Drop Probability Research Articles

Congestion Control Mechanism Based on Dual Threshold DI-RED for WSNs

In wireless sensor networks (WSNs), traffic flow congestion can reduce network performance. We propose a congestion control method for WSNs that use a cache state with a dual threshold in the router buffer to control congestion. In addition, the congestion is controlled by the channel transmission state that is monitored by the queuing variation tendency and the transmission rate. Based on the active queue management mechanism, a dual threshold with the double index random early detection (DI-RED) model is established to improve network performance. We also present the congestion control algorithm of DI-RED model to ensure the perfect actualization. By solving the DI-RED model, a series of channel indicators is obtained, including packet loss rate, average queue length, delay and throughput. Simulation results show that the proposed DI-RED model is more stable and demonstrates better control, which can overcome the parameter sensitivity of the RED mechanism. The proposed model realises a reasonable tradeoff between network performance and packet dropping probability, which effectively avoids degraded network performance caused by pursuing a lower packet dropping probability. The results are important when selecting a congestion control mechanism and parameter settings for WSNs.

Surrogate dropout: Learning optimal drop rate through proxy

Dropout is commonly used in deep neural networks to alleviate the problem of overfitting. Conventionally, the neurons in a layer indiscriminately share a fixed drop probability, which results in difficulty in determining the appropriate value for different tasks. Moreover, this static strategy will also incur serious degradation on performance when the conventional dropout is extensively applied to both shallow and deep layers. A question is whether selectively dropping the neurons would realize a better regularization effect. This paper proposes a simple and effective surrogate dropout method whereby neurons are dropped according to their importance. The proposed method has two main stages. The first stage trains a surrogate module that can be jointly optimized along with the neural network to evaluate the importance of each neuron. In the second stage, the output of the surrogate module is regarded as a guidance signal for dropping certain neurons, approximating the optimal per-neuron drop rate when the network converges. Various convolutional neural network architectures and multiple datasets, including CIFAR-10, CIFAR-100, SVHN, Tiny ImageNet, and two medical image datasets are used to evaluate the surrogate dropout method. The experimental results demonstrate that the proposed method achieves a better regularization effect than the baseline methods.

Optimal Pending Interest Table Size for ICN With Mobile Producers

Many next generation Internet architectures exist in the literature for addressing various issues like increasing traffic, mobility and efficient content dissemination. One such emerging fundamental design is Information Centric Networking (ICN). The Pending Interest Table (PIT) is one of the essential components of the ICN forwarding plane responsible for the stateful routing in ICN. Optimal size of the PIT is essential for the efficient performance of the network and the enhanced consumer experience. Therefore, the optimal sizing of the PIT under various network conditions is an important and challenging problem. To this end, this paper models the PIT of a router as a GI/M/c/N queue. The model has (i) a general arrival process to accommodate the diverse nature of traffic, (ii) a service time model which takes into account the caching at the content stores and the mobility of producers, and (iii) a sojourn time distribution which is used to characterize the content delivery time at the consumers. Using the GI/M/c/N queueing model, we formulate an optimization problem to minimize the PIT size while subjecting the interest drop probability to an upper bound. The accuracy of our analytical model is demonstrated using simulations on different Internet Service Provider (ISP) topologies across a wide range of system parameters.

Trade-off queuing delay and link utilization for solving bufferbloat

Abstract To solve the bufferbloat problem, active queue management (AQM) has been recommended but existing AQM algorithms suffer from poor adaptability to dynamic traffics. We develop a novel adaptive AQM algorithm named TODU for a better Trade-Off between queuing Delay and link Utilization. TODU aims to maintain a stable queue length against dynamical network traffics by adapting the drop probability to balance the expected link utilization and queuing delay. We compare TODU with benchmark algorithms in Network Simulator-2 and the results demonstrate that TODU achieves a better trade-off between low queuing latency and high link utilization in a dynamical environment.

Vertical Handoff from WLAN to Cellular Network Based on Relay Environment

Seamless handoff in loosely coupled WLAN/cellular systems is still a challenging issue. Since the cellular network overlaps the WLAN during a handoff from a cellular network to a WLAN, this type of handoff is less complicated. Thus, all the attempts go toward the handoff from the WLAN to the cellular network. In the cellular network, this handoff is considered as a new call. Therefore, it has a significant delay which makes it inappropriate for the real-time services. The worst case occurs when a mobile station (MS) exits a building surrounding the WLAN. In such a case, there might not be enough time for the handoff to be completed due to the abrupt loss of the received signal strength indication. In this paper, the proposed solution uses MSs as relay stations (RSs) and the existing frequency channels dedicated to the RSs to provide the required WLAN coverage during the handoff process. The suggested solution is analyzed using a simple proposed analytical model. Both the analytic and simulation results confirm that the suggested method improves the handoff dropping probability.

Performance Analysis of Priority-Based Access Class Barring Scheme for Massive MTC Random Access

In a massive machine-type communication (MTC) environment, a traffic control algorithm is essential to solving traffic congestion problems. Additionally, as MTC applications have diverse quality-of-service requirements, traffic control should be applied in a way that guarantees diverse requirements. Therefore, we propose a priority-based access class barring (PACB) scheme, in which MTC devices are divided into multiple classes according to their delay requirements, and a different access class barring (ACB) factor is used for each class. This article suggests an approach to choose multiple ACB factors that can maximize the number of successful access events and can also guarantee the low delay requirement of the high-priority class. Then, an analytical model is proposed for the ideal case, and throughput, drop probability, and access delay are derived to evaluate the performance. For a realistic environment, we also suggest an estimation algorithm for the PACB scheme, in which a base station estimates the number of active devices for each class. Numerical and simulation results showed that the analytical model is consistent with the simulation results. Additionally, the performance of the estimation algorithm was similar to that of the ideal case. Furthermore, the proposed PACB scheme can decrease the delay of a high-priority class and improve the throughput and drop probability compared to existing algorithms.

Spectrum efficiency in CRNs using hybrid dynamic channel reservation and enhanced dynamic spectrum access

Blocking of new arriving services and dropping of ongoing services are inherent problems in Cognitive Radio Networks (CRNs), which need to be addressed to enhance spectrum efficiency. In particular, Secondary Users (SUs) undergo service degradation in the face of Primary Users (PUs)’ arrivals. In this paper, we present a scheme called Efficient Spectrum Utilization (ESU) that reduces the dropping and blocking probabilities of existing and new services, respectively, to make efficient use of the available spectrum. The scheme divides the available spectrum into reserved and non-reserved bands. The reserved band is dynamically allocated a number of channels from the non-reserved band in order to accommodate those services which face interruptions while operating in the non-reserved band. The scheme renders dynamic access to the available spectrum and facilitates priority-based channel allocation and termination. SUs are divided into low and high priority levels depending on their Quality of Service (QoS) requirements. SUs with low priority level are granted direct access to both the bands to enhance channel utilization. SUs operating in the reserved band with high priority levels are granted uninterruptible status to ensure a certain level of service provisioning to SUs. The proposed ESU scheme is modeled using Continuous Time Markov Chain (CTMC) and mathematical expressions are derived for several QoS parameters. Performance of the proposed scheme is evaluated under various network conditions. Results demonstrate that ESU reasonably improves spectrum efficiency under channel failure in CRNs.

An Exponential Active Queue Management Method Based on Random Early Detection

Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.

Uniform distribution for Pachinko

Pachinko is a Japanese mechanical gambling game similar to pinball. Recently, several mathematical models of Pachinko have been proposed. A number of pins are spiked in a field. A ball drops from the top of the playfield and the ball falls down. In the 50-50 model, if the ball hits a pin, it moves to the left or right passage of the pin with an equal probability. An arrangement of pins generates a distribution of the drop probability for all of the columns. This problem was considered by generating uniform distributions. Previous studies have demonstrated that the (1/2a)-uniform distribution is possible for a∈{0,1,2,3,4} and is conjectured so that it is possible for any positive integer a. This study describes the constructive proof for this conjecture. This study also formalizes a natural decision problem yielded by this model while investigating its computational complexity. More precisely, given any drop-probability distribution A and any partial drop-probability distribution B, this study uses non-deterministic polynomial-time (NP) hardness to determine if there exists a pin arrangement that transforms A into B.

Flexible hybrid state estimation for power systems with communication irregularities

This study proposes a novel flexible hybrid state estimation (SE) algorithm when a realistic communication system with its irregularities is taken into account. This system is modelled by the Network Simulator 2 software tool, which is also used to calculate communication delays and packet drop probabilities. Within this setup, the system observability can be predicted, and the proposed SE can decide between using the static SE (SSE) or the discrete Kalman filter plus SSE-based measurements and time alignment (Forecasting-aided SE). Flexible hybrid SE (FHSE) incorporates both phasor measurement units and supervisory control and data acquisition-based measurements, with different time stamps. The proposed FHSE with detailed modelling of the communication system is motivated by: (i) well-known issues in SSE (time alignment of the measurements, frequent un-observability for fixed SE time stamps etc.); and (ii) the need to model a realistic communication system (calculated communication delays and packet drop probabilities are a part of the proposed FHSE). Application of the proposed algorithm is illustrated for examples with time-varying bus load/generation on two IEEE test cases: 14-bus and 300-bus.

Efficient task pruning mechanism to improve robustness of heterogeneous computing systems

In heterogeneous distributed computing (HC) systems, diversity can exist in both computational resources and arriving tasks. In an inconsistently heterogeneous computing system, task types have different execution times on heterogeneous machines. A method is required to map arriving tasks to machines based on machine availability and performance, maximizing the number of tasks meeting deadlines (defined as robustness). For tasks with hard deadlines (e.g., those in live video streaming), tasks that miss their deadlines are dropped. The problem investigated in this research is maximizing the robustness of an oversubscribed HC system. A way to maximize this robustness is to prune (i.e., defer or drop) tasks with low probability of meeting their deadlines to increase the probability of other tasks meeting their deadlines. In this paper, we first provide a mathematical model to estimate a task’s probability of meeting its deadline in the presence of task dropping. We then investigate methods for engaging probabilistic dropping. We propose methods to dynamically determine task dropping and deferring threshold probabilities. Next, we develop a pruning system and a pruning-aware mapping heuristic, which we extend to engender fairness across various task types. We present the pruning mechanism as an independent component that can be applied to any mapping heuristic to improve the system robustness. To reduce overhead of the pruning mechanism, we propose approximation methods that remarkably reduce the number of mathematical calculations and improve the practicality of deploying the mechanism in heterogeneous or even homogeneous computing systems. We show the cost and energy gains of the pruning mechanism. Simulation results, harnessing a selection of mapping heuristics, show efficacy of the pruning mechanism in improving robustness (on average by ≃22%) and cost in an oversubscribed HC system by up to ≃33%.

Inter-slice handover management in a V2X slicing environment using bargaining games

This paper provides a V2X slicing logical architecture based on Software Defined Networking. We devote a special concern to inter-slice mobility management. In this context, we propose a complete mobility solution that handles resources management through the use of bargaining games enhanced by a heuristic and particular swarm optimization algorithm. More specifically, this work proposes a resource borrowing scheme to readjust resources of an overloaded slice in case of a inter-slice handover. The borrowing procedure is modeled via two bargaining games. Performance analysis shows that our proposed solution guarantees fairness between V2X slices, reduces the probability of call dropping and blocking probabilities and enhances resource utilization. Moreover, a comparative study between the proposed games is provided and analyzed.

BD-ELM: A Regularized Extreme Learning Machine Using Biased DropConnect and Biased Dropout

In order to prevent the overfitting and improve the generalization performance of Extreme Learning Machine (ELM), a new regularization method, Biased DropConnect, and a new regularized ELM using the Biased DropConnect and Biased Dropout (BD-ELM) are both proposed in this paper. Like the Biased Dropout to hidden nodes, the Biased DropConnect can utilize the difference of connection weights to keep more information of network after dropping. The regular Dropout and DropConnect set the connection weights and output of the hidden layer to 0 with a single fixed probability. But the Biased DropConnect and Biased Dropout divide the connection weights and hidden nodes into high and low groups by threshold, and set different groups to 0 with different probabilities. Connection weights with high value and hidden nodes with a high-activated value, which make more contribution to network performance, will be kept by a lower drop probability, while the weights and hidden nodes with a low value will be given a higher drop probability to keep the drop probability of the whole network to a fixed constant. Using Biased DropConnect and Biased Dropout regularization, in BD-ELM, the sparsity of parameters is enhanced and the structural complexity is reduced. Experiments on various benchmark datasets show that Biased DropConnect and Biased Dropout can effectively address the overfitting, and BD-ELM can provide higher classification accuracy than ELM, R-ELM, and Drop-ELM.

Characterizing Resource Allocation Trade-Offs in 5G NR Serving Multicast and Unicast Traffic

The use of highly directional antenna radiation patterns for both the access point (AP) and the user equipment (UE) in the emerging millimeter-wave (mmWave)-based New Radio (NR) systems is inherently beneficial for unicast transmissions by providing an extension of the coverage range and eventually resulting in lower required NR AP densities. On the other hand, efficient resource utilization for serving multicast sessions demands narrower antenna directivities, which yields a trade-off between these two types of traffic that eventually affects the system deployment choices. In this work, with the tools from queuing theory and stochastic geometry, we develop an analytical framework capturing both the distance- and traffic-related aspects of the NR AP serving a mixture of multicast and unicast traffic. Our numerical results indicate that the service process of unicast sessions is severely compromised when (i) the fraction of unicast sessions is significant, (ii) the spatial session arrival intensity is high, or (iii) the service time of the multicast sessions is longer than that of the unicast sessions. To balance the multicast and unicast session drop probabilities, an explicit prioritization is required. Furthermore, for a given fraction of multicast sessions, lower antenna directivity at the NR AP characterized by a smaller NR AP inter-site distance (ISD) leads to a better performance in terms of multicast and unicast session drop probabilities. Aiming to increase the ISD, while also maintaining the drop probability at the target level, the serving of multicast sessions is possible over the unicast mechanisms, but it results in worse performance for the practical NR AP antenna configurations. However, this approach may become feasible as arrays with higher numbers of antenna elements begin to be available. Our developed mathematical framework can be employed to estimate the parameters of the NR AP when handling a mixture of multicast and unicast sessions as well as drive a lower bound on the density of the NR APs, which is needed to serve a certain mixture of multicast and unicast traffic types with their target performance requirements.

A visually driven nonlinear droop control for inverter-dominated islanded microgrids

The network asymmetries have led to the conventional droop methods that have significant reactive power sharing error during islanding operation of a microgrid. That is why the expandability, modularity, flexibility and communication-free nature of droop-based methods do not let the researchers to easily replace them with another kind of solution. Accordingly, this paper proposes a visually driven nonlinear droop curve making it possible to improve the reactive power sharing accuracy. Of course, due to the probability of the voltage drop at the output terminals of distributed generations, a voltage restoration mechanism should be also presented to maintain the voltage within an acceptable range. In this way, the proposed method can successfully improve both the reactive power sharing accuracy and voltage profile even during fault conditions. Both simulation and experimental case studies using various scenarios are conducted to demonstrate the merits of the proposed scheme. According to the obtained results, the proposed scheme ensures the voltage regulation of MG buses at all times. For instance, the accuracy of reactive power sharing in heavy load conditions has been increased to 96.5% which indicates 21.2% increase in comparison with the conventional droop strategy.

Multiple QoS provisioning with pre-emptive priority schedulers in multi-resource OFDMA networks

In this paper, we present an analytical framework for the performance evaluation of a pre-emptive priority scheduler in multi-resource networks, like those using Orthogonal Frequency Division Multiple Access (OFDMA). We focus on Quality of Service (QoS)-guaranteed traffic for which QoS is guaranteed to individual users by restricting the number of admitted users. For this, the QoS-constrained capacity, in terms of the number of supported users, needs to be ascertained a priori. The QoS-constrained capacity is a function of users’ QoS requirements, channel conditions, and radio resource allocation algorithms, which in this work is the pre-emptive priority scheduler. It is, thus, a variable quantity and mostly obtained using time-consuming offline computer simulations. Mathematical models, on the other hand, are timely and accurate, allowing the capacity to be derived in real-time as a function of the current network configuration. Existing works on mathematical modelling of pre-emptive priority schedulers have mostly focussed on single servers or multiple servers where a single server is assigned to each user. In contrast, OFDMA networks have multiple radio resources, i.e., multiple servers and each user may need more than one radio resource for a single packet transmission, i.e. it is a multi-resource system, which has been accounted for in this paper. We classify the users based on their resource requirements and model the pre-emptive priority scheduler as a multi-class, multi-server, multi-resource, non-work conserving queueing system. We derive its QoS metrics like average delay, packet drop probability, throughput, etc., from its continuous-time Markov Chain. We then use the derived QoS metrics to obtain the QoS-constrained capacity and design a threshold based predictive call admission control unit. We have validated the results using extensive discrete event simulations.

Active Queue Management Supporting TCP Flows Using Disturbance Observer and Smith Predictor

Active queue management (AQM) is a technique to avoid serious congestion of the transmission control protocol (TCP) flows at a router. AQM based on control theory, which utilizes congestion controllers such as proportional-derivative (PD) controller or proportional-integral-derivative (PID) controller, has been previously proposed. In addition, disturbance observer (DOB) has been utilized to compensate for modeling error of a TCP/AQM congestion control system. However, the DOB-based controllers cannot cope with a large time delay in TCP/AQM networks. Although one of the effective time delay compensators is Smith predictor (SP), the implementation of the DOB and SP in an integrated manner has not been accomplished, because of saturation due to the input limit of packet drop probability. In this paper, a novel TCP/AQM congestion control system with the DOB and SP considering the saturation function is proposed to compensate for the modeling error and time delay simultaneously. Simulation results show that the proposed controller provides better throughput and goodput than conventional controllers. One of the simulations assume that the propagation delay and bottleneck link capacity are set to 100 ms and 100 Mbps, respectively. Under this assumption, it is confirmed that the proposed controller achieves a goodput of 99.55 Mbps whereas the classical PID controller and PD controller with DOB achieve 99.23 Mbps and 99.24 Mbps, respectively.

A Multi-Objective Model Checking for Transmission Policy Optimization in Hybrid Powered Small Cell Networks

In the fifth generation (5G) communication system, maximizing energy utilization is one of the key challenges especially with limited green energy sources. In particular, it is important to determine the optimal transmission policy for the hybrid model with grid and green energy in small cell networks. The optimal transmission policy should minimize both grid power consumption and wastage of green harvested energy at the same time; while satisfying the quality of service (QoS) requirements such as minimum packet drop probability. In contrast to existing policies with a single objective for the hybrid powered small cell networks, the proposed transmission policy adopts a multi-objective model checking for Markov decision process (MOMC-MDP) with a linear temporal logic (LTL) scheme. The MOMC-MDP firstly determines all feasible decisions for packet transmissions in every time slot, and then chooses one of the best feasible decisions to obtain the optimal transmission policy. Three dimension Markov decision process (3D-MDP) that includes packet buffer, battery capacity, and channel state, are used with the age of information (AoI) of the packet arrivals to improve the overall performance of the proposed MOMC-MDP scheme. Numerical results validate that the proposed MOMC-MDP with AoI can provide higher green energy utilization compared to the conventional MDP-based schemes.

A neuron-based active queue management scheme for internet congestion control

To deal with nonlinear and complex problems of internet congestion control, an intelligent scheme is required, which can learn the traffic pattern of the network. In this paper, we design a robust AQM scheme called neuron-based AQM (N-AQM) to efficiently control the complex network congestion problem and achieve QoS. In N-AQM, a neural network is used to predict the future value of current queue length and estimate the differential queue length error and use it to define the packet drop probability. Our simulation result demonstrates that N-AQM is stable, robust and outperforms other AQM schemes. From the result section, it is observed that N-AQM is more efficient in stabilising the queue length around the target with faster settling time and incurs lower oscillation than others.

Optimal Control Based on Scheduling for Comfortable Smart Home Environment

Smart home environments account to a major portion of the total energy consumption in today’s world. The residents of smart home environments wish to find solutions that reduce the energy costs along with providing an optimal indoor environment for the residents. Another significant aspect in smart home systems is efficiency of tasks management and control commands’ execution for smart home actuators. In this paper, we propose an optimal control solution for smart home environment based on smart home energy optimization and control tasks’ load dispatching and scheduling. Optimal control is achieved by first defining an objective function for minimizing energy cost which is implemented using VB-PSO (velocity boost particle swarm optimization) algorithm. Next, the control tasks are generated using rule set implemented in fuzzy logic; defined based on optimal values achieved from VB-PSO. A Markov model based mechanism dispatches control tasks at scheduler, for efficient scheduling and optimal control. The results show that the proposed optimization scheme saves up to 29.73% energy costs on average, in comparison to baseline scheme. The proposed tasks’ load dispatching scheme of admission control, makes the job of load balancing among the processors efficient while giving priority to the urgent tasks. The results for scheduler evidently show the low dropping probabilities for urgent tasks along with showing 34.9% reduction in tasks’ starvation rate and 36.82% reduction in average tasks’ instances missing rates.