Avoid Network Congestion Research Articles

A low-storage synchronization framework for blockchain systems

The advent of blockchain technology has brought major changes to traditional centralized storage. Therefore, various fields have begun to study the application and development of blockchain. However, blockchain technology has a serious shortcoming of data bloating. The reason is that blockchain technology achieves decentralization by storing complete blockchain data at each node, incurring a significant amount of blockchain data. Therefore, each node must spend significant amount of storage space and initialization synchronization time. To solve the above problems, in this research, we propose a secure and agile synchronization framework for low storage blockchains. First, we design a K-extreme segment algorithm, which reduces the synchronization time by returning only the first and last k blocks of each block segment at once to the local storage. Next, we decentrally store the block data of the blockchain by IPFS and establish a backup mechanism by IPFS-cluster. Finally, due to use of distributed storage, the nodes must request un-stored block data from IPFS, causing an increase in the throughput of the blockchain network. To avoid network congestion, we propose the working set algorithm to improve the hit ratio of the local storage and reduce the number of requests to decrease throughput. In summary, our experiments demonstrate that the ratio of full nodes to low storage nodes is significantly lower for nodes with higher storage limits compared to those with lower storage limits. In other words, a higher storage limit results in more low storage nodes which can be permitted to ensure that the blockchain network is robust and reliable. Therefore, our proposed framework can provide reliable low storage nodes for the blockchain. The node can reduce the local storage pressure and can still maintain the full functionality of blockchains.

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

Wireless Power Transfer System in Electric Vehicle Application

Abstract: As an alternate form in the road transportation system, electric vehicle (EV) can help reduce the fossil-fuel consumption. However, the usage of EVs is constrained by the limited capacity of battery. Wireless Power Transfer (WPT) can increase the driving range of EVs by charging EVs in motion when they drive through a wireless charging lane embedded in a road. The amount of power that can be supplied by a charging lane at a time is limited. A problem here is when a large number of EVs pass a charging lane, how to efficiently distribute the power among different penetrations levels of EVs? However, there has been no previous research devoted to tackling this challenge. To handle this challenge, we propose a system to balance the State of Charge (called BSoC) among the EVs. It consists of three components: i) fog-based power distribution architecture, ii) power scheduling model, and iii) efficient vehicle-to-fog communication protocol. The fog computing center collects information from EVs and schedules the power distribution. We use fog closer to vehicles rather than cloud in order to reduce the communication latency. The power scheduling model schedules the power allocated to each EV. In order to avoid network congestion between EVs and the fog, we let vehicles choose their own communication channel to communicate with local controllers.

Novel AMI in Zigbee Satellite Network Based on Heterogeneous Wireless Sensor Network for Global Machine-to-Machine Connectivity

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project’s scheduling aligns with the enterprise’s sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses.

Assessment of QOS indicators of a network with UDP and TCP traffic under a node peak load mode

The object of research is Markov models of network nodes with UDP (User Datagram Protocol) and TCP (Transmission Control Protocol) traffic and their differences. The task solved is the lack of Markov models of network nodes describing the behavior of TCP traffic from the point of view of packet retransmissions and packet delivery guarantees. Markov models of network nodes describing traffic behavior with guaranteed packet delivery have been further advanced. Given the comparison of the models, the differences from the classic models serving TCP traffic were shown, for each packet flow, an additional dimensionally was added to the graph of states and transitions, which takes into account the retransmission of a lost packet. The comparison graph shows similar behavior of queue length and packet loss for both types of traffic. But the nature of the curves is different. With TCP traffic, packet loss can exceed 5 percent. In addition, lost packets must be retransmitted, which increases the load on the network node. More failures and packet queue lengths at a network node during peak load typically occur with TCP traffic compared to UDP traffic. At peak load, the difference in service failures can reach 20–30 percent. The main reason is that TCP uses flow control and rate-limiting mechanisms to avoid network congestion and ensure efficient data transfer between nodes. The Markov model of TCP traffic requires an additional dimensionally on the graph of states and transitions, which affects the behavior of queues and packet failures. The investigated problem was solved due to the universality and diversity of the mathematical apparatus of Markov mass service systems. The results could be used in network modeling software products for building and reengineering the topology of electronic communications networks at enterprises and organizations

Load-balancing routing algorithms for service congestion avoidance in LEO optical satellite networks

The low-Earth-orbit optical satellite network (LOSN) is an attractive solution to realize the end-to-end service quality of large-scale services, and it is the basis of establishing the 6G mobile network with high throughput and high reliability. However, the current LOSN is practically unable to achieve such high throughput for global service access. First, the centralized deployment of ground gateways will cause a heavy traffic load in the space segment of the LOSN, which becomes the bottleneck constraining the further growth of network throughput. Second, the high-speed movement of LEO satellites will cause continuous movement of traffic load, which will affect the scalability of routing computing. In this paper, two routing algorithms are proposed to increase the throughput in the dynamic LOSN topology. The congestion-aware load balancing (CALB) algorithm is proposed to address the inter-satellite link congestion in the dynamic LOSN topology. Then, a load-balancing routing algorithm based on satellite–ground cooperation (SGC-LB) is proposed to further reduce the impact of the network bottleneck. To evaluate the performance of the proposed routing algorithm, extensive simulations were performed on a 288-satellite Walker-Star constellation with inter-satellite links. The service blockage rate and network bandwidth utilization rate are evaluated showing the effectiveness of the proposed routing algorithm. The network using the SGC-LB algorithm can accommodate 60.00%, 56.00%, 42.22%, and 33.33% more services than using the Shortest Path algorithm, Sway algorithm, CALB algorithm, and Anycast algorithm, respectively, with zero service congestion during the simulation. The SGC-LB also gains a 6.04%, 5.00%, 5.04%, and 0.77% higher network utilization rate than the Shortest Path, Sway, CALB, and Anycast algorithms, respectively.

Output-Based Dynamic Periodic Event-Triggered Control with Application to the Tunnel Diode System

The integration of communication channels with the feedback loop in a networked control system (NCS) is attractive for many applications. A major challenge in the NCS is to reduce transmissions over the network between the sensors, the controller, and the actuators to avoid network congestion. An efficient approach to achieving this goal is the event-triggered implementation where the control actions are only updated when necessary from stability/performance perspectives. In particular, periodic event-triggered control (PETC) has garnered recent attention because of its practical implementation advantages. This paper focuses on the design of stabilizing PETC for linear time-invariant systems. It is assumed that the plant state is partially known; the feedback signal is sent to the controller at discrete-time instants via a digital channel; and an event-triggered controller is synthesized, solely based on the available plant measurement. The constructed event-triggering law is novel and only verified at periodic time instants; it is more adapted to practical implementations. The proposed approach ensures a global asymptotic stability property for the closed-loop system under mild conditions. The overall model is developed as a hybrid dynamical system to truly describe the mixed continuous-time and discrete-time dynamics. The stability is studied using appropriate Lyapunov functions. The efficiency of the technique is illustrated in the dynamic model of the tunnel diode system.

Boosting Erasure-Coded Multi-Stripe Repair in Rack Architecture and Heterogeneous Clusters: Design and Analysis

Large-scale distributed storage systems have introduced erasure codes to guarantee high data reliability, yet inevitably at the expense of high repair costs. In practice, storage nodes are usually divided into different racks, and data blocks in storage nodes are often organized into multiple stripes independently manipulated by erasure code. Due to the scarcity and heterogeneity of the cross-rack bandwidth, the cross-rack network transmission dominates the entire repair costs. We argue that when erasure code is deployed in a rack architecture, existing repair techniques are limited in different aspects: neglecting the heterogeneous cross-rack bandwidth, less consideration for multi-stripe failure, and no special treatment on repair link scheduling. In this paper, we present CMRepair, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cross-rack Multi-stripe Repair</i> technique that aims to reduce the repair time for multi-stripes failure repair in heterogeneous erasure-coded clusters. CMRepair first carefully chooses the nodes for reading/repairing blocks and searches for the multi-stripe repair solution. It adopts different algorithms to adjust per stripe solution, including the Computation Time Priority (CTP) algorithm based on the greedy idea and the Repair Time Priority (RTP) algorithm based on the meta-heuristics idea. Furthermore, CMRepair selectively schedules the execution orders of cross-rack links, with the primary objective of saturating the unused upload/download bandwidth resources and avoiding network congestion. The experiments show that CMRepair with the CTP algorithm can reduce 27.59%-58.12% of the repair time while only introducing negligible computation overhead, and CMRepair with the RTP algorithm can reduce 33.52%-97.75% of the repair time in an acceptable computation time, over existing repair techniques.

A novel fault tolerance based load balancing technique in cloud computing

Cloud computing has emerged as one of the most promising technologies for meeting rising computing needs. However, high-performance computing systems are more likely to fail due to the proliferation of components and servers. If a sub-system fails, the entire system may not be functional. In this regard, the occurrence of faults is tolerable using an efficient fault-tolerant method. Since cloud computing involves storing data on a remote network, system failures and congestion are the most common causes of faults. The paper presents a new approach to adopting a fault-tolerant mechanism that adaptively monitors health to detect faults, handles faults using a migration technique, and avoids network congestion. With the advantage of the Ant Colony Optimization (ACO) algorithm and active clustering, the load is distributed evenly in data centers. Simulation results indicate that our algorithm outperforms previous algorithms regarding total execution time and imbalance degree up to 10% and 17%, respectively.

ACC-RL: Adaptive Congestion Control Based on Reinforcement Learning in Power Distribution Networks with Data Centers

Modern data center power distribution networks place greater demands on the stability and reliability of power supply. Growing network computing demands and complex network environments can cause network congestion, which in turn leads to network traffic overload and power supply equipment overload. Therefore, network congestion is one of the most important problems faced by data center power distribution networks. In this paper, we propose an approach called ACC-RL based on reinforcement learning (RL), which can effectively avoid network congestion and improve energy performance. ACC-RL models the congestion control task as a Partially Observable Markov Decision Process (POMDP). It is independent of the estimated value function and supports deterministic policies. It also sets the reward value function using real-time network information such as the transmission rate, RTT, and switch queue length, with the target transmission rate as the target equilibrium point. ACC-RL is highly general, can be trained on datasets running in different network environments, and generates a robust congestion control policy. The experimental results show that ACC-RL can solve the congestion problem without any predefined scenarios in different network environments. It can control the network traffic well, thus ensuring the stability and reliability of the power supply in the distribution network. We conduct network simulation experiments through NS-3. We set up different scenarios for experiments and data analysis in many-to-one, all-to-all, and long–short network environments. Compared with the popular rule-based congestion control algorithms such as TIMELY, DCQCN, and HPCC, ACC-RL shows different degrees of energy performance advantages in network metrics such as fairness, link utilization, and throughput.

Congestion Avoidance in Intelligent Transport Networks Based on WSN-IoT through Controlling Data Rate of Zigbee Protocol by Learning Automata

Congestion control is one of the primary challenges in improving the performance of wireless sensor networks (WSNs). With the development of this network based on the Internet of Things (IoT), the importance of congestion control increases, and the need to provide more efficient strategies to deal with this problem is strongly felt. This problem is even more important in applications such as Intelligent Transport Systems (ITSs). This article introduces a new method for congestion control in ITSs based on WSN-IoT infrastructure, namely, the Congestion Avoidance by Reinforcement Learning algorithm (CARLA). The purpose of the research was to improve the performance of the Zigbee protocol in congestion control through more efficient routing and also the intelligent adjustment of the data rate of the nodes. For this purpose, a topology control and routing strategy based on the multiple Bloom filter (MBF) is proposed in this research. Further, learning automata (LA) was used as a reinforcement learning model to adjust the data rate of network nodes in a distributed manner. These strategies distinguish the current research from previous efforts and can be effective in reducing the probability of congestion in the network. The performance evaluation results of the proposed algorithm in a simulated ITS environment were compared with conventional Zigbee and state of the art methods. According to the results, CARLA can improve PDR by 4.64%, and at the same time, reduce energy consumption and end-to-end delay by 11.44% and 25.26%, respectively. The results confirm that by using CARLA, in addition to congestion control in the ITS, energy consumption and the end-to-end delay can also be reduced.

High Minimum Inter-Execution Time Sigmoid Event-Triggered Control for Spacecraft Attitude Tracking With Actuator Saturation

This paper investigates the attitude tracking problem for spacecraft with limited communication, network congestion, unknown model parameters, actuator saturation, and external disturbances. To alleviate communication load, a novel sigmoid event-triggered mechanism is proposed with the special ability to guarantee a high minimum inter-execution time to avoid network congestion like package loss or time delay effectively. A neural network-based adaptive control algorithm is designed to deal with unknown model parameters. Besides, the problem of actuator saturation is tackled by introducing a dynamic loop gain function-based approach. System stability is proved by Lyapunov stability analysis and the high minimum inter-event time is substantiated by Zeno Behavior analysis with explanatory remarks. Numerical simulation results also show that a high minimum inter-event time and a high average inter-event time can be realized on the premise of high attitude tracking accuracy. Compared with all the previous studies, the ratio of the minimum inter-execution time to the average minimum inter-execution time has been improved by nearly 10 times with the proposed approach. Note to Practitioners—This paper was motivated by the problem of spacecraft attitude takeover control, but it also applies to other aperiodic discrete-time control systems. Existing event-triggered control methods have solved the problem of limited communication for attitude takeover control of spacecraft by reducing the number of times wireless communication is required. This paper proposes a new method for realizing quasi-periodic control with a large time gap between any two consistent control impulses using a hyperbolic tangent function or another sigmoid function in an event-triggered mechanism. The proposed sigmoid event-triggered mechanism (ETM) can magnify the event-triggered threshold dozens of times in a short time to avoid unnecessary frequent triggering without decreasing the precision of control. It’s feasible to employ the proposed sigmoid ETM to deal with limited communication and network congestion in other network control systems that are constructed with similar dynamic structures. Furthermore, it would be marvelous if we could transform this sigmoid ETM into a high-efficiency decision-making mechanism in some control systems without using a wireless network. Now we are studying how to realize orbital-attitude (maneuver) control with a relatively average time interval and fewer times of orbit transfer by using the proposed sigmoid ETM. Besides, to further improve the system performance, we are also seeking to further increase the ratio of the minimum inter-execution time to the average inter-execution time. For the purpose of a more even distribution of the triggering events on the time axis, we may modify the proposed sigmoid event-triggered method or associate it with other methods.

ANALYSIS AND FEATURES OF TRAFFIC MONITORING SOFTWARE

The article discusses the main capabilities of traffic control programs. Sending too many Internet connection packets can result in heavy Internet traffic, which can lead to bottlenecks or network congestion. Meanwhile, operations between communication devices are also affected and slowed down. The main problem is that the growth of Internet traffic increases dramatically, and on the other hand, the Internet connection always has a finite bandwidth for data flow and can only support a certain number of packets at a time. Sending too many packets at once over an Internet connection is difficult because sending too many packets at once can lead to heavy Internet traffic that can become a bottleneck or cause network congestion. Meanwhile, transactions between communication devices are also affected and slowed down, which ultimately leads to Internet congestion and network service degradation. Effective congestion management is one of the most important issues in managing Internet traffic. The new algorithmic congestion prevention model will improve performance and network utilization by making TCP a more flexible and efficient protocol with a new preemptive approach. Traffic control programs are the primary application of a computer engineer. The entire world is interconnected by a vast network of routers, switches, servers, and consumers, and so many devices and packets need to be controlled. That is why the basic principle of traffic control was created. Thanks to such programs, there is a stable traffic and network. The future of today’s Internet is the adaptation of cloud computing, 3D technologies, the Internet of Things, global communication, distributed computing, wireless media, and high-definition multimedia broadcasting over the Internet. To manage sustainable and efficient Internet traffic, a systematic approach to planning, testing, and maintaining its behavioral mechanism is desirable to avoid network congestion and ensure uninterrupted data transmission. Effective congestion management is one of the most important problems of internet traffic management efficiency that needs to be solved. The main way to solve the problem is to use traffic control programs. Effective means that the control mechanism must also ensure the proper level of use of network resources, which is a fundamental condition for ensuring the profitability of work. It was found that the reason for the use of traffic control is the increase in the use of Internet resources – therefore, the use of programs is necessary for successful traffic control.

Preface

The 2nd International Conference on Smart Grid and Energy Internet (SGEI 2022) was successfully held on 21st-23rd October, 2022 in Sanya, China (virtual conference). SGEI 2022 promoted innovative technologies and practical applications in the grid and energy fields, aiming to fully realize the interconnection, openness, sharing and synergy of various energy resources, and to shape a green, low-carbon, efficient and low-cost smart energy system.We had the honor of having invited Prof. Jizhong Zhu, President of IEEE PES SBLC (China), to serve as our Conference General Chair. The conference was composed of keynote speeches, oral presentations, and online Q&A discussion, attracting 100 individuals. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the next part, oral presentations, the excellent papers selected were presented by their authors in sequence.During the conference, three distinguished professors were invited to address their keynote speeches. Among them, Prof. Behnam Mohammadi-Ivatloo from University of Tabriz, Iran addressed a thought-provoking speech on Flexibility Resources for Integrating more Renewable Energy Resources in Power Systems. In the speech, different challenges of integrating renewable energy resources were discussed and different flexibility options were presented for hosting a higher share of renewable power resources in the power systems. And then, Prof. Amjad Anvari-Moghaddam from Aalborg University, Denmark made a speech on Operation and Control of Renewable Energy Systems. To accommodate large numbers of renewable resources, energy distribution and transmission networks need to be adapted and expanded to avoid network congestion and failures. This report covered the aforementioned promising areas in green energy transition and discussed the current and future opportunities and challenges exist in this context. All the sophisticated keynote speakers combined their own interest fields with the conference theme to make dramatic speeches, triggering heated discussion in the conference. And every participant praised this conference for disseminating useful and insightful knowledge.List of Committee member is available in this pdf.

Peer-to-peer energy trading in energy local area network considering decentralized energy routing

Peer-to-peer (P2P) energy trading is perceived as a promising energy management mechanism for decentralized renewable energy sources. The concept of energy local area network (e-LAN) is proposed as a new system framework for future microgrids to facilitate P2P trading where the end-to-end power delivery is achieved by energy routing. Decentralized P2P trading in e-LAN requires a perfectly competitive market and flexible end-to-end power delivery. In this paper, a new P2P trading model incorporating decentralized energy routing is proposed for e-LAN. The proposed model is composed of a P2P energy market and a loss compensation market. In the P2P energy market, a tripartite graph model is proposed to represent transactions where trading decisions and energy routing are jointly considered for each participant to pursue its optimal benefit. Considering the size problem and nonlinear weights of the tripartite graph, an extended depth-first branch-and-bound algorithm is proposed to prune low-efficiency paths. Based on the pruned tripartite graph, the competitive P2P market is captured by a multi-leader multi-follower Stackelberg game model. A loss compensation market is established to coordinate energy routing paths planned in a decentralized manner. By means of setting the price for utilizing each transmission line, the loss compensation market avoids network congestion and ensure a normal operation of e-LAN. Simulation results show that the proposed model can bring higher utilities for P2P participants compared with the existing P2P trading models in e-LAN. Additionally, the effectiveness of the proposed model in resolving path conflicts in a decentralized fashion and improving computation efficiency, are verified.

Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

A Survey of Traffic Shaping Technology in Internet of Things

In the era of the Internet of Everything, the burst of traffic will bring many problems. Traffic shaping, as a common means to limit burst traffic rate, achieves “peak and valley reduction” to smooth the output rate, avoid network congestion, guarantee the quality of service (QoS) and improve the overall network transmission efficiency. This paper first explains the basic concept of traffic shaping, the related algorithms in traffic shaping, and compares the differences between traffic shaping and traffic policing. It then describes the research on traffic shaping in software-defined networks and the results from the combination of the two. This is followed by an introduction to relatively new technology for industry, time-sensitive networks, and an exploration of the functions of various types of traffic shapers in time-sensitive networks. This is followed by a description of case studies of traffic shaping in IoT scenarios and an overview of these traffic shaping schemes, summarizing the usefulness of the shaping schemes. Finally, the future of traffic shaping is explored.

Adaptive neural quantized control for fractional-order full-state constrained non-strict feedback systems subject to input fault and nonlinearity

In this work, an adaptive neural network (NN)-based quantized fault-tolerant controller (FTC) has been designed for incommensurate fractional-order (FO) nonlinear systems (NSs) in the non-strict feedback form subject to independent asymmetric time-varying full-state constraints, input quantization, unknown dynamics, infinite number of actuator faults and unknown input nonlinearities. It is assumed that the controller communicates with the actuator via a network with limited bandwidth. To avoid network congestion, the control signal is first quantized by an asymmetric hysteresis quantizer (HQ), then transmitted to the actuator via the network. By incorporating the backstepping algorithm, barrier Lyapunov functions (BLFs) and fractional direct Lyapunov stability (FDLS) theorem, a novel method has been proposed to design a control scheme for FO state constrained NSs and establish the closed-loop stability. In this method, the Lyapunov function of each step of the backstepping algorithm is chosen based on the BLF and parameter estimation error of the considered step and their boundedness is shown from the last step to the first one. Under the proposed control approach, the full-state constraints are met, the tracking error can be made small by selecting proper design parameters and all closed-loop signals remain bounded. Finally, three simulation examples have been provided to demonstrate the designed controller effectiveness.

Traffic Load Balancing based on Congestion Avoidance (TLBCA) in high-speed computer networks

Congestion, and how to manage it or prevent its occurrence, is one of the most crucial and cutting-edge subjects in the world of networks since it has a significant influence on the computer network and the quality of service (QoS). Network efficiency is decreased by congestion, which frequently results in service interruptions. In order to preserve the continuity of data flow in the network, it is necessary to design approaches and processes to avoid congestion or to minimize its effects. Congestion are avoided at two levels, knot and link, by using different techniques, including close loop or open loop. This paper proposed a new technique (TLBCA) to distribute packets arriving at a specific node on the links connected to it, which leads to the same destination, to avoid congestion on one of the links and out of service, which then leads to more load on the other links until the network collapses. TLBCA is based on the popular algorithm Round Robin. ECMP and TinyFlow, the two most widely used comparable algorithms in this sector, performed around 20% worse than the approach when it was simulated using OMNET++.

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.