Network Impairments Research Articles

Understanding and Influencing Responsibility Attribution when Experiencing Technical Issues in Video Conferencing

During video conferencing, technical issues such as network impairments can unexpectedly hinder remote collaboration for distributed teams. However, it remains unclear how such issues affect the impression formation process between unacquainted interlocutors in situations like job interviews or kick-off meetings. Having first encounters online without prior in-person interaction has become prevalent nowadays. Therefore, examining the impact of technical issues on remote unacquainted interlocutors and exploring design solutions to reduce the negative impact of technical issues on the impression is essential in informing the design of future communication systems. With three controlled experiments simulating an online job interview, we discovered that technical issues made people give low credibility ratings to remote job applicants (Study 1). We further examined two intervention approaches to reduce the negative impact of technical issues in video conferencing, including a virtual agent represented in a human-like avatar to actively take responsibility for the technical issues (Study 2), and forewarning messages to inform people about the technical issues in video conferencing (Study 3). Results demonstrated that introducing an agent that represents a communication system to take responsibility for technical issues actively could reduce the responsibility people assign to remote job applicants, but without increasing their positive credibility rating for remote job applicants. Furthermore, showing forewarning messages, which explicitly made people aware the cause of technical issues was unidentifiable and its potential negative impacts on impression formation, enabled people to rate the credibility of the remote counterpart without being influenced by technical issues. We discussed how interventions can be designed to mitigate negative attribution outcomes when encountering uncontrollable technical issues in computer-mediated communication.

Individual-level metabolic connectivity from dynamic [18F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard.

This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [18F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer's full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression. We designed an index (Distance from Healthy Group, DfHG) based on the alteration of wi-MC in each patient (n = 44) compared to a healthy reference (from 57 healthy controls), to individually quantify metabolic connectivity abnormalities, resulting in an Impairment Map highlighting significantly compromised areas. We then assessed whether our measure of metabolic network alteration is associated with well-established markers of disease severity (tumor grade and volume, with and without edema). Subsequently, we investigated disruptions in wi-MC homotopic connectivity, assessing both affected and seemingly healthy tissue to deepen the pathology's impact on neural communication. Finally, we compared network impairments with local metabolic alterations determined from SUVR, a validated diagnostic tool in clinical practice. Our framework revealed how gliomas cause extensive alterations in the topography of brain networks, even in structurally unaffected regions outside the lesion area, with a significant reduction in connectivity between contralateral homologous regions. High-grade gliomas have a stronger impact on brain networks, and edema plays a mediating role in global metabolic alterations. As compared to the conventional SUVR-based analysis, our approach offers a more holistic view of the disease burden in individual patients, providing interesting additional insights into glioma-related alterations. Considering our results, individual PET connectivity estimates could hold significant clinical value, potentially allowing the identification of new prognostic factors and personalized treatment in gliomas or other focal pathologies.

Early developmental alterations of CA1 pyramidal cells in Dravet syndrome

Dravet Syndrome (DS) is most often caused by heterozygous loss-of-function mutations in the voltage-gated sodium channel gene SCN1A (Nav1.1), resulting in severe epilepsy and neurodevelopmental impairment thought to be cause by reduced interneuron excitability. However, recent studies in mouse models suggest that interneuron dysfunction alone does not completely explain all the cellular and network impairments seen in DS. Here, we investigated the development of the intrinsic, synaptic, and network properties of CA1 pyramidal cells in a DS model prior to the appearance of overt seizures. We report that CA1 pyramidal cell development is altered by heterozygous reduction of Scn1a, and propose that this is explained by a period of reduced intrinsic excitability in early postnatal life, during which Scn1a is normally expressed in hippocampal pyramidal cells. We also use a novel ex vivo model of homeostatic plasticity to show an instability in homeostatic response during DS epileptogenesis. This study provides evidence for the early effects of Scn1a haploinsufficiency in pyramidal cells in contributing to the pathophysiology of DS.

Segmental vitiligo distribution follows the underlying arterial blood supply territory: a hypothesis based on anatomo-clinical, pathological and physio-pathological studies.

Segmental vitiligo (SV) is a subset of vitiligo typically characterized by its unilateral distribution. The pathogenesis of SV remains unclear, and until now the two main patterns proposed for SV have lacked biological support. This calls for a new approach. Use data obtained from anatomo-clinical, pathological, and physio-pathological studies to formulate a new hypothesis on segmental vitiligo distribution and its pathogenesis. Using transparent templates of local arterial blood supply, we evaluated anatomical correspondence (AC) in 140 SV lesions according to the number of SV lesions that fit within the corresponding arterial blood-supply areas. SV lesions were graded as 1 (moderate: AC < 50%), 2 (good: AC > 50%), or 3 (excellent: AC of all lesions). To support this anatomical investigation, we searched for complementary assessments according to the activity of SV lesions. Arterial and periarterial network impairment and inflammatory infiltration were histologically studied using nerve growth factor (NGF) and CD4 and CD8 monoclonal antibodies. Increased blood flow of the underlying arteries was also investigated using thermography and ultrasonography. We recruited 140 patients with a sex ratio of 0.8 and mean age 26.13 years. Localizations: head and neck 84.28%; trunk 6.42%; upper limb 5%; genital areas 2.14%; lower limb 1.42%. The AC of each SV lesion with the underlying artery blood supply territory was rated as 72% excellent; 16% good; and 12% moderate. Histologically (40 patients), we found some periarterial network impairments. Thermal asymmetry was significantly associated with active SV (p < 0.001). We hypothesized that SV distribution corresponds to the underlying artery blood-supply territory.

Different patterns of structural network impairments in two amyotrophic lateral sclerosis subtypes driven by 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging.

The structural network damages in amyotrophic lateral sclerosis patients are evident but contradictory due to the high heterogeneity of the disease. We hypothesized that patterns of structural network impairments would be different in amyotrophic lateral sclerosis subtypes by a data-driven method using 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging. The data of positron emission tomography, structural MRI and diffusion tensor imaging in fifty patients with amyotrophic lateral sclerosis and 23 healthy controls were collected by a 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid. Two amyotrophic lateral sclerosis subtypes were identified as the optimal cluster based on grey matter volume and standardized uptake value ratio. Network metrics at the global, local and connection levels were compared to explore the impaired patterns of structural networks in the identified subtypes. Compared with healthy controls, the two amyotrophic lateral sclerosis subtypes displayed a pattern of a locally impaired structural network centralized in the sensorimotor network and a pattern of an extensively impaired structural network in the whole brain. When comparing the two amyotrophic lateral sclerosis subgroups by a support vector machine classifier based on the decreases in nodal efficiency of structural network, the individualized network scores were obtained in every amyotrophic lateral sclerosis patient and demonstrated a positive correlation with disease severity. We clustered two amyotrophic lateral sclerosis subtypes by a data-driven method, which encompassed different patterns of structural network impairments. Our results imply that amyotrophic lateral sclerosis may possess the intrinsic damaged pattern of white matter network and thus provide a latent direction for stratification in clinical research.

Impact of network degradation on load frequency control of large interconnected power system

Due to the extensive geographical coverage of the power system, the load frequency control (LFC) performance is impacted by communication delays and packet loss that occur during the exchange of control and measurement signals. In the literature on LFC of large interconnected power systems, communication delays are often assumed to be arbitrary or time-varying values based on probability distributions or estimation models. Most studies neglect realistic delays based on the actual communication technology present in the system despite its practical significance. Furthermore, the impact of packet loss in LFC studies has been largely overlooked in the existing literature. This research work presents a comprehensive investigation into the impact of real-time communication delay and packet loss on LFC performance in large interconnected power systems. The simulation results demonstrate the significant impact of communication network impairments on LFC performance. The classical simulation results are validated using Real-Time Hardware-in-the-Loop (RT-HIL) simulation.

Unveiling serotonergic dysfunction of obsessive-compulsive disorder on prefrontal network dynamics: a computational perspective.

Serotonin (5-HT) regulates working memory within the prefrontal cortex network, which is crucial for understanding obsessive-compulsive disorder. However, the mechanisms how network dynamics and serotonin interact in obsessive-compulsive disorder remain elusive. Here, we incorporate 5-HT receptors (5-HT1A, 5-HT2A) and dopamine receptors into a multistable prefrontal cortex network model, replicating the experimentally observed inverted U-curve phenomenon. We show how the two 5-HT receptors antagonize neuronal activity and modulate network multistability. Reduced binding of 5-HT1A receptors increases global firing, while reduced binding of 5-HT2A receptors deepens attractors. The obtained results suggest reward-dependent synaptic plasticity mechanisms may attenuate 5-HT related network impairments. Integrating serotonin-mediated dopamine release into circuit, we observe that decreased serotonin concentration triggers the network into a deep attractor state, expanding the domain of attraction of stable nodes with high firing rate, potentially causing aberrant reverse learning. This suggests a hypothesis wherein elevated dopamine concentrations in obsessive-compulsive disorder might result from primary deficits in serotonin levels. Findings of this work underscore the pivotal role of serotonergic dysregulation in modulating synaptic plasticity through dopamine pathways, potentially contributing to learned obsessions. Interestingly, serotonin reuptake inhibitors and antidopaminergic potentiators can counteract the over-stable state of high-firing stable points, providing new insights into obsessive-compulsive disorder treatment.

Dynamic microglia alterations associate with hippocampal network impairments: A turning point in amyloid pathology progression

Alzheimer's disease is a progressive neurological disorder causing memory loss and cognitive decline. The underlying causes of cognitive deterioration and neurodegeneration remain unclear, leading to a lack of effective strategies to prevent dementia. Recent evidence highlights the role of neuroinflammation, particularly involving microglia, in Alzheimer's disease onset and progression. Characterizing the initial phase of Alzheimer's disease can lead to the discovery of new biomarkers and therapeutic targets, facilitating timely interventions for effective treatments.We used the AppNL-G-F knock-in mouse model, which resembles the amyloid pathology and neuroinflammatory characteristics of Alzheimer’s disease, to investigate the transition from a pre-plaque to an early plaque stage with a combined functional and molecular approach.Our experiments show a progressive decrease in the power of cognition-relevant hippocampal gamma oscillations during the early stage of amyloid pathology, together with a modification of fast-spiking interneuron intrinsic properties and postsynaptic input. Consistently, transcriptomic analyses revealed that these effects are accompanied by changes in synaptic function-associated pathways. Concurrently, homeostasis- and inflammatory-related microglia signature genes were downregulated. Moreover, we found a decrease in Iba1-positive microglia in the hippocampus that correlates with plaque aggregation and neuronal dysfunction. Collectively, these findings support the hypothesis that microglia play a protective role during the early stages of amyloid pathology by preventing plaque aggregation, supporting neuronal homeostasis, and overall preserving the oscillatory network’s functionality.These results suggest that the early alteration of microglia dynamics could be a pivotal event in the progression of Alzheimer's disease, potentially triggering plaque deposition, impairment of fast-spiking interneurons, and the breakdown of the oscillatory circuitry in the hippocampus.

Theta-burst rTMS in schizophrenia to ameliorate negative and cognitive symptoms: a double-blind, sham-controlled, randomized clinical trial

IntroductionSchizophrenia is a major mental disorder that affects approximately 1% of the population worldwide. Social cognition impairments and negative symptoms such as blunted affect or emotional withdrawal strongly contribute to the psychosocial functioning deficits and long-term disability in schizophrenia. The state-like and trait-like components of social cognition are impaired in schizophreniaObjectivesTreatment effects of conventional approaches with antipsychotics or psychosocial interventions are limited when it comes to reducing negative and cognitive symptoms in schizophrenia. While there is emerging clinical evidence that new, augmented protocols based on theta-burst stimulation can increase rTMS efficacy dramatically in depression, data on similar augmented therapies are very limited in schizophrenia. The different patterns of network impairments in subjects may underlie that some but not all patients responded to given stimulation locations.MethodsTherefore, we propose an augmented theta-burst stimulation protocol in schizophrenia by stimulating both locations connected to negative symptoms, namely the vermis of the cerebellum and the left Dorsolateral Prefrontal Cortex (DLPFC). Ninety subjects with schizophrenia presenting negative symptoms and aging between 18-50 years will be randomized to active and sham stimulation in a 1:1 ratio. The TBS parameters we adopted follow the standard TBS protocols, with 3-pulse 50-Hz bursts given every 200 ms (at 5 Hz) and an intensity of 100% active motor threshold. We plan to deliver 1800 stimuli to the vermis and 1800 stimuli to the left DLPFC daily in two 9.5-minute blocks for four weeks.ResultsThe primary endpoint is the change in negative symptom severity measured by the Positive and Negative Syndrome Scale (PANSS). Secondary efficacy endpoints are the change in cognitive flexibility measured by the Wisconsin Card Sorting Test and the change in social cognition assessed by the ‘Reading the Mind in the Eyes’, facial emotion recognition, and the ‘Faux pas’ tests. The safety outcome is the number serious adverse events.ConclusionsIn conlucion the aim of our study is to proove the safety and efficacy of theta burst stimulation for treating negativ sympotoms of schizophrenia.Disclosure of InterestNone Declared

Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality

Interactive, multi-user experiences are meant to define the present and future of Virtual Reality (VR). Such immersive experiences will typically consist of remote collaborations where content is streamed and/or synchronized over a network connection. Thus, real-time collaboration will be key. In this light, the responsiveness of the system and the network will define the overall experience. As such, understanding the effect of network distortions, especially related to time delay, on end-user’s perception (in terms of Quality-of-Experience (QoE)), performance, and collaboration becomes crucial. The existing literature, however, has mostly focused on network requirements from a system point-of-view, where the key performance parameters are only provided in the form of Quality-of-Service (QoS) parameters (such as end-to-end latency). However, the translation of these network impairments to the end-user experience is often omitted. The purpose of this paper is to fill the gap by providing a thorough investigation of the impact of latency on the perception of users while performing collaborative tasks in multi-user VR. To this end, an experimental framework was designed, developed, and tested. It is based on a multi-device synchronizing architecture, enabling two simultaneous users to work together in a gamified virtual environment. The developed test environment also allows for the identification of the most prominent network requirements and objective analysis for each traffic link. To experimentally investigate the impact of latency on user perception, a user study was conducted. Participants were paired and asked to perform the collaborative task under different latency-prone scenarios. The results show that users are able to easily distinguish between distorted and non-distorted network configurations. However, making a distinction between different manifestations of latency is much less straightforward. Moreover, factors such as the user’s role in the experience and the required task, and the level of interactivity and movement have an important influence on the subjective level of perception, the strength of the user’s preferences, and the occurrence of cybersickness. In contrast, no significant differences in objective metrics, such as system performance and user completion time were observed. These results can support the creation of collective QoE metrics that model the group as a whole rather than each individual separately. As such, this work provides an important step to dynamically counteract any drops in group dynamics and performance by means of smart interventions in the transmission system and/or virtual environment.

Introducing a Neuroscience-Based Assessment Instrument: Development and Psychometric Study of the Neural Networks Symptomatology Inventory.

Background: Neuroscience research methods contribute to the understanding of the underlying neural impairments associated with psychopathology. Previous research suggested that impairments in Default Mode Network, Fronto-Parietal Executive Network, Amygdaloid-Hippocampal Memory Network, and Attentional Salience Network are present in different psychopathological symptoms. However, a self-report measure based on this evidence is lacking. Aims: Therefore, the present study describes the development and preliminary psychometric study of the Neural Network Symptomatology Inventory (NNSI). Method: Two different samples were recruited (sample 1: N = 214, Mage = 21.0, SD = 7.10; sample 2: N = 194, Mage = 21.5, SD = 8.41) and responded to self-report instruments in a cross-sectional design. Standard methodologies to scale development and psychometric study were applied: Item development, Exploratory (EFA), Confirmatory Factor Analysis (CFA), and Pearson correlations. Results: EFA and CFA suggested a 4-factor model with adequate goodness-of-fit indices (χ2(449) = 808,9841, TLI = .89, CFI = .92, RMSEA = .048 (.042-.053). All NNSI subscales correlated positively with psychopathological domains and correlated negatively with psychological well-being. Conclusions: This preliminary study suggests that NNSI may be a valid instrument to assess symptomatology associated with complex neural network impairments. Nevertheless, further research is required to deepen and improve NNSI psychometric characteristics.

AsQM: Audio Streaming Quality Metric Based on Network Impairments and User Preferences

Audio streaming services have many users because the proliferation of cloud-based audio streaming services for different content. The complex networks that support these services do not always guarantee an acceptable quality on the end-user side. In this paper, the impact of temporal interruptions on the audio streaming reproduction and the user’s preference in relation to audio content are studied. In order to determine the key parameters in the audio streaming service, subjective tests are conducted, and their results show that a user’s quality-of-experience (QoE) is highly correlated with the following application parameters: the number of temporal interruptions or stalls, its frequency and length, and the temporal location where they occur. However, most important, the experimental results demonstrate that users’ preference for audio content plays an important role in users’ QoE. Thus, a preference factor (PF) function is defined and considered in the formulation of the proposed metric named audio streaming quality metric (AsQM). Considering that multimedia service providers are based on web servers, a framework to obtain user information is proposed. Furthermore, the results show that the AsQM implemented in the audio player of an end user’s device has a low impact on energy, processing and memory consumption.

Prolonged and specific spatial training during adolescence reverses adult hippocampal network impairments in a mouse model of fragile X syndrome

The fragile X syndrome (FXS) is the leading monogenetic cause of cognitive impairment and autism. A hallmark of FXS in patients and the FXS mouse model (Fmr1 KO) is an overabundance of immature appearing dendritic spines in the cortex and hippocampus which is associated with behavioral deficits. Spine analysis in the different hippocampal subregions and at different developmental stages revealed that in adult mice, hippocampal spine pathology occurs specifically in the CA3 subregion, which plays a pivotal role in pattern completion processes important for efficient memory recall from parts of the initial memory stimulus. In line with this synaptic defect we document an impairment in memory recall during partially cued reference memory test in the Morris water maze task. This is accompanied by impaired recruitment of engram cells as well as impaired spine structural plasticity in the CA3 region. In order to promote hippocampal network development adolescent mice were either raised in an enriched environment or subjected to specific hippocampus-dependent spatial training. Intriguingly, only specific spatial training alleviated the cognitive symptoms and the spine phenotype shown in adult Fmr1 KO mice suggesting that specific stimulation of hippocampal networks during development might be used in the future as a therapeutic strategy.

Alterations to parvalbumin-expressing interneuron function and associated network oscillations in the hippocampal – medial prefrontal cortex circuit during natural sleep in AppNL-G-F/NL-G-F mice

In the early stages of Alzheimer's disease (AD), the accumulation of the peptide amyloid-β (Aβ) damages synapses and disrupts neuronal activity, leading to the disruption of neuronal oscillations associated with cognition. This is thought to be largely due to impairments in CNS synaptic inhibition, particularly via parvalbumin (PV)-expressing interneurons that are essential for generating several key oscillations. Research in this field has largely been conducted in mouse models that over-express humanised, mutated forms of AD-associated genes that produce exaggerated pathology. This has prompted the development and use of knock-in mouse lines that express these genes at an endogenous level, such as the AppNL-G-F/NL-G-F mouse model used in the present study. These mice appear to model the early stages of Aβ-induced network impairments, yet an in-depth characterisation of these impairments in currently lacking. Therefore, using 16 month-old AppNL-G-F/NL-G-F mice, we analysed neuronal oscillations found in the hippocampus and medial prefrontal cortex (mPFC) during awake behaviour, rapid eye movement (REM) and non-REM (NREM) sleep to assess the extent of network dysfunction. No alterations to gamma oscillations were found to occur in the hippocampus or mPFC during either awake behaviour, REM or NREM sleep. However, during NREM sleep an increase in the power of mPFC spindles and decrease in the power of hippocampal sharp-wave ripples was identified. The latter was accompanied by an increase in the synchronisation of PV-expressing interneuron activity, as measured using two-photon Ca2+ imaging, as well as a decrease in PV-expressing interneuron density. Furthermore, although changes were detected in local network function of mPFC and hippocampus, long-range communication between these regions appeared intact. Altogether, our results suggest that these NREM sleep-specific impairments represent the early stages of circuit breakdown in response to amyloidopathy.

Cortico-muscular coherence and brain networks in familial adult myoclonic epilepsy and progressive myoclonic epilepsy

ObjectiveFamilial Adult Myoclonic Epilepsy (FAME) presents with action-activated myoclonus, often associated with epilepsy, sharing various features with Progressive Myoclonic Epilepsy (PMEs), but with slower course and limited motor disability. We aimed our study to identify measures suitable to explain the different severity of FAME2 compared to EPM1, the most common PME, and to detect the signature of the distinctive brain networks. MethodsWe analyzed the EEG-EMG coherence (CMC) during segmental motor activity and indexes of connectivity in the two patient groups, and in healthy subjects (HS). We also investigated the regional and global properties of the network. ResultsIn FAME2, differently from EPM1, we found a well-localized distribution of beta-CMC and increased betweenness-centrality (BC) on the sensorimotor region contralateral to the activated hand.In both patient groups, compared to HS, there was a decline in the network connectivity indexes in the beta and gamma band, which was more obvious in FAME2. ConclusionsIn FAME2, better localized CMC and increased BC in comparison with EPM1 patients could counteract the severity and the spreading of the myoclonus.Decreased indexes of cortical integration were more severe in FAME2. SignificanceOur measures correlated with different motor disabilities and identified distinctive brain network impairments.

REST and EDA architectures in IoT actuation

While most Internet of Things (IoT) solutions involve sensing, some of them also introduce actuation mechanisms. Specifically, devices interact with assets in the environment and transmit sensor readouts to applications that perform analytics. These applications typically reside on the network core and, in turn, process the readouts that trigger the transmission of actuation commands to the device. One important issue in these schemes is the nature of the communication channels. Most devices are wireless and therefore they are affected by the effects of signal multipath fading that results in application loss. More importantly, these impairments may cause actuation commands to be lost or to be critically delayed. In this context, several standard mechanisms have been proposed for the transmission of traffic from the application to the devices. They fall under two main architectural categories: (1) Representational State Transfer (REST) and (2) Event Driven Architecture (EDA). In this paper, we analyze two protocols associated with each of these two architectures by comparing them in order to assess their efficiency in IoT actuation solutions. This analysis leads to the development of a mathematical model that drives an algorithm that enables the dynamic selection of the right technology based on network impairments.

Feasibility of critical infrastructure protection using network functions for programmable and decoupled ICS policy enforcement over WAN

Industrial control systems (ICS) represent a major component of our critical infrastructure. With the increasing need for more control and monitoring of such systems, ICS have seen an increase in connectivity to wide area networks (WAN) exposing aging equipment to rapidly evolving cybersecurity threats. Furthermore, the ICS data requires a reliability measure from the networks for critical functions for infrastructure monitoring and control. Especially when remote plant sites are involved such as pipelines, energy distribution networks, and transportation, WAN transport impairments most often provide a best effort delivery with no strict reliability guarantees. Network functions can provide a vendor agnostic, programmable critical infrastructure protection with a single maintenance, policy determination, and reliability assurance surface. A network function (NF) can be utilized for policy enforcement over the communication between remote entities and the main control office. This paper presents the research on transparent integration with existing ICS without disrupting communications, resulting in minimal downtime while decoupling the fast paced evolution of defensive security measures from the upgrade cycle of expensive long term hardware. We report our measurements on the resource requirements and overhead in the network for successful NF insertion under a wide variety of network impairments (network packet delay, reordering, and loss). Our paired NF implementation provides a policy enforcement platform extensible to cover myriad cybersecurity-related communication goals, including packet signing for verification, encryption for data privacy, packet filtering and data diode operation (i.e. protecting against eavesdropping, packet injection, and denial-of-service). Furthermore, bundling communication specifications into packet flows allows for tunability in applying policies as coarse- or fine-grained as the needs of the operator. We report on network function resource requirements in the form of required queue depth and network utilization overhead.

Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications.

With the advancement in next-generation communication technologies, the so-called Tactile Internet is getting more attention due to its smart applications, such as haptic-enabled teleoperation systems. The stringent requirements such as delay, jitter, and packet loss of these delay-sensitive and loss-intolerant applications make it more challenging to ensure the Quality of Service (QoS) and Quality of Experience (QoE). In this regard, different haptic codec and control schemes were proposed for QoS and QoE provisioning in the Tactile Internet. However, they maximize the QoE while degrading the system’s stability under varying delays and high packet rates. In this paper, we present a reinforcement learning-based Intelligent Tactile Edge (ITE) framework to ensure both transparency and stability of teleoperation systems with high packet rates and variable time delay communication networks. The proposed ITE first estimates the network challenges, including communication delay, jitter, and packet loss, and then utilizes a Q-learning algorithm to select the optimal haptic codec scheme to reduce network load. The proposed framework aims to explore the optimal relationship between QoS and QoE parameters and make the tradeoff between stability and transparency during teleoperations. The simulation result indicates that the proposed strategy chooses the optimal scheme under different network impairments corresponding to the congestion level in the communication network while improving the QoS and maximizing the QoE. The end-to-end performance of throughput (1.5 Mbps) and average RTT (70 ms) during haptic communication is achieved with a learning rate and discounted factor value of 0.5 and 0.8, respectively. The results indicate that the communication system can successfully achieve the QoS and QoE requirements by employing the proposed ITE framework.

Graph wavelets for fault localization in optical mesh networks

• Real-time fault detection and localization mechanism in Elastic Optical Networks (EONs). • Alarm correlation strategies fail in the face of static threshold settings. • Detects and localizes silent network failures. • Multi-resolution analysis based on graph wavelets to address fault detection and localization problems. • Exceeds alarm based engines for fault detection and localization accuracy. EONs (Elastic Optical Networks) form the backbone of web-scale cloud infrastructure. These networks should deal with network faults and restoration in real-time with high accuracy and scalability. Thus, fault management functions to detect and localize such failures, play a critical role in modern day EONs. Network impairments are complex in nature in optical networks such as fiber losses, NLI (Non Linear Impairments) and ASE (Amplified Stimulated Emission). Currently, pre-engineered thresholds are used to detect failures in optical networks. Traditional alarm correlation strategies fail in the face of these static threshold settings. In this work, we introduce a real-time fault detection and localization mechanism, which is agnostic to such pre-engineered threshold settings. We apply multi-resolution analysis based on graph wavelets to address fault detection and localization problems. Thus, we propose to use spectrograms and wavelet coefficients to identify the irregularities in an otherwise smooth graph signal. We present a real-time fault localization analysis based on a novel Wavelet Coefficients Trail (WCT) algorithm to root-cause a client signal failure due to OSNR (Optical Signal to Noise Ratio) degradation. We apply the proposed techniques and algorithms to simulated and actual network data. Our results indicate that the proposed methods can root-cause OSNR degradation to major/minor deviations in received power on optical spans. In conclusion, we present a fault localization framework based on graph wavelets in optical mesh networks.

Supervised graph convolution networks for OSNR and power estimation in optical mesh networks

The optical signal-to-noise ratio (OSNR) and received optical channel power are critical parameters in determining the quality of transmission. The OSNR and received optical channel power are influenced by network impairments such as fiber loss, amplified stimulated emission noise, and nonlinear impairments. Furthermore, environmental effects and routing, modulation, and spectrum assignment schemes influence the OSNR and thus the reach of the optical channels. These impairments and effects vary with the spectral loads that are hard to predict in brownfield networks. Several deep neural network (DNN)-based methods have been explored to estimate the OSNR and nonlinear noise. However, these methods ignore the network topology. This paper bridges this gap by leveraging supervised graph convolution neural networks (GCNs), which operate directly on graphs for OSNR and received power estimation in an optical mesh network. We also develop and implement a novel graph windowed neural network (GWinN) to reduce the over-smoothing effects of a GCN and thus learn localized behaviors like fiber cuts. We apply a DNN, GCN, and GWinN in practice to a testbed of 8 reconfigurable optical add-drop multiplexers and 22 amplifiers. Our procedure accurately estimates the OSNR with a prediction error mean and a standard deviation of ( − 0.02 d B , 0.35 dB) for a reference OSNR ranging from (16 dB) to (24 dB).