop while the low-latency constraint is met by imposing an upper bound on the total number of packet retransmissions across the network. A hallmark of our strategies is the one-hop listening capability wherein nodes utilize the unused ARQs of their preceding node just by counting the number of failed attempts due to decoding errors. We further extend the idea of one-hop listening to multi-hop listening, wherein a set of consecutive nodes form clusters to utilize the unused ARQs of the preceding nodes, beyond its nearest neighbour, to further improve reliability. Thus, our strategies provide the high-reliability feature with no compromise in the original latency-constraint. For the proposed strategies, we solve non-linear optimization problems on distributing the ARQs across the nodes so as to minimize packet drop probability (PDP) subject to a total number of ARQs in the network. Through extensive theoretical results on PDP and delay profiles, we show that the proposed strategies outperform the best-known strategies in this space.

DETERMINANTS OF DISTANCE EDUCATION DROPOUT: EVIDENCE FOR OPEN UNIVERSITY OF BRAZIL/FEDERAL UNIVERSITY OF SANTA MARIA COURSES

This work seeks to understand the determinants of student dropout in the courses offered by Open University of Brazil system at the Federal University of Santa Maria. To achieve this objective was used: survival function, factorial analysis and logistic function. The mean dropout rates at the UAB/UFSM were close to 47%, and for undergraduate courses this rate was 50.5%, higher than that of postgraduate, from 43.4%. The probability of a student dropping out of a course is 58.85%, and is higher among male students, with higher income, who resides at a greater distance and who need to move more frequently to perform activities in the classroom. Also, it was found that the perception of the academic aspects as adequate and the greater time of dedication reduce the probability of dropout.

Analysis on D2D Heterogeneous Networks with State-Dependent Priority燭raffic

In this work, we consider the performance analysis of state dependent priority traffic and scheduling in device to device (D2D) heterogeneous networks. There are two priority transmission types of data in wireless communication, such as video or telephone, which always meet the requirements of high priority (HP) data transmission first. If there is a large amount of low priority (LP) data, there will be a large amount of LP data that cannot be sent. This situation will cause excessive delay of LP data and packet dropping probability. In order to solve this problem, the data transmission process of high priority queue and low priority queue is studied. Considering the priority jump strategy to the priority queuing model, the queuing process with two priority data is modeled as a two-dimensional Markov chain. A state dependent priority jump queuing strategy is proposed, which can improve the discarding performance of low priority data. The quasi birth and death process method (QBD) and fixed point iteration method are used to solve the causality, and the steady-state probability distribution is further obtained.Then, performance parameters such as average queue length, average throughput, average delay and packet dropping probability for both high and low priority data can be expressed. The simulation results verify the correctness of the theoretical derivation. Meanwhile, the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.

Effects on random early detection of the packet drop probability function with an adjustable nonlinearity

This paper proposed a packet drop probability function with an adjustable nonlinearity parameter in random early detection (RED) for active queue management of a router to control network congestion. We investigated the effect of nonlinearity on the average queue size, average throughput, average retransmission rate, average round-trip time, and fairness index for two widely used loss-based congestion control algorithms: Reno and CUBIC. Simulations were performed with Tail-Drop and the original RED to clarify the effect of nonlinearity under different traffic loads. The results showed that the RED with a nonlinear function did not aggravate the network performance statistics because achieved high throughput while maintaining a low-queuing delay, such as the original RED with a linear function under the extremely heavy traffic condition. Under the light and the heavy traffic conditions, increasing the bending degree of the nonlinear function accomplished high throughput by preventing excessive discarding of packets.

Active Learning Assisted Admission and Bandwidth Management in HWN for Facilitating Differential QoS under Multicriteria Factors

Control of call admission and management of bandwidth are the two important functionalities to achieve higher call handling capacity in heterogeneous wireless networks (HWN). This work addresses the problem of supporting differential qualities of services (QoS) with adherence to multicriteria factors in addition to reducing call-dropping probability in HWN. Toward this end, learning-assisted admission and bandwidth management are proposed. The decision to control the call acceptance ratio and bandwidth allocation level is learned continuously based on current network dynamics and the differential QoS requirements of the current calls. This learning reduces the call drop probability and slippage in QoS for calls. The parameters employed for evaluation in the suggested approach for call admission control include call priority, service type, service delivery mode, bandwidth availability for scalable and nonscalable calls, QoS distortion rate, and call ratio.