Network Control Mechanisms Research Articles

Explore the Evolution of Computer Network Architecture

Since the invention of computer networks in the 1960s, their architecture has undergone many major innovations and evolutions. This review aims to sort out and analyze this evolution process and reveal its internal motivations and fundamental driving forces. We will analyze the development of network architecture from multiple dimensions such as network topology, protocol stack, and control mechanism, and reveal the internal logic of the architecture evolving with the times.First, the paper reviews the origins of computer network development, explains the original packet switching ideas and ARPANET architecture, and analyzes its influence on later generations. Then it focuses on the birth of the TCP/IP protocol family and its architecture, analyzing its design concepts and innovations. Subsequently, the paper will explore the rise of the Internet and the formation of its layered architecture model, commenting on the advantages and disadvantages of this model. We will also examine the ideas and attempts of the next generation Internet architecture, such as active networks and content-centric networks, and analyze their challenges and limitations.In addition, the paper will explore the new changes brought about by emerging network paradigms such as software-defined networking (SDN) and network function virtualization (NFV) to network architecture, and comment on their advantages and impacts. We will also focus on the driving role of emerging paradigms such as cloud computing and edge computing on network architecture. Finally, the paper will look forward to new trends and challenges in the development of future network architecture, such as the potential application prospects of cutting-edge technologies such as artificial intelligence, machine learning, and blockchain in network architecture. By comprehensively analyzing and summarizing the evolution of network architecture, the paper hopes to provide readers with a macro perspective and a deep understanding of the internal driving force and development logic of network architecture innovation. This will not only help us understand the past, but also provide inspiration and ideas for future network architecture innovation.

Edge-centric network control on the human brain structural network

Abstract Network control theory models how gray matter regions transition between cognitive states through associated white matter connections, where controllability quantifies the contribution of each region to driving these state transitions. Current applications predominantly adopt node-centric views and overlook the potential contribution of brain network connections. To bridge this gap, we use edge-centric network control theory (E-NCT) to assess the role of brain connectivity (i.e., edges) in governing brain dynamic processes. We applied this framework to diffusion MRI data from individuals in the Human Connectome Project. We first validate edge controllability through comparisons against null models, node controllability, and structural and functional connectomes. Notably, edge controllability predicted individual differences in phenotypic information. Using E-NCT, we estimate the brain’s energy consumption for activating specific networks. Our results reveal that the activation of a complex, whole-brain network predicting executive function (EF) is more energy efficient than the corresponding canonical network pairs. Overall, E-NCT provides an edge-centric perspective on the brain’s network control mechanism. It captures control energy patterns and brain-behavior phenotypes with a more comprehensive understanding of brain dynamics.

Analysis of Artificial Intelligence Based MPPT in PV Grid Connected System

The There has been a rise in the demand for electrical power during the past 10 years. Installing a new power generator (PG) is a costly and time-consuming procedure. For this reason, solar power plants are considered a viable alternative for meeting the current energy demand. But crucial maintenance and output power balance are the key issues facing solar power facilities. In order to reduce output power balance and maintenance problems in solar plants, an appropriate approach is required. This study offers a novel method for tracking the maximum power for hybrid photovoltaic (PV) and wind energy systems (WES): single maximum power point tracking, or MPPT. In the proposed MPPT technique, a radial basis function network (RBFN) control mechanism is based on an artificial neural network (ANN).

IoT based security and privacy implementation in smart home

IoT based security and privacy implementation in smart home

Unpacking drug trafficking phenomenon through seaports: lessons from the Italian ports

ABSTRACT This article aims to provide an analysis of the factors influencing the operational strategies of drug trafficking organisations in the context of seaports. It seeks to move beyond a monolithic representation of this complex phenomenon by investigating the intricate interplay between legal and illicit spheres, as well as the diverse methodologies employed in the illicit transportation of drugs from vessels to destinations outside of ports. Drawing from empirical evidence gathered in the Italian context, the results reveal that drug trafficking through seaports is a multifaceted endeavour influenced by a combination of endogenous and exogenous factors. Internal factors within the network, such as organisational structure (including network size, control mechanisms, and member accountability) and resources (human, material, and informational), play a pivotal role in shaping drug trafficking organisations’ strategies. Additionally, external factors, including maritime dynamics, port economics, cultural aspects, and institutional determinants, significantly impact decision-making processes within these criminal organisations.

EagerCC: An ultra-low latency congestion control mechanism in datacenter networks

With the rapid development of cloud applications, the workload pattern of datacenters presents the characteristics of mixed long and short flows and frequent micro-burst traffic, which puts forward new requirements for network transmission performance, including ultra-low latency, high throughput, and strong stability. At the same time, datacenters begin to deploy the low-diameter topology to accommodate these new requirements, and the high-performance datacenter (HPDC) comes into being. However, due to the complexity of the load, existing congestion control mechanisms cannot control dynamic delay in the network well, which significantly restricts the development of the HPDC. Therefore, it is necessary to deploy the congestion control mechanism for the HPDC. So we propose EagerCC, an ultra-low latency, low-overhead, and accurate congestion control mechanism based on In-Network-Telemetry (INT) information for various datacenter scenarios, especially for HPDCs. EagerCC uses switch-feedback, ACK-padding, ACK-first to reduce feedback delay and uses switch-calculation, probabilistic ACK-padding to reduce the overhead of congestion signals. We conduct a lot of experiments and the result shows that EagerCC performs well in various datacenter scenarios, especially for HPDCs. Specifically, EagerCC reduces the 99th-FCT and avg-FCT by 52.3% and 13.3% for the HPC workload NPB-CG compared to HPCC in Dragonfly. In addition, EagerCC significantly reduces the network’s feedback delay and queue occupancy.

Salience-based VNF placement method for evolving multiple network slices

Network Function Virtualization (NFV) technology enables the deployment of a distinct network slice composed of various virtualized network functions (VNFs) required for offering a communication service. As the user population or network traffic volume fluctuates, the capacity of the network slices must be dynamically adjusted. It is necessary to decide whether to place VNFs in appropriate locations such that the quality of services (QoS) is guaranteed while optimally utilizing the allocated resources. The above challenge can be addressed by solving an optimization problem, such as integer linear programming. However, as solving the optimization problem requires a considerable amount of time, it is not applicable in agile network control mechanisms. In this paper, we propose a network function placement algorithm based on salience, which is one of the characteristic metrics of links in a graph. Our proposal is aimed to provide the adequate placement in short time to achieve the following objectives of keeping the slice reconstruction cost low and avoiding the cases of QoS degradation. The simulation results show that our scheme performs equally well for different sized slices (i.e., the user population). We found that the network reconfiguration costs are low and almost the same for various sized slices when they are reconstructed by the proposed method in a substrate network of 200 nodes. Furthermore, we confirmed that the proposed method keeps the slice reconstruction cost low and avoids blocking of slice reconstruction even in the situations when a resource competition occurs among multiple slices.

CO-CAC: A new approach to Call Admission Control for VoIP in 5G/WiFi UAV-based relay networks

Voice over IP (VoIP) requires a Call Admission Control (CAC) mechanism in WiFi networks to preserve VoIP packet flows from excessive network delay or packet loss. Ideally, this mechanism should be integrated with the operational scenario, guarantee the quality of service of active calls, and maximize the number of concurrent calls. This paper presents a novel CAC scheme for VoIP in the context of a WiFi access network deployed with Unmanned Aerial Vehicles (UAVs) that relay to a backhaul 5G network. Our system, named Codec-Optimization CAC (CO-CAC), is integrated into each drone. It intercepts VoIP call control messages and decides on the admission of every new call based on a prediction of the WiFi network’s congestion level and the minimum quality of service desired for VoIP calls. To maximize the number of concurrent calls, CO-CAC proactively optimizes the codec settings of active calls by exchanging signaling with VoIP users.We have simulated CO-CAC in a 50m × 50m scenario with four UAVs providing VoIP service to up to 200 ground users with IEEE 802.11ac WiFi terminals. Our results show that without CAC, the number of calls that did not meet a minimum quality level during the simulation was 10% and 90%, for 50 and 200 users, respectively. However, when CO-CAC was in place, all calls achieved minimum quality for up to 90 users without rejecting any call. For 200 users, only 25% of call attempts were rejected by the admission control scheme. These results were narrowly worse when the ground users moved randomly in the scenario.

A Comprehensive Analysis on Numerous Learning Models for Intrusion Detection for Security Conservation

The term intrusion refers to a series of behaviours that exposes computer networks and systems' security to compromises. Corrective action on the network cannot go on without intrusion detection. IDS and IDS is the framework used to detect network traffic intrusions, which is how the network control mechanism identifies potential intrusions. Security breaches are designed to undermine one or more of the network's three primary security goals: privacy, availability, and trust. To get access to a system, an attacker must follow a predetermined set of procedures. Once inside, they can begin gathering data such as the protocol being used and the network resources available. There are many ways for a hacker to find out what systems are available on the network and how vulnerable they are to attacks. The rapid advancement of network technology necessitated IDS to focus on the detection of assaults using contextual analysis from signature matching processes. Using machine learning to detect and prevent intrusions, the IDS is a critical part of protecting data systems. Network intrusion detection is the focus of this paper, which examines and shows various machine learning techniques.

The relationship of alpha-synuclein to mitochondrial dynamics and quality control.

Maintenance of mitochondrial health is essential for neuronal survival and relies upon dynamic changes in the mitochondrial network and effective mitochondrial quality control mechanisms including the mitochondrial-derived vesicle pathway and mitophagy. Mitochondrial dysfunction has been implicated in driving the pathology of several neurodegenerative diseases, including Parkinson’s disease (PD) where dopaminergic neurons in the substantia nigra are selectively degenerated. In addition, many genes with PD-associated mutations have defined functions in organelle quality control, indicating that dysregulation in mitochondrial quality control may represent a key element of pathology. The most well-characterized aspect of PD pathology relates to alpha-synuclein; an aggregation-prone protein that forms intracellular Lewy-body inclusions. Details of how alpha-synuclein exerts its toxicity in PD is not completely known, however, dysfunctional mitochondria have been observed in both PD patients and models of alpha-synuclein pathology. Accordingly, an association between alpha-synuclein and mitochondrial function has been established. This relates to alpha-synuclein’s role in mitochondrial transport, dynamics, and quality control. Despite these relationships, there is limited research defining the direct mechanisms linking alpha-synuclein to mitochondrial dynamics and quality control. In this review, we will discuss the current literature addressing this association and provide insight into the proposed mechanisms promoting these functional relationships. We will also consider some of the alternative mechanisms linking alpha-synuclein with mitochondrial dynamics and speculate what the relationship between alpha-synuclein and mitochondria might mean both physiologically and in relation to PD.

Finding an appropriate radio propagation model for rate aware congestion control mechanism in wireless sensor networks

Finding an appropriate radio propagation model for rate aware congestion control mechanism in wireless sensor networks

A Preamble-Based MAC Mechanism in Ad-Hoc Network

In this paper, we propose a preamble-based medium access control (P-MAC) mechanism in Ad-Hoc network. Different from traditional carrier sense multiple access (CSMA) in Ad-Hoc network, P-MAC uses a much shorter preamble to establish the network. First, we propose the P-MAC mechanism to shorten the time of establishing the Ad-Hoc network. Based on the P-MAC, we propose a more efficient way to maintain the network. Next, focusing on the power line communication (PLC) network which is a kind of Ad-Hoc network, we propose a frequency division power line communication (FD-PLC) network architecture to obtain the best communication frequency. To obtain the best frequency, i.e., highest signal-to-noise ratio (SNR), we design the frequency sweeping mechanism which can determine the frequency of uplink and downlink communication before the transmitter and receiver communicate. Due to the large-scale networks in industry, P-MAC can be exploited to speed up the establishment of the Ad-Hoc PLC network. Finally, we compare our mechanism with CSMA. Numerical results indicate that our strategy greatly shortens the time of establishing the Ad-Hoc network.

Dynamics of the Rumor‐Spreading Model with Control Mechanism in Complex Network

The spread of rumors has a great impact on social order, people’s psychology, and life. In recent years, the application of rumor‐spreading models in complex networks has received extensive attention. Taking the management and control of rumors by relevant departments in real life into account, the SIDRQ rumor‐spreading model that combines forgetting mechanism, immune mechanism, and suspicion mechanism and guides on a uniform network is established in this paper. Then, the basic reproductive number of the system and the unique existence of the solution are discussed, and the stability of the system is analyzed using the basic reproductive number, Lyapunov function, and Lienard and Chipart theorem; furthermore, the basic reproductive number may not be able to deduce the stability of the system and a counterexample is given. Finally, the influence of different parameters on the spread of rumors is studied, and the validity of the theoretical results is verified.

Research on extended access control mechanism in online social network

Research on extended access control mechanism in online social network

A Brain-Inspired Homeostatic Neuron Based on Phase-Change Memories for Efficient Neuromorphic Computing.

One of the main goals of neuromorphic computing is the implementation and design of systems capable of dynamic evolution with respect to their own experience. In biology, synaptic scaling is the homeostatic mechanism which controls the frequency of neural spikes within stable boundaries for improved learning activity. To introduce such control mechanism in a hardware spiking neural network (SNN), we present here a novel artificial neuron based on phase change memory (PCM) devices capable of internal regulation via homeostatic and plastic phenomena. We experimentally show that this mechanism increases the robustness of the system thus optimizing the multi-pattern learning under spike-timing-dependent plasticity (STDP). It also improves the continual learning capability of hybrid supervised-unsupervised convolutional neural networks (CNNs), in terms of both resilience and accuracy. Furthermore, the use of neurons capable of self-regulating their fire responsivity as a function of the PCM internal state enables the design of dynamic networks. In this scenario, we propose to use the PCM-based neurons to design bio-inspired recurrent networks for autonomous decision making in navigation tasks. The agent relies on neuronal spike-frequency adaptation (SFA) to explore the environment via penalties and rewards. Finally, we show that the conductance drift of the PCM devices, contrarily to the applications in neural network accelerators, can improve the overall energy efficiency of neuromorphic computing by implementing bio-plausible active forgetting.

Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence

For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper proposes a solution for intelligent work control and traffic optimization. This paper is mainly focused on SDN-based network traffic algorithm optimization and experimental verification. In this paper, we design a network control mechanism for network intelligent control as well as solutions for traffic optimization based on SDN and artificial intelligence. We analyze operators’ network requirements (e.g., the carrying of the 5th generation mobile network (5G) service, multi-protocol label switching virtual private networks optimization, cloudification of services and the IP backbone network). Then, we propose an intelligent network control architecture based on SDN and artificial intelligence. The proposed architecture consists of three modules, including a network status collection/perception module, an AI intelligent analysis module and an SDN controller module. Moreover, this paper also analyzes the objects of traffic optimization as well as routing calculation algorithms (e.g., the greedy algorithm, the top-k-shortest paths (KSP) algorithm) and routing optimization algorithms (e.g., particle swarm optimization, simulated annealing and genetic algorithms). In addition, we also put forward three optimization algorithms for the operator’s network, namely, network congestion control and prevention algorithms, resource preemption algorithms and balance of the entire network traffic algorithms. Then, we propose optimization algorithms for the above three objectives of operator network optimization, respectively. Finally, we conduct large-scale experiments to verify the effectiveness of the control mechanism and algorithms. The experimental results demonstrate that the use of SDN and artificial intelligence in operator networks can realize network intelligent control and traffic optimization more intelligently.

Virus Propagation in Wireless Sensor Networks with Media Access Control Mechanism

In wireless sensor networks, network security against virus propagation is one of the challenges with the applications. In severe cases, the network system may become paralyzed. In order to study the process of virus propagation in wireless sensor networks with the media access control mechanism, this paper uses the susceptible-infectious-removed (SIR) model to analyze the spreading process. It provides a theoretical basis for the development of virus immune mechanisms to solve network virus attack hidden dangers. The research shows that the media access control (MAC) mechanism in the wireless sensor network can inhibit the process of virus propagation, reduce the network virus propagating speed, and decrease the scale of infected nodes. The listen/sleep duty cycle of this mechanism will affect the suppression effect of virus propagation. The smaller the listen/sleep duty cycle, the stronger the suppression effect. Energy consumption has a peak value under specific infection probability. Meanwhile, it is also found that the spreading scale of the virus in wireless sensor networks can be effectively inhibited by the MAC mechanism.

OpenTSN: an open-source project for time-sensitive networking system development

Time-sensitive networking (TSN) is a promising technique in many fields such as industrial automation and autonomous driving. The standardization of TSN has been rapidly improved by the IEEE 802.1 TSN working group. Currently, it has formed a comprehensive standard system with a wide range of choices. However, there is a large gap between TSN standards and application specific TSN systems. Designers need to determine the required TSN standards and standard implementation methods based on the application’s transmission performance and reliability requirements. Therefore, an easy-to-use developing platform for rapid TSN system prototyping and evaluation plays a vital role in the application of TSN technologies. This article mainly introduces OpenTSN, an open source project that supports rapid TSN system customization. This project has three features, which are SDN-based TSN network control mechanism, time-sensitive management protocol and time-sensitive switching model, for building an efficient TSN system. OpenTSN opens all the hardware and software source codes so that designers can quickly and flexibly customize the TSN system according to their own needs, maximizing the reuse of existing code and reducing the customization complexity. With this project, two FPGA-based prototyping examples with star and ring topology are presented at the experimental section. The experiment results show that the synchronization precision of the entire testing network is under 32 ns and the transmission performance matches the theory analysis of the testing Cyclic Queue and Forwarding based TSN network.

A Machine-Learning-Based Framework for Optimizing the Operation of Future Networks

5G and beyond are not only sophisticated and difficult to manage, but must also satisfy a wide range of stringent performance requirements and adapt quickly to changes in traffic and network state. Advances in machine learning and parallel computing underpin new powerful tools that have the potential to tackle these complex challenges. In this article, we develop a general machinelearning- based framework that leverages artificial intelligence to forecast future traffic demands and characterize traffic features. This makes it possible to exploit such traffic insights to improve the performance of critical network control mechanisms, such as load balancing, routing, and scheduling. In contrast to prior works that design problem-specific machine learning algorithms, our generic approach can be applied to different network functions, allowing reuse of existing control mechanisms with minimal modifications. We explain how our framework can orchestrate ML to improve two different network mechanisms. Further, we undertake validation by implementing one of these, mobile backhaul routing, using data collected by a major European operator and demonstrating a 3 reduction of the packet delay compared to traditional approaches.

EARS: Intelligence-driven experiential network architecture for automatic routing in software-defined networking

Software-Defined Networking (SDN) adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources. However, due to the limitation of traditional routing strategies relying on manual configuration, SDN may suffer from link congestion and inefficient bandwidth allocation among flows, which could degrade network performance significantly. In this paper, we propose EARS, an intelligence-driven experiential network architecture for automatic routing. EARS adapts deep reinforcement learning (DRL) to simulate the human methods of learning experiential knowledge, employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment. The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions. Under the network architecture, we design the network utility function with throughput and delay awareness, differentiate flows based on their size characteristics, and design a DDPG-based automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows. To validate the network architecture, we implement it on a real network environment. Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes (e.g. OSPF, ECMP).