Dynamic Network Configuration Research Articles

On the Optimal Deployment of Virtual Network Functions in Non-Terrestrial Segments

In the upcoming Sixth-Generation (6G) of mobile communication systems, space network entities will cooperate with conventional terrestrial networks to provide three-dimensional wireless connectivity around the World. By considering the resulting massive amount of data to be managed into non-terrestrial segments, it will be necessary to dynamically configure functionalities across space network entities. Preliminary contributions in this context focus on quasi-static scenarios, while ignoring the challenging issues (i.e., intermittent visibility, dynamic network configuration, and communication delays) introduced by satellites’ movement and communication protocols enabling the integration of terrestrial and non-terrestrial networks. To bridge this gap, this work presents a network architecture with novel orchestration capabilities of services into non-terrestrial segments. In the proposed approach, the interaction between terrestrial and non-terrestrial entities and the cloud has been detailed across service request, configuration, and provisioning phases. Then, starting from a system model describing the network configuration and the resulting deployment delays of services, an optimization problem has been formulated to dynamically allocate Virtual Network Functions (VNFs) among LEO CubeSats over a looking-ahead horizon, based on service requests, computational capabilities of the involved CubeSats, visibility matrices, and expected deployment delay bounds. Finally, the proposed optimization problem has been solved through three heuristic strategies. Computer simulations have been carried out to demonstrate the ability of the developed strategies to achieve results close to the optimal solution and to ensure better performance against a benchmark scheme.

Real-Time Anomaly Detection of Network Traffic Based on CNN

Network traffic anomaly detection mainly detects and analyzes abnormal traffic by extracting the statistical features of network traffic. It is necessary to fully understand the concept of symmetry in anomaly detection and anomaly mitigation. However, the original information on network traffic is easily lost, and the adjustment of dynamic network configuration becomes gradually complicated. To solve this problem, we designed and realized a new online anomaly detection system based on software defined networks. The system uses the convolutional neural network to directly extract the original features of the network flow for analysis, which can realize online real- time packet extraction and detection. It utilizes SDN to flexibly adapt to changes in the network, allowing for a zero-configuration anomaly detection system. The packet filter of the anomaly detection system is used to automatically implement mitigation strategies to achieve online real-time mitigation of abnormal traffic. The experimental results show that the proposed method is more accurate and can warn the network manager in time that security measures can be taken, which fully demonstrates that the method can effectively detect abnormal traffic problems and improve the security performance of edge clustering networks.

Interictal dynamic network transitions in mesial temporal lobe epilepsy.

To reveal the possible routine of brain network dynamic alterations in patients with mesial temporal lobe epilepsy (mTLE) and to establish a predicted model of seizure recurrence during interictal periods. Seventy-nine unilateral mTLE patients with hippocampal sclerosis and 97 healthy controls from two centers were retrospectively enrolled. Dynamic brain configuration analyses were performed with resting-state functional magnetic resonance imaging (MRI) data to quantify the functional stability over time and the dynamic interactions between brain regions. Relationships between seizure frequency and ipsilateral hippocampal module allegiance were evaluated using a machine learning predictive model. Compared to the healthy controls, patients with mTLE displayed an overall higher dynamic network, switching mainly in the epileptogenic regions (false discovery rate [FDR] correctedp-FDR < .05). Moreover, the dynamic network configuration in mTLE was characterized by decreased recruitment (intra-network communication), and increased integration (inter-network communication) among hippocampal systems and large-scale higher-order brain networks (p-FDR < .05). We further found that the dynamic interactions between the hippocampal system and the default-mode network (DMN) or control networks exhibited an opposite distribution pattern (p-FDR < .05). Strikingly, we showed that there was a robust association between predicted seizure frequency based on the ipsilateral hippocampal-DMN dynamics model and actual seizure frequency (p-perm < .001). These findings suggest that the interictal brain of mTLE is characterized by dynamical shifts toward unstable state. Our study provides novel insights into the brain dynamic network alterations and supports the potential use of DMN dynamic parameters as candidate neuroimaging markers in monitoring the seizure frequency clinically during interictal periods.

Learning Decentralized Wireless Resource Allocations With Graph Neural Networks

We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure. We develop the use of Aggregation Graph Neural Networks (Agg-GNNs), which process a sequence of delayed and potentially asynchronous graph aggregated state information obtained locally at each transmitter from multi-hop neighbors. We further utilize model-free primal-dual learning methods to optimize performance subject to constraints in the presence of delay and asynchrony inherent to decentralized networks. We demonstrate a permutation equivariance property of the resulting resource allocation policy that can be shown to facilitate transference to dynamic network configurations. The proposed framework is validated with numerical simulations that exhibit superior performance to baseline strategies.

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.

A user-centric intelligent context-aware system for realizing internet-of-things environments

In the near future, an Internet-of-things (IoT) environment will be realized across ubiquitous terminals such as computers and smartphones, supporting connections between people and machines. IoT platforms facilitate the deployment of various services to users; however, the information provided by these platforms is focused on the present state of things and environments. To realize a true IoT environment, there is a need for user-centric and seamless context-aware services as well as IoT control services based on environmental feedback. In this paper, we propose a dynamic mesh network configuration method to analyze real-time user context and provide seamless services, as well as a user context-aware abstraction mechanism. To evaluate the performance of the proposed system, we simulate a parking system, and then compare the performance to the best effort method, finding that performance was significantly improved. Thus, we expect that seamless context-aware services will become possible through the application of such methods.

EFSUTE: a novel efficient and survivable traffic engineering for software defined networks

Computer networks are the fundamental infrastructure of intelligent environments (IEs). Software defined network (SDN) is an emerging software-based network architecture that separates the control plane from data plane to facilitate dynamic network management and configuration; two important parameters which are essential in real-time IEs. This separation alleviates network administrators to implement efficient fault tolerance strategies and increasing reliability and quality of service (QoS). Hence, they can make their networks more reliable and as a result, decrease the adverse effects due to network failures. As a result, SDN is a candidate for using in new IEs such as internet of things (IoT). The control plane of SDN can be deployed on the IoT cloud server to manage the entire network. Link failure is one of the most common and critical failures in such computer networks which causes dropping of data packets and reduces network performance considerably. A survivable traffic engineering (TE) method can provide protection against link failures and heighten the reliability of data transmission. To this purpose, this paper proposes an efficient and survivable software-based TE model over SDN called EFSUTE to increase reliability in real-time IEs. EFSUTE leverages the capabilities of SDN to compute and install two disjoint paths between any source–destination pair in the network. EFSUTE models network routing as a Shared Risk Link Group problem which provides topological diversity. This policy increases the survivability by providing alternative paths between network hosts. To prove the effectiveness of EFSUTE, we conducted extensive simulations and compared it with restoration techniques. Experimental results confirm that EFSUTE can be practically used in SDN and efficiently protects the network traffics against link failures to increase safety and provides a reliable software based infrastructure with QoS for real-time IEs.

Autonomous Operation Control of IoT Blockchain Networks

Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with sufficient computing power, a blockchain node with low computing power and memory, such as an IoT sensor/actuator, cannot operate in a blockchain as a fully functional node. A lightweight blockchain provides practical blockchain availability over IoT networks. We propose essential operational advances to develop a lightweight blockchain over IoT networks. A dynamic network configuration enforced by deep clustering provides ad-hoc flexibility for IoT network environments. The proposed graph neural network technique enhances the efficiency of dApp (distributed application) spreading across IoT networks. In addition, the proposed blockchain technology is highly implementable in software because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed methods.

Reward-Based Decision-Making Engages Distinct Modes of Cross-Frequency Coupling.

Prefrontal cortex exerts control over sensory and motor systems via cross-frequency coupling. However, it is unknown whether these signals play a role in reward-based decision-making and whether such dynamic network configuration is altered in a major depressive episode. We recruited men and women with and without depression to perform a streamlined version of the Expenditure of Effort for Reward Task during recording of electroencephalography. Goal-directed behavior was quantified as willingness to exert physical effort to obtain reward, and reward-evaluation was the degree to which the decision to exert effort was modulated by incentive level. We found that the amplitude of frontal-midline theta oscillations was greatest in participants with the greatest reward-evaluation. Furthermore, coupling between frontal theta phase and parieto-occipital gamma amplitude was positively correlated with reward-evaluation. In addition, goal-directed behavior was positively correlated with coupling between frontal delta phase to motor beta amplitude. Finally, we performed a factor analysis to derive 2 symptom dimensions and found that mood symptoms positively tracked with reward-evaluation and motivation symptoms negatively tracked with goal-directed behavior. Altogether, these results provide evidence that 2 aspects of reward-based decision-making are instantiated by different modes of prefrontal top-down control and are modulated in different symptom dimensions of depression.

A Novel Routing Algorithm for the Acceleration of Flow Scheduling in Time-Sensitive Networks.

IEEE Time-Sensitive Networking (TSN) Task Group specifies a series of standards such as 802.1Qbv for enhancing the management of time-critical flows in real-time networks. Under the IEEE 802.1Qbv standard, the scheduling algorithm is employed to determine the time when a specific gate in the network entities is opened or closed so that the real-time requirements for the flows are guaranteed. The computation time of this scheduling algorithm is critical for the system where dynamic network configurations and settings are required. In addition, the network routing where the paths of the flows are determined has a significant impact on the computation time of the network scheduling. This paper presents a novel scheduling-aware routing algorithm to minimize the computation time of the scheduling algorithm in network management. The proposed routing algorithm determines the path for each time-triggered flow by including the consideration of the period of the flow. This decreases the occurrence of path-conflict during the stage of network scheduling. The detailed outline of the proposed algorithm is presented in this paper. The experimental results show that the proposed routing algorithm reduces the computation time of network scheduling by up to 30% and improves the schedulability of time-triggered flows is the network.

Detection of the triple riding and speed violation on two-wheelers using deep learning algorithms

To curb the accident rate and traffic levels, strict implementation of the rules and continuous monitoring of the traffic is mandatory. Traffic Rule Violation Monitoring System ensures that the rules are followed strictly and it reduces the human effort. The main objective of this work is to identify the Triple Riding. To detect the triple riders, the deep learning framework darknet is used, which in turn uses a type of convolutional neural networks i.e. Deconvolutional neural network-based YOLO (You Only Look Once) algorithm for detection of the number of persons riding a bike, the system classifies the vehicle as to the rule-breach vehicle or not. The junctions acting as the data collections center, collects the data. The image of the vehicle classified as the rule-breach is stored along with the data such as vehicle manufacturing ID and vehicle speed transferred at the particular frame. The transfer of the data is facilitated using the GSM module and the NodeMCU deployed on the vehicle. The vehicle number will be verified with the transport office. To survive the lack of internet connectivity or low internet connectivity, the system is being equipped with the GSM module; else, the data related to the vehicle can be pulled by the development boards deployed at the junctions, acting them as the central part of the public internetwork deployed. This public internetwork acting the medium to pull the data from the vehicle to the central system. This is carried out using the concept of dynamic network configuration in NodeMCU. The use of Node MCU and the public network system makes the system much more viable, available and reliable. Thereby making the riders follow the rules properly and reducing irresponsible driving.

Detecting False Data Injection Attacks Using Canonical Variate Analysis in Power Grid

With the knowledge of the measurement configuration and the topology structure of a power system, attackers can launch false data injection attacks (FDIAs) without detection by existing bad data detection methods in state estimation. The attacks can also introduce errors to estimated state variables, which are critical to grid reliability and operation stability. Existing protection methods cannot handle dynamic and variable network configurations. In this paper, to effectively defend against FDIAs, we propose a canonical variate analysis based detection method which monitors the variation of statistical detection indicators $\boldsymbol{T}^2$ and $\boldsymbol{Q}$ about projected canonical variables before and after attacks. Unlike most statistic models that only consider cross-correlation of discrete measurements constrained by Kirchhoff's Law at each independent sampling time, we also consider the auto-correlation of measurements caused by time series characteristics of varying loads. Experiment results on IEEE-14 bus system demonstrate the effectiveness and accuracy of our proposed method based on both synthetically generated data and real-world electricity data from the New York independent system operator.

Effective brain connectivity at rest is associated with choice-induced preference formation.

Preferences can change as a consequence of making hard decisions whereby the value of chosen options increases and the value of rejected options decreases. Such choice‐induced preference changes have been associated with brain areas detecting choice conflict (anterior cingulate cortex, ACC), updating stimulus value (dorsolateral prefrontal cortex, dlPFC) and supporting memory of stimulus value (hippocampus and ventromedial prefrontal cortex, vmPFC). Here we investigated whether resting‐state neuronal activity within these regions is associated with the magnitude of individuals' preference updates. We fitted a dynamic causal model (DCM) to resting‐state neuronal activity in the spectral domain (spDCM) and estimated the causal connectivity among core regions involved in preference formation following hard choices. The extent of individuals' choice‐induced preference changes were found to be associated with a diminished resting‐state excitation between the left dlPFC and the vmPFC, whereas preference consistency was related to a higher resting‐state excitation from the ACC to the left hippocampus and vmPFC. Our results point to a model of preference formation during which the dynamic network configurations between left dlPFC, ACC, vmPFC and left hippocampus at rest are linked to preference change or stability.

Not All Attention Is Needed: Gated Attention Network for Sequence Data

Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional neural networks (CNNs) selectively activates only part of the network at a time for different inputs. In this paper, we combine the two dynamic mechanisms for text classification tasks. Traditional attention mechanisms attend to the whole sequence of hidden states for an input sentence, while in most cases not all attention is needed especially for long sequences. We propose a novel method called Gated Attention Network (GA-Net) to dynamically select a subset of elements to attend to using an auxiliary network, and compute attention weights to aggregate the selected elements. It avoids a significant amount of unnecessary computation on unattended elements, and allows the model to pay attention to important parts of the sequence. Experiments in various datasets show that the proposed method achieves better performance compared with all baseline models with global or local attention while requiring less computation and achieving better interpretability. It is also promising to extend the idea to more complex attention-based models, such as transformers and seq-to-seq models.

On the Dynamic Centralized Coded Caching Design

Coded caching scheme provides us an effective framework to realize additional coded multicasting gain by exploiting coding into multiple transmitted signals. The goal of coded caching design is to jointly optimize the placement and delivery scheme so as to minimize the amount of transmitted coded signals. However, few research efforts consider multiple-round coded caching design problem, in which fixed and mobile users may coexist in one network, namely different number of active users present in successive rounds. Obviously, such dynamic network configurations may lead to undesired frequent placement caching content updating at user sides, if we assume coded caching scheme for all users in each round separately. Thus how to tailor the coded caching design, such that the frequent caching content updating in the placement phase can be avoided, and simultaneously retaining full caching gain in delivery phase in multiple rounds will become highly desirable. In this paper, by carefully examining the bipartite graph representation of the coded caching scheme, a dynamic centralized coded caching design is proposed on the basis of the concatenating-based placement and the saturating matching based delivery scheme. Our analysis unveils that, the proposed dynamic coded caching scheme can realize the flexible coded multicast, and is order optimal.

Designing an efficient security framework for detecting intrusions in virtual network of cloud computing

Cloud computing has grown for various IT capabilities such as IoTs, Mobile Computing, Smart IT, etc. However, due to the dynamic and distributed nature of cloud and vulnerabilities existing in the current implementations of virtualization, several security threats and attacks have been reported. To address these issues, there is a need of extending traditional security solutions like firewall, intrusion detection/prevention systems which can cope up with high-speed network traffic and dynamic network configuration in the cloud. In addition, identifying feasible network traffic features is a major challenge for an accurate detection of the attacks. In this paper, we propose a hypervisor level distributed network security (HLDNS) framework which is deployed on each processing server of cloud computing. At each server, it monitors the underlying virtual machines (VMs) related network traffic to/from the virtual network, internal network and external network for intrusion detection. We have extended a binary bat algorithm (BBA) with two new fitness functions for deriving the feasible features from cloud network traffic. The derived features are applied to the Random Forest classifier for detecting the intrusions in cloud network traffic and intrusion alerts are generated. The intrusion alerts from different servers are correlated to identify the distributed attack and to generate new attack signature. For the performance and feasibility analysis, the proposed security framework is tested on the cloud network testbed at NIT Goa and using recent UNSW-NB15 and CICIDS-2017 intrusion datasets. We have performed a comparative analysis of the proposed security framework in terms of fulfilling the cloud network security needs.

Enabling hard service guarantees in Software-Defined Smart Grid infrastructures

Information and Communication Technology (ICT) infrastructures play a key role in the evolution from traditional power systems to Smart Grids. Increasingly fluctuating power flows, sparked by the transition towards sustainable energy generation, become a major issue for power grid stability. To deal with this challenge, future Smart Grids require precise monitoring and control, which in turn demand for reliable, real-time capable and cost-efficient communications. For this purpose, we propose applying Software-Defined Networking (SDN) to handle the manifold requirements of Smart Grid communications. To achieve reliability, our approach encompasses fast recovery after failures in the communication network and dynamic service-aware network (re-)configuration. Network Calculus (NC) logic is embedded into our SDN controller for meeting latency requirements imposed by the standard IEC 61850 of the International Electrotechnical Commission (IEC). Thus, routing provides delay-optimal paths under consideration of existing cross traffic. Also, continuous latency bound compliance is ensured by combining NC delay supervision with means of flexible reconfiguration. For evaluation we consider the well-known Nordic 32 test system, on which we map a corresponding communication network in both experiment and emulation. The described functionalities are validated, employing realistic IEC 61850 transmissions and distributed control traffic. Our results show that hard service guarantees can be ensured with the help of the proposed SDN solution. On this basis, we derive extremely time critical services, which must not be subjected to flexible reconfiguration.

Improving networked music performance systems using application‐network collaboration

SummaryNetworked Music Performance (NMP) systems involve musicians located in different places who perform music while staying synchronized via the Internet. The maximum end‐to‐end delay in NMP applications is called Ensemble Performance Threshold (EPT) and should be less than 25 milliseconds. Due to this constraint, NMPs require ultra–low‐delay solutions for audio coding, network transmission, relaying, and decoding, each one a challenging task on its own. There are two directions for study in the related work referring to the NMP systems. From the audio perspective, researchers experiment on low‐delay encoders and transmission patterns, aiming to reduce the processing delay of the audio transmission, but they ignore the network performance. On the other hand, network‐oriented researchers try to reduce the network delay, which contributes to reduced end‐to‐end delay. In our proposed approach, we introduce an integration of dynamic audio and network configuration to satisfy the EPT constraint. The basic idea is that, the major components participating in an NMP system, the application and the network interact during the live music performance. As the network delay increases, the network tries to equalize it by modifying the routing behavior using Software‐Defined Networking principles. If the network delay exceeds a maximum affordable threshold, the network reacts by informing the application to change the audio processing pattern to overcome the delay increase, resulting in below EPT end‐to‐end delay. A full prototype of the proposed system was implemented and extensively evaluated in an emulated environment.

Attention Selectively Gates Afferent Signal Transmission to Area V4.

Selective attention allows focusing on only part of the incoming sensory information. Neurons in the extrastriate visual cortex reflect such selective processing when different stimuli are simultaneously present in their large receptive fields. Their spiking response then resembles the response to the attended stimulus when presented in isolation. Unclear is where in the neuronal pathway attention intervenes to achieve such selective signal routing and processing. To investigate this question, we tagged two equivalent visual stimuli by independent broadband luminance noise and used the spectral coherence of these behaviorally irrelevant signals with the field potential of a local neuronal population in male macaque monkeys' area V4 as a measure for their respective causal influences. This new experimental paradigm revealed that signal transmission was considerably weaker for the not-attended stimulus. Furthermore, our results show that attention does not need to modulate responses in the input populations sending signals to V4 to selectively represent a stimulus, nor do they suggest a change of the V4 neurons' output gain depending on their feature similarity with the stimuli. Our results rather imply that selective attention uses a gating mechanism comprising the synaptic "inputs" that transmit signals from upstream areas into the V4 neurons. A minimal model implementing attention-dependent routing by gamma-band synchrony replicated the attentional gating effect and the signals' spectral transfer characteristics. It supports the proposal that selective interareal gamma-band synchrony subserves signal routing and explains our experimental finding that attention selectively gates signals already at the level of afferent synaptic input.SIGNIFICANCE STATEMENT Depending on the behavioral context, the brain needs to channel the flow of information through its networks of massively interconnected neurons. We designed an experiment that allows to causally assess routing of information originating from an attended object. We found that attention "gates" signals at the interplay between afferent fibers and the local neurons. A minimal model demonstrated that coherent gamma-rhythmic activity (∼60 Hz) between local neurons and their afferent-providing input neurons can realize the gating. Importantly, the attended signals did not need to be amplified already in an earlier processing stage, nor did they get amplified by a simple output response modulation. The method provides a useful tool to study mechanisms of dynamic network configuration underlying cognitive processes.

A Survey on Security-Aware Measurement in SDN

Software-defined networking (SDN) is one of the most prevailing networking paradigms in current and next-generation networks. Basically, the highly featured separation of control and data planes makes SDN a proper solution towards many practical problems that challenge legacy networks, for example, energy efficiency, dynamic network configuration, agile network measurement, and flexible network deployment. Although the SDN and its applications have been extensively studied for several years, the research of SDN security is still in its infancy. Typically, the SDN suffers from architecture defect and OpenFlow protocol loopholes such as single controller problem, deficiency of communication verification, and network resources constraint. Hence, network measurement is a fundamental technique of protecting SDN against the above security threats. Specifically, network measurement aims to understand and quantify a variety of network behaviors to facilitate network management and monitoring, anomaly detection, network troubleshooting, and the establishment of security mechanisms. In this paper, we present a systematic survey on security-aware measurement technology in SDN. In particular, we first review the basic architecture of SDN and corresponding security challenges. Then, we investigate two performance measurement techniques in SDN, namely, link latency and available bandwidth measurements. After that, we further provide a general overview of topology measurement in SDN including intradomain and interdomain topology discovering techniques. Finally, we list three interesting future directions of security-aware measurement in SDN followed by giving conclusion remarks.