Avoid Network Congestion Research Articles

EWPS: Emergency Data Communication in the Internet of Medical Things

In this article, we explore the remote monitoring of patients in an on-demand service that can be implemented using the Internet of Medical Things (IoMT). Network congestion and delay are important issues that must be resolved to handle emergencies in IoMT. Backpressure scheduling is a well-known scheme to resolve network congestion and improve network throughput for emergency data packets. Traditional backpressure scheduling is known to categorize each packet and schedules emergency packets to be forwarded faster than regular packets. However, in large-scale networks, these scheduling algorithms find unnecessarily long paths. This leads to high end-to-end delay and decreases the performance of guaranteed delivery. To resolve these issues, this article proposes an event-aware priority scheduling algorithm for data packets. This model follows a single priority queue model to manage all packets and emergency packets have been identified by the threshold values. Also, a separate communication path has been identified as well to reduce the waiting time for each category of the packet. Meanwhile, to reduce delay in packet communication, a delay-efficient data aggregation tree is constructed, which is combined with the priority queue model. Our in-depth simulation results of the proposed model prove the novel contribution to reducing delay in emergency packet delivery and avoiding network congestion. Moreover, the proposed model is also compared with the existing state-of-the-art models to show its ability to outperform similar methodologies.

Data-Centric Load and QoS-Aware Body-to-Body Network Routing Protocol for Mass Casualty Incident

Triage is the most important requirement of Mass Casualty Incident (MCI) where monitoring the vital signs of casualty is the crucial aspect to assess the severity of their current medical conditions. One of the most significant challenges in the triage center is to provide effective remote monitoring of vital signs of mass casualties. To overcome this challenge, there is a necessity to design a dynamic routing protocol that supports data-centric quality parameters such as delay and reliability as well network-specific quality parameters such as throughput and network lifetime over an ad hoc network. The proposed protocol handles data-centric quality parameters by jointly considering the link and node cost of the neighboring nodes. Further, the protocol handles network-specific quality parameters by including load distribution along with buffer management based on the medical condition of the casualties and beaconless routing mechanism. Furthermore, the proposed approach focuses on the transmission of vital signs of critical casualties while also avoiding network congestion and extending network lifespan. The experimentation results show that the proposed protocol is efficient in handling end-to-end delay, the packet transmission ratio of the critical casualties vital signs as compared to the existing state-of-the-art approaches.

Efficient Replica Migration Scheme for Distributed Cloud Storage Systems

With the wide adoption of large-scale internet services and big data, the cloud has become the ideal environment to satisfy the ever-growing storage demand. In this context, data replication has been touted as the ultimate solution to improve data availability and reduce access time. However, replica management systems usually need to migrate and create a large number of data replicas over time between and within data centers, incurring a large overhead in terms of network load and availability. In this paper, we propose CRANE, an effiCient Replica migrAtion scheme for distributed cloud Storage systEms. CRANE complements any replica placement algorithm by efficiently managing replica creation in geo-distributed infrastructures in order to (1) minimize the time needed to copy the data to the new replica location, (2) avoid network congestion, and (3) ensure the minimum desired availability for the data. Through simulation and experimental results, we show that CRANE provides a sub-optimal solution for the replica migration problem with lower computational complexity than its integer linear program formulation. We also show that, compared to OpenStack Swift, CRANE is able to reduce by up to 60 percent the replica creation and migration time and by up to 50 percent the inter-data center network traffic while ensuring the minimum required data availability.

Multi-Objective Control Optimization for Congestion Avoidance in Computer Networks

Effective congestion control is an issue strongly impacting basic features demanded from modern network environment as reliability, high and stable throughput, and low delays. These characteristics define the quality of communication channels. Optimizing network nodes configuration for only one of mentioned features, can exacerbate other parameters. This paper focuses on avoiding and alleviating network congestions using multi-objective optimization for gain setting of used controllers. Unlike in other presented approaches, in this case the non-stationary, discrete, dynamical model is discussed. The significant advantage of this approach is in the better reflection of the real environment conditions, where the transmission delay is floating. As the further development of the control strategy, the controller with the memory of previous steps have been deployed. Such control strategy mitigates the unfavorable impact of extended delays. Both proposed control strategies tune the presented model of communication channel to alleviate the results of sudden, unexpected network state changes. It is obtained by maximization of available bandwidth usage combined with minimization of buffer utilization. This supports avoiding undesirable congestion effects like packet dropping, retransmissions, high delay, and low network throughput.

Fault-tolerant and Congestion Balanced Routing Algorithm for 2D Mesh NoCs

With the number of cores and nodes in networks-on-chips (NoCs) growing, the node fault occurrence probability is increasing. Although the existing turn model can route packets around the fault area and avoid deadlocks, a large traffic load is generated in the non-rightmost column of the fault region. This paper presents a novel fault-tolerant and congestion balanced (FTCB) routing algorithm that chooses a lower load area as the optimal router path by calculating the maximum path channels to balance traffic load and avoid network congestion. Two methods are proposed to calculate path channels for the fault-free mesh and the fault mesh. The improved odd-even turn rule is introduced to calculate path channels for the fault-free mesh. To balance the network load, free buffer length information is added to path channel calculations, which reflects the global perception. For the non-fault region, we update path channels by using the back formulas from the destination node to the source node. In a fault region, the modified calculation rules of path channels and fault-location odd-even turn rules are given. Compared to the other two related works, the throughput of the FTCB algorithm is improved by 6.92% and 10.7%. Meanwhile, the traffic load of FTCB is decreased to some degree in whole mesh, which shows the FTCB routing algorithm can obviously improve network load balanced, saturation throughput and network latency.

Statistical Learning Based Congestion Control for Real-Time Video Communication

With the increasing demands on interactive video applications, how to adapt video bit rate to avoid network congestion has become critical, since congestion results in self-inflicted delay and packet loss which deteriorate the quality of real-time video service. The existing congestion control is hard to simultaneously achieve low latency, high throughput, good adaptability and fair bandwidth allocation, mainly because of the hardwired control strategy and egocentric convergence objective. To address these issues, we propose an end-to-end statistical learning based congestion control, named Iris. By exploring the underlying principles of self-inflicted delay, we reveal that congestion delay is determined by sending rate, receiving rate and network status, which inspires us to control video bit rate using a statistical-learning congestion control model. The key idea of Iris is to force all flows to converge to the same queue load, and adjust the bit rate by the model. All flows keep a small and fixed number of packets queuing in the network, thus the fair bandwidth allocation and low latency are both achieved. Besides, the adjustment step size of sending rate is updated by online learning, to better adapt to dynamically changing networks. We carried out extensive experiments to evaluate the performance of Iris, with the implementations of transport layer (UDP) and application layer (QUIC) respectively. The testing environment includes emulated network, real-world Internet and commercial LTE networks. Compared against TCP flavors and state-of-the-art protocols, Iris is able to achieve high bandwidth utilization, low latency and good fairness concurrently. Especially over QUIC, Iris is able to increase the video bitrate up to 25%, and PSNR up to 1dB.

Coordinated power and train transportation system with transportable battery-based energy storage and demand response: A multi-objective stochastic approach

Due to the recent upward trend in utilizing renewable energies (REs) as a result of economic reasons and global trend in reducing greenhouse gases (GHG) emission, the independent system operators (ISOs) have been confronted with several obstacles. The system operators have to overcome not only the fluctuating nature of renewable generation, but also the transmission of large amounts of REs from plants to the load centers. This is because of avoiding network congestion to obtain full utilization of renewable generation capacity. In addition, the cleaner production of power systems is one of the major concerns of today’s world. Transportable battery-based energy storage (TBES) system is a novel idea, which utilizes rail roads as a complementary infrastructure to transmit electric power in the form of stored energy in batteries. In this paper, a multi-objective stochastic network constrained unit commitment (NCUC) problem with TBES and demand response (DR) program is presented to minimize the operation cost as well as the total GHG emission of the power grid. The ε-constrained approach is utilized to solve the multi-objective problem and the compromise solution for each case is defined by the min-max method. To manage the wind and load uncertainty the stochastic optimization approach is applied using Monte Carlo simulation method. Moreover, DR programs are introduced as one of the flexible resources to manage the uncertainties, provide cleaner operation and reduce the overall cost by modifying the load profile and peak load shaving. The proposed model is implemented on a 6-bus power system coordinated with 3-station railway network. Simulation results revealed that considering TBES and DR reduces the transmission congestion, total operation cost and GHG emission. The overall operation cost, without considering the emission constraints, is decremented by 11.2% in case of using TBES and DR. On the other side, applying emission constraints led to an increase in overall cost to obtain the compromise solution. However, the total GHG emission is reduced by 17.2% in case of developing TBES and DR.

Deep Learning-Based Traffic Prediction for Energy Efficiency Optimization in Software-Defined Networking

Extensive redundant links and dedicated network components are deployed in existing networks to avoid network congestion caused by peak traffic. These network components are underutilized, leading to an extreme waste of electrical energy and negative environmental consequences. Most energy efficiency schemes have little consideration of the subsequent traffic load as well as the impact of energy saving on network performance, which brings many challenges, such as frequent flow rerouting, continual device state transition, and network load imbalance. In this article, we propose an energy efficiency optimization framework based on traffic prediction in software-defined networking, aiming at reducing network energy consumption while ensuring communication quality. First, we design a real-time traffic prediction mechanism based on gated recurrent unit neural network of deep learning to capture the temporal characteristics of network traffic and provide data basis for the deployment of energy-saving strategies. Second, we propose a heuristic algorithm for energy efficiency optimization to balance flow demand and energy consumption and to achieve dynamic load balancing and energy saving. Finally, simulation is carried out through Ryu controller, Mininet, and TensorFlow. Experimental results show that our algorithm achieves about 47.71% reduction in terms of the overall energy consumption with good network load balancing.

Jellyfish dynamic routing protocol with mobile sink for location privacy and congestion avoidance in wireless sensor networks

Abstract Recently, Wireless Sensor Network (WSN) is often viewed with an oversized range of sensors that are structured and collaborate to gather and transmit information around the targets. As sensors may be positioned in harsh surroundings, it is critical for secure data transmission. Therefore, a dynamic routing path should be essential for WSN applications. In this paper, a Jellyfish Dynamic Routing Protocol (JDRP) for preserving location privacy and congestion avoidance with less delay guaranteed is proposed. With this routing technique, the complete sensor field is divided into different subdivisions and each subdivision elects a target area by computing its transmission distance. The backbone of the dynamic routing protocol consists of a virtual ring called bell nodes and a radial line called tentacle nodes employs more nodes to construct the network. The amount of radial line and radius of the virtual ring in a network are conjointly determined to ease the communication path from the node to sink. In this structure, the radial line paths are routed directionally and bell nodes are routed with angular directions probabilistically. From the routing path, the tentacle nodes collect the data to dynamic sink which will assure that the information is going to be collected with less delay and attacker cannot guess their positions. The experimental results show that the proposed JDRP method accomplishes enhanced performance in terms of energy consumption, packet delivery delay and lifetime.

LoRa-Based Anomaly Detection Platform: Center and Sensor-Side

The implementation of a LoRa-based wireless sensor network, aiming to operate as an edge-based anomaly detection system is elaborated in this paper. The dominant prerequisites are defined as achieving a maximum possible sensing rate on edge, minimum Communication packet loss, the lowest possible false alarm, and the optimal resource management. In this regard, the developed sensor-side algorithm manages measurements accordingly to make effective communications with the center while transmitting either notification or requested compressed data packets. On the other side, a central algorithm keeps all the authorized sensors synchronized and updated. Besides, the center avoids network congestion primarily by queuing the received event notifications and later, by transmitting the data queries consecutively under supervision. The re-construction of the received anomaly-related data packets for further evaluations is another task to do by the center. Overall, the complementary proposal is experimentally tested and validated through the network operation in a worst-case scenario.

Robust congestion control in cognitive radio network using event-triggered sliding mode based on reaching laws

In a band-limited spectrum, an increase in requests for various services of wireless applications is a matter of apprehension. To fulfill the expectations of immense wireless users, effective management and efficient use of the band-limited spectrum is highly desired. Cognitive Radio emerges as a solution to the problem of spectrum scarcity by opportunistically sharing the spectrum among licensed and unlicensed users. A careful balance of spectrum usage among different priority class users must be ensured to avoid network congestion. A robust congestion controller with three different reaching laws has been studied in this work for optimal spectrum utilization in Cognitive Radio Network. The sliding mode controller, which is eminent for its robustness and disturbance rejection capabilities has been combined with a novel dynamic event-triggering scheme to reduce energy expenses and computational complexities. The controller adequacy has been supported by simulations.

Grey wolf optimizer based fuzzy-PI active queue management design for network congestion avoidance

<p>Congestion is one of the most important issues in communication networks which has attracted much research attention. To ensure a stable TCP network, we can use active queue management (AQM for early congestion detection and router queue length regulation. In this study, it was proposed to use the Grey Wolf Optimizer (GWO) algorithm in designing a fuzzy proportional integral (fuzzy-PI) controller as a novel AQM for internet routers congestion control and for achieving a low steady-state error and fast response. The suggested Fuzzy logic-based network traffic control strategy permit us to deploy linguistic knowledge for depicting the dynamics of probability marking functions and ensures a more accurate use of multiple inputs to depict the the network’s state. The possibility of incorporating human knowledge into such a control strategy using Fuzzy logic control methodology was demonstrated. The postulated controller was compared to proportion integral (PI) through several MATLAB simulation scenarios. The results indicated the stability of the postulated controller and its ability to attain a faster response in a dynamic network with varying network load and target queue length.</p>

Big data over wireless pipe-Path: Queuing model for multi-Directional radio links

Big data needs to be delivered to remote devices via wireless links in many applications such as North Pole monitoring, UAV communications, smart city crowd-sourcing, etc. Single routing path may not be able to meet the ultra-high-throughout requirements of wireless big data delivery. A pipe-like path consisting of closely coupled links (forming a bundle) is more suitable to big data transmissions. The queuing model for such a coupled routing pipe is important to network congestion diagnosis and avoidance. Particularly, the queuing analysis between neighboring hops can help to analyze the pipe traffic accumulation dynamics. Our pioneering work presented here first develops a queuing model for a single node of the pipe. We design a full Markov-Chain-based queuing model for bulk arrival, bulk service queues with multiple vacations. We then extend such a single node queuing model to the pipe-based queuing scenarios, and deduce the closed-form equations for the typical queue statistics such as the service delay. The numerical results can serve as the guidelines for big data transmission protocols. Comprehensive, accurate simulations have been conducted to verify the correctness of our proposed pipe queuing models.

A bidirectional congestion control transport protocol for the internet of drones

Wireless sensor networks (WSNs) are composed of energy constrained devices that autonomously form networks through which sensed information is transported from the region of interest to the central control station (sink), integration with unmanned aerial vehicles (UAVs) leads to enlarged monitoring area and to enhance overall network performance. Due to application specific nature of wireless sensor networks, it is challenging to design a congestion control protocol that is suitable for all types of applications in the Internet of Drones (IoD). Congestion avoidance and control in wireless sensor networks mainly aims at reducing packet drop due to congestion and maintaining fair bandwidth allocation to all network flows. In this research work, we propose a reliable and congestion based protocol, which provides both bidirectional reliability and rate adjustment based congestion control. It uses Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to select optimal path for data transmission because TOPSIS selects an alternative such that it has shortest distance from the ideal one and greatest distance from negative ideal solution. Congestion is detected by using proportion of average packet service time over average packet inter-arrival time as congestion degree. Then, congestion is notified using implicit notification to save energy and reduce overhead. To mitigate congestion along with maintaining fairness, an equal priority index is assigned to all data sources and when congestion occurs, rate adjustment to optimal value based on priority value is used for congestion control. This approach helps to diminish packet drops, maintain fairness and get better energy efficiency. Finally, we compare the performance of the proposed protocol with that of existing protocols. Our simulation results show reduced average delivery overhead, drop packet ratio, queue length and delay with increased average delivery ratio. Moreover, our protocol provides better energy efficiency and fairness when compared with the existing competing protocols.

SDN Candidate Selection in Hybrid IP/SDN Networks for Single Link Failure Protection

We focus on the problem of selecting a smallest subset of IP routers for upgrading to SDN switches to protect all single link failures in a given network, or the SDN candidate selection problem . In solving the problem, we also aim at minimizing the repair path length for reducing the delay experienced by the rerouted traffic and saving network bandwidth. The key enabler is a novel tunneling mechanism for constructing multi-tunnel repair paths by leveraging multiple SDN switches. Multi-tunnel repair paths provide additional flexibility in rerouting and a new SDN candidate selection (SCS) algorithm is then designed to take advantage of them. To further reduce the repair path length, we propose to replace the link-based tunneling adopted by fault-detection routers by destination-based tunneling. If the network traffic distribution is available, we further show that destination-based tunneling can be used to avoid network congestion.

Congestion-Aware and Energy-Aware Virtual Network Embedding

Network virtualization is an inherent component of future internet architectures. Network resources are virtualized from the underlying substrate and elastically provisioned and offered to customers on-demand. Optimal allocation of network resources in terms of utilization, quality of service, and energy consumption has been a challenge. Existing solutions consider congestion control in a single-objective virtual network embedding (VNE) problem. This paper defines a multiple-objective VNE problem called the congestion-aware, energy-aware VNE (CEVNE). The aim is to seek a solution that saves cost, saves energy and avoids network congestion simultaneously. CEVNE modelling techniques and solution approaches apply both the weighting method and the constraint method to search for pareto-optimal solutions that produce the best compromised solutions for all three objectives. Solving VNE problem is, however, NP-hard. A heuristic solution is proposed involving a two-stage coordinated CEVNE. The node-mapping algorithm searches for the sub-optimal solutions for three objectives. The link mapping process is an SDN-based heuristic algorithm that deploys a path service and a resource monitoring application on an SDN controller. The solution is realized using SDN, Segment Routing, and open network operating system platform (ONOS) technologies. The energy minimization is implemented with a registry that keeps track of active nodes and sets inactive nodes to sleep mode. The evaluation results showed that the multiple-objective CEVNE approach is feasible and achieves its goals of optimizing the resource allocation, improving the runtime, saving the energy consumption and controlling the network congestion.

An Adaptive Active Queue Management Based on Model Predictive Control

In recent years, active queue management (AQM) has gained more and more attention as an important part of network congestion control. Although there are many AQM algorithms, these algorithms show weaknesses to detect and control congestion due to the complexity and dynamics of the networks. Hence, this paper proposes a new AQM algorithm based on model predictive control (MPC) theory which has been widely applied in nonlinear and time-delay systems. In order to adjust the parameters of the MPC-based AQM algorithm adaptively according to network scenario variations, the adaptive mechanism is introduced into the new algorithm, named PHAQM, by using the Hebb learning rules from the neural network control theory. The simulation results show that the algorithm is effective in avoiding network congestion. Compared to the traditional AQM schemes, such as PI, REM, and GPC algorithm, the PHAQM has a faster convergence rate and smaller queue length fluctuations and outperforms especially under dynamically changing network situations.

Edge-centric Resource Allocation for Heterogenous IoT Applications using a CoAP-based Broker

Edge/fog computing is an emerging paradigm that exploits computing and management at the edge of the network to improve QoS of traditional cloud-centric IoT applications. In large-scale IoT deployments, multiple applications are expected to access the same constrained resource (e.g., a sensor) with heterogeneous QoS requirements. However, IoT applications may be unable to adapt their access patterns to cope with bandwidth limitations. To address this issue, we propose a fog-based IoT broker that determines suitable notification rates to access IoT resources while maximising applications' QoS and avoiding network congestion. We first formalise the rate selection as an optimisation problem, and then we propose a practical algorithm that leverages transmission reliability to dynamically select notification periods. We developed a CoAP-based prototype and validated our proposal through simulations and experiments in a real IoT testbed. Our broker provides higher QoS satisfaction, throughput and energy efficiency compared to a conventional CoAP proxy.

Predictive Traffic Control and Differentiation on Smart Grid Neighborhood Area Networks

Smart Grid (SG) networks include an associated data network for the transmission and reception of control data related to the electric power supply service. A subset of this data network is the SG Neighborhood Area Network (SG NAN), whose objective is to interconnect the subscribers’ homes with the supplier control center. The data flows transmitted through these SG NANs belong to different applications, giving rise to the need for different quality of service requirements. Additionally, other subscriber appliances could use this network to communicate over the Internet. To avoid network congestion, as well as to differentiate the quality of service (QoS) received by the different data flows, a congestion control mechanism with traffic differentiation capabilities is required. The main contribution of this work is the proposal of a new congestion control mechanism based on machine learning techniques to try to guarantee the different QoS requirements to the different data flows. A main problem when applying machine learning techniques is the need for datasets to be used in the training steps. In this sense, a second contribution of this article is the proposal of a method to generate such datasets by means of simulation techniques. The proposed mechanism is then evaluated in the context of a wireless SG NAN. The nodes of this network are the subscriber’s smart meters, which in turn perform the function of concentrating the data traffic sent and received by the rest of the home appliances. Besides, different machine learning classification methods are taken into account. The evaluation carried out shows significant improvements in terms of network throughput, transit time, and quality of service differentiation. Finally, the computational cost of the algorithms used in this proposal has also been evaluated, using real low-cost IoT hardware platforms.

ИСПОЛЬЗОВАНИЕ СПЛАЙН-ЭКСТРАПОЛЯЦИИ ДЛЯ ПОВЫШЕНИЯ ПОКАЗАТЕЛЕЙ КАЧЕСТВА ТЕЛЕКОММУНИКАЦИОННЫХ СИСТЕМ

The problem of predicting self-similar traffic with significant and frequent ripples and the property of long-term dependence is considered. For short-term prediction of self-similar traffic, which is performed outside the considered time interval, a method of spline extrapolation based on various spline functions (cubic and cubic B-splines) is proposed. We recommend a method for estimating traffic prediction errors for each traffic prediction option using cubic or cubic B-splines. Comparison of the results of predicting self-similar traffic using various spline functions (cubic and cubic B-splines) show that the accuracy of the prediction can be improved by using cubic B-splines. According to the results of the study, it is established that cubic B-splines are characterized by high constructively, adequacy to the tasks and simplicity of practical implementation. Their use makes it possible to greatly simplify computational processes using formalized mathematical constructs. The use of the proposed method of spline extrapolation based on cubic B-splines has several advantages over other known methods, namely, ease of practical implementation, high accuracy of forecasting, the ability to accurately extrapolate peak "bursts" of traffic, which is especially important when solving problems in real time. The results of predicting self-similar traffic will allow the operator, at the stage of design and further operation of the mobile communication network, to provide the required amount of buffer devices for the network hardware and software, thereby avoiding network congestion and exceeding standard values of traffic service quality indicators. Proposed is the prospect of further research through the use of wavelet-extrapolation in order to improve the accuracy of prediction. Practical significance is determined by the developed recommendations for using the spline-extrapolation method in solving various problems of designing networks of various technologies and structures.