Search Results (30)

Efficient congestion management in Software-Defined Networks (SDNs) remains a significant challenge due to dynamic traffic patterns and complex topologies. Conventional congestion control techniques based on static or heuristic rules often fail to adapt effectively to real-time network variations. This paper proposes a data-driven framework based on Multi-Agent Reinforcement Learning (MARL) to enable intelligent, adaptive congestion control in SDNs. The framework integrates two collaborative agents: a Congestion Classification Agent that identifies congestion levels using metrics such as delay and packet loss, and a Decision-Making Agent based on Deep Q-Learning (DQN or its variants), which selects the optimal actions for routing and bandwidth management. The agents are trained offline using both synthetic and real network traces (e.g., the MAWI dataset), and deployed in a simulated SDN testbed using Mininet and the Ryu controller. Extensive experiments demonstrate the superiority of the proposed system across key performance metrics. Compared to baseline controllers, including standalone DQN and static heuristics, the MARL system achieves up to 3.0% higher throughput, maintains end-to-end delay below 10 ms, and reduces packet loss by over 10% in real traffic scenarios. Furthermore, the architecture exhibits stable cumulative reward progression and balanced action selection, reflecting effective learning and policy convergence. These results validate the benefit of agent specialization and modular learning in scalable and intelligent SDN traffic engineering. Full article

Network packets may arrive out of their original order due to network delays, transmission speed variations, congestion, or uneven resource distribution. These factors cause significant challenges to network performance. These challenges result in jitter, packet loss, and reduced throughput, negatively affecting the efficient arrangement of packets. The Multipath tunnel-Generic Routing Encapsulation (MPT-GRE) architecture addresses this issue through a packet reordering mechanism designed for multipath GRE with User Datagram Protocol (UDP) encapsulation networks. This study investigates and analyses the path-specific delays, jitter, and transmission speed constraints to evaluate the influence of out-of-order packets on the MPT-GRE tunnel throughput aggregation capability. By comparing scenarios with and without the re-ordering mechanism, the results demonstrate that the reordering mechanism substantially improves the traffic throughput in symmetric and asymmetric channel configurations. Additionally, the study emphasizes the critical role of optimizing the reordering window parameter for effective performance. These findings confirm that packet reordering mechanisms significantly enhance MPT-GRE network performance by reducing the negative effects of delays and out-of-order arrivals. Full article

Various uncertainties such as communication delay, packet loss and disconnection in the Industrial Internet, as well as the asynchronous sampling of sensors, can cause irregularity, sparsity, and misalignment of sampling sequences, and thereby seriously affect the training and prediction performance of a digital twin model. Sequence reconstruction is an effective way to deal with the above problems, but if the measurement data become sparse or contain significant noise due to packet loss and electromagnetic interference, existing methods struggle to achieve ideal results. Therefore, a novel variational autoencoder model based on a parallel reference network and neural controlled differential equation (PRN-NCDE) is proposed in this article to solve the problem of reconstructing irregular series under sparse measurements and high noise levels. First, a multi-channel self-attention module is established, which can not only analyze the position and feature information of the sampled data to improve the reconstruction accuracy under sparse measurements, but also effectively tackle the misalignment and irregularity of the observation sequence through multi-channel and mask mechanisms. Second, to improve the accuracy of sequence reconstruction under large noise levels, a PRN is established to obtain reference features, which are weighted and fused with the features of observed data. Third, we use the NCDE to construct a decoder that can combine the control input of the system to predict the output values to solve the problem of sequence reconstruction in a controlled system. Finally, a weighted loss function is constructed to better train the network parameters of the model. This article takes the furnace of the boiler system in a coal-fired power plant as the test object to verify the effectiveness and fitting accuracy of the proposed PRN-NCDE model compared to the existing methods for a controlled system under sparse measurements and large noise levels. Simulation results show that the proposed PRN-NCDE model can improve the estimation accuracy by more than 50% and 70% compared with the recurrent neural network-NCDE (RNN-NCDE) under different sampling numbers and noise levels, and by more than 80% and 60% compared with the recurrent neural network-NODE (RNN-NODE). Full article

Designing routing algorithms for Low Earth Orbit (LEO) satellite networks poses a significant challenge due to their high dynamics, frequent link failures, and unevenly distributed traffic. Existing studies predominantly focus on shortest-path solutions, which compute minimum-delay paths using global topology information but often neglect the impact of traffic load on routing performance and struggle to adapt to rapid link-state variations. In this regard, we propose a Multi-Agent Reinforcement Learning-Based Joint Routing (MARL-JR) algorithm, which integrates centralized and distributed routing algorithms. MARL-JR combines the accuracy of centralized methods with the responsiveness of distributed approaches in handling dynamic disruptions. In MARL-JR, ground stations initialize Q-tables and upload them to satellites, reducing onboard computational overhead while enhancing routing performance. Compared to traditional centralized algorithms, MARL-JR achieves faster link-state awareness and adaptation; compared to distributed algorithms, it delivers superior initial performance due to optimized pre-training. Experimental results demonstrate that MARL-JR outperforms both Q-Routing (QR) and DR-BM algorithms in average delay, packet loss rate, and load-balancing efficiency. Full article

This study addresses the issue of nonfragile state estimation for memristor-based fractional-order neural networks with hybrid randomly occurring delays. Considering the finite bandwidth of the signal transmission channel, quantitative processing is introduced to reduce network burden and prevent signal blocking and packet loss. In a real-world setting, the designed estimator may experience potential gain variations. To address this issue, a fractional-order nonfragile estimator is developed by incorporating a logarithmic quantizer, which ultimately improves the reliability of the state estimator. In addition, by combining the generalized fractional-order Lyapunov direct method with novel Caputo–Wirtinger integral inequalities, a lower conservative criterion is derived to guarantee the asymptotic stability of the augmented system. At last, the accuracy and practicality of the desired estimation scheme are demonstrated through two simulation examples. Full article

The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions. Full article

In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing performance in dynamic environments, particularly when there is a sudden and rapid change in the nodes’ mobility. Therefore, this paper suggests an improved Greedy Perimeter Stateless Routing Protocol, namely AFB-GPSR, to reduce routing overhead and increase network reliability by maintaining correct route selection. To this end, an adaptive beaconing strategy based on a fuzzy logic scheme (AFB) is utilized to choose more optimal routes for data forwarding. Instead of constant periodic beaconing, the AFB strategy can dynamically adjust beacon interval time with the variation of three network parameters: node speed, one-hop neighbors’ density, and link quality of nodes. The routing evaluation of the proposed protocol is carried out using OMNeT++ simulation experiments. The results show that the AFB strategy within the GPSR protocol can effectively reduce the routing overhead and improve the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols (AODV and GPSR with fixed beaconing). An enhancement of the packet-delivery ratio of up to 14% is achieved, and the routing cost is reduced by 35%. Moreover, the AFB-GPSR protocol exhibits good performance versus the state-of-the-art protocols in MANET. Full article

Negative group delay may be observed in dispersive media with anomalous dispersion in a certain frequency range. The fact that an outgoing wave packet precedes an incoming one does not violate the causality principle but is only a consequence of a waveform reshaping. This effect is observed in media such as photonic crystals, hyperbolic and epsilon-near-zero metamaterials, undersized waveguides, subwavelength apertures, side-by-side prisms, and resonant circuits at various frequencies. The current work is devoted to the design of a simple negative-group-delay medium with tunable properties in the THz frequency range. This medium consists of a bismuth-based frequency-selective surface on a dielectric substrate and may be tuned both statically and dynamically. While a geometry variation defines a main form of an effective permittivity dispersion and group delay/group velocity spectra, an external voltage allows one to adjust them with high precision. For the configuration proposed in this work, all frequency regions with noticeable change in group delay/group velocity lie within atmospheric transparency windows, which are to be used in 6G communications. This medium may be applied to THz photonics for a tunable phase-shift compensation, dispersion management in systems of THz signal modulation, and for encoding in next-generation wireless communication systems. Full article

In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/∞ process able to exhibit a correlation structure decaying slowly in time. Given the independence among frames in frame-synchronous coded-slotted ALOHA (CSA), this variation should only take effect on frame-asynchronous CSA. On the other hand, we vary the marginal distribution of the arrival process using discrete versions of the Lognormal and Pareto distributions, with the objective of investigating the influence of the right tail. In this case, both techniques should be affected by the change, albeit to a different degree. Our results confirm these hypotheses and show that these factors must be taken into account when designing and analyzing these systems. In frameless operations, both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. Nevertheless, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance. Full article

The Vehicular Ad-hoc Network (VANET) is an innovative technology that allows vehicles to connect with neighboring roadside structures to deliver intelligent transportation applications. To deliver safe communication among vehicles, a reliable routing approach is required. Due to the excessive mobility and frequent variation in network topology, establishing a reliable routing for VANETs takes a lot of work. In VANETs, transmission links are extremely susceptible to interruption; as a result, the routing efficiency of these constantly evolving networks requires special attention. To promote reliable routing in VANETs, we propose a novel context-aware reliable routing protocol that integrates k-means clustering and support vector machine (SVM) in this paper. The k-means clustering divides the routes into two clusters named GOOD and BAD. The cluster with high mean square error (MSE) is labelled as BAD, and the cluster with low MSE is labelled as GOOD. After training the routing data with SVM, the performance of each route from source to target is improved in terms of Packet Delivery Ratio (PDR), throughput, and End to End Delay (E2E). The proposed protocol will achieve improved routing efficiency with these changes. Full article

The transmission of satellite payload data is critical for services provided by aerospace ground networks. To ensure the correctness of data transmission, the TCP data transmission protocol has been used typically. However, the standard TCP congestion control algorithm is incompatible with networks with a long time delay and a large bandwidth, resulting in low throughput and resource waste. This article compares recent studies on TCP-based acceleration algorithms and proposes an acceleration algorithm based on the learning of historical characteristics, such as end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment’s deep data inspection, and random packet loss caused by various factors. The proposed algorithm is evaluated and compared with the TCP congestion control algorithms under both laboratory and ground network conditions. Experimental results indicate that the proposed acceleration algorithm is efficient and can significantly increase throughput. Therefore, it has a promising application prospect in high-speed data transmission in aerospace-ground service networks. Full article

The Industrial Internet of Things (IIoT) has revolutionized businesses by changing the way data are used to make products and services more efficient, reliable, and profitable. To achieve the improvement goals, the IIoT must guarantee the real-time performance of industrial applications such as motion control, by providing stringent quality of service (QoS) assurances for their (industrial applications) communication networks. An application or service may malfunction without adequate network QoS, resulting in potential product failures. Since an acceptable end-to-end delay and low jitter or packet delay variation (PDV) are closely related to quality of service (QoS), their impact is significant in ensuring the real-time performance of industrial applications. Although a communication network topology ensures certain jitter levels, its real-life performance is affected by dynamic traffic due to the changing number of devices, services, and applications present in the communication network. Hence, it is essential to study the jitter experienced by real-time traffic in the presence of background traffic and how it can be maintained within the limits to ensure a certain level of QoS. This paper presents a probabilistic network calculus approach that uses moment-generating functions to analyze the delay and PDV incurred by the traffic flows of interest in a wired packet switched multi-stage network. The presented work derives closed-form, end-to-end, probabilistic performance bounds for delay and PDV for several servers in series in the presence of background traffic. The PDV analysis conducted with the help of a Markovian traffic model for background traffic showed that the parameters from the background traffic significantly impact PDV and that PDV can be maintained under the limits by controlling the shape of the background traffic. For the studied configurations, the model parameters can change the PDV bound from 1 ms to 100 ms. The results indicated the possibility of using the model parameters as a shaper of the background traffic. Thus, the analysis can be beneficial in providing QoS assurances for real-time applications. Full article

This work investigates adaptive random linear packet coding (RLPC) for reliable underwater acoustic (UWA) communications. Our goal is to minimize the total transmission time of data blocks by adjusting the packet coding rate. We first consider the application of RLPC with the conventional automatic repeat request (ARQ) scheme. We dynamically adjust the coding rate to fit the time variations of UWA channels by choosing the optimal number of packets in each transmission. The optimal number of packets in each transmission is obtained based on a dynamic programming (DP) algorithm according to the feedback messages, which contain the number of successfully transmitted packets in the last transmission and the channel state information. Furthermore, considering the long propagation delay of UWA communications, we propose a modified juggling-like ARQ (J-ARQ) for the RLPC scheme, for which the duration of each transmission can be adjusted based on the characteristics of RLPC. A two-step DP algorithm is proposed to find out the optimal solutions for this case. Simulation results show that the proposed schemes can improve the throughput efficiency and reduce the outage probability. Full article

The prominence of Machine-to-Machine (M2M) communications in the future wide area communication networks place various challenges to the cellular technologies such as the Long Term Evolution (LTE) standard, owing to the large number of M2M devices generating small bursts of infrequent data packets with a wide range of delay requirements. The channel structure and Quality of Service (QoS) framework of LTE networks fail to support M2M traffic with multiple burst sizes and QoS requirements while a bottleneck often arises from the limited control resources to communicate future uplink resource allocations to the M2M devices. Moreover, many of the M2M devices are battery-powered and require a low-power consuming wide area technology for wide-spread deployments. To alleviate these issues, in this article we propose an adaptive semi-persistent scheduling (SPS) scheme for the LTE uplink which caters for multi-service M2M traffic classes with variable burst sizes and delay tolerances. Instead of adhering to the rigid LTE QoS framework, the proposed algorithm supports variation of uplink allocation sizes based on queued data length yet does not require control signaling to inform those allocations to the respective devices. Both the eNodeB and the M2M devices can determine the precise uplink resource allocation related parameters based on their mutual knowledge, thus omitting the burden of regular control signaling exchanges. Based on a control parameter, the algorithm can offer different capacities and levels of QoS satisfaction to different traffic classes. We also introduce a pre-emptive feature by which the algorithm can prioritize new traffic with low delay tolerance over ongoing delay-tolerant traffic. We also build a model for incorporating the Discontinuous Reception (DRX) mechanism in synchronization with the adaptive SPS transmissions so that the UE power consumption can be significantly lowered, thereby extending their battery lives. The simulation and performance analysis of the proposed scheme shows significant improvement over the traditional LTE scheduler in terms of QoS satisfaction, channel utilization and low power requirements of multi-service M2M traffic. Full article

Traffic management challenges in peak seasons for popular destinations such as Madinah city have accelerated the need for and introduction of autonomous vehicles and Vehicular ad hoc networks (VANETs) to assist in communication and alleviation of traffic congestions. The primary goal of this study is to evaluate the performance of communication routing protocols in VANETs between autonomous and human-driven vehicles in Madinah city in varying traffic conditions. A simulation of assorted traffic distributions and densities were modeled in an extracted map of Madinah city and then tested in two application scenarios with three ad hoc routing protocols using a combination of traffic and network simulation tools working in tandem. The results measured for the average trip time show that opting for a fully autonomous vehicle scenario reduces the trip time of vehicles by approximately 7.1% in high traffic densities and that the reactive ad hoc routing protocols induce the least delay for network packets to reach neighboring VANET vehicles. From these observations, it can be asserted that autonomous vehicles provide a significant reduction in travel time and that either of the two reactive ad hoc routing protocols could be implemented for the VANET implementation in Madinah city. Furthermore, we perform an ANOVA test to examine the effects of the factors that are considered in our study on the variation of the results. Full article