Autonomous Network Research Articles

Analysis of Brain Regions Activated by Afferent Renal Nerves in DOCA-Salt Hypertensive Mice

We have reported that the neurogenic component of DOCA-salt HTN is dependent on afferent renal nerves (ARNs) that modulate central autonomic networks, however, the central pathways are poorly understood. Targeted recombination in active populations (TRAP2) mice provide a model for global visualization and quantification of neuronal activity via the expression of the fluorescent reporter protein tdTomato which is expressed under the immediate-early gene, c-Fos, a neuronal activation marker, via tamoxifen-inducible Cre. In combination with the deoxycorticosterone-acetate (DOCA)-salt model of hypertension (HTN), neuronal activation in central autonomic centers can be evaluated in TRAP2 mice during DOCA-salt-induced HTN to identify the role of ARNs in the control of blood pressure. We evaluated the expression of tdTomato in TRAP2 mice as a measure of c-Fos activation following 7 days of DOCA-salt HTN and compared it to immunolabeled c-Fos at day 21 of DOCA-salt HTN in brain regions known to be involved in blood pressure regulation. We hypothesized that DOCA will increase the expression of tdTomato and c-Fos in mice with intact renal nerves and that afferent renal denervation (ARDN) will attenuate this DOCA-induced neuronal activity. Three groups of TRAP2-DOCA mice were studied: normotensive (Vehicle-Sham), hypertensive (DOCA-Sham), hypertensive with ARDN (DOCA-ARDN). Mice received a nephrectomy, sham or ARDN, implant of 50mg DOCA and 0.9% saline drinking water for the length of the study (drug-free pellet and diH2O for vehicle). TRAP2-DOCA-salt mice received an injection of 5mg/mL 4-hydroxytamoxifen (4-OHT), a short acting tamoxifen metabolite, on day 7 to induce Cre recombination and expression of tdTomato during the development of HTN. On day 21, mice were anesthetized and perfused with 4% paraformaldehyde. Brains were immunolabeled for c-Fos expression indicating day 21 neuronal activation and compared to endogenous Cre-induced tdTomato expression from day 7 4-OHT. Preliminary data indicate increased neuronal activity in TRAP2 DOCA-Sham mice compared to Vehicle-Sham in the paraventricular nucleus of the hypothalamus, lateral parabrachial nucleus, and nucleus of the solitary tract. This activation pattern was reduced in TRAP2 DOCA-ARDN compared to TRAP2 DOCA-Sham mice. A future study will evaluate c-Fos expression in wild-type DOCA-salt mice at day 7 and 21 of DOCA-salt HTN development to validate the observations in the TRAP2-DOCA model. This experiment will also validate TRAPing as a method for measuring neuronal activation at two time points within the same animal. These preliminary results establish an anatomical foundation for future investigations into the physiological role of ARN input to central autonomic control centers that regulate blood pressure in a salt-induced hypertensive model. NIH R01 HL116476. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A Dynamic Threat Prevention Framework for Autonomous Vehicle Networks based on Ruin-theoretic Security Risk Assessment

In recent years, Autonomous Vehicle Networks (AVNs) have gained significant attention for their potential to make transportation safer and more efficient. These networks rely on Vehicle-to-Vehicle (V2V) communication to exchange critical information, such as location, speed, and driving intentions. However, V2V communication also introduces security vulnerabilities that can be exploited to compromise the safety and privacy of drivers and passengers. Malicious or selfish drivers can potentially intercept, modify, and manipulate V2V communication, causing confusion among vehicles or stealing sensitive data. Therefore, in order to identify and mitigate security threats that could jeopardize V2V communication in AVNs, the implementation of a threat prevention framework is imperative. This paper presents a threat prevention framework that assesses security risks dynamically to facilitate secure message forwarding in V2V communication. First, we propose a dynamic risk assessment technique that utilizes the PIER approach to evaluate the level of security threats posed to V2V communication, and ultimately generate a risk score. Second, we develop a security decay assessment method that utilizes ruin theory to continuously monitor security risk within the AVNs. Third, we design a risk-aware message forwarding protocol based on coalitional game theory to facilitate secure V2V communication. Our experiments using the simulator Veins demonstrate the efficiency and scalability of the proposed framework in preventing potential damage caused by common security threats and enhancing the security of the Automated Highway System (AHS).

Pelvic Neuro-Visualization: An Anatomical Illustration of the Autonomic Pelvic Nervous Network in Gynecologic Surgery

ObjectiveDuring radical pelvic surgeries fibers of the autonomic pelvic nervous network can be accidentally damaged leading to significant visceral sequelae, which dramatically affect women's quality of life because of urinary, anorectal, and sexual postoperative dysfunctions.1,2 Direct visualization is one way to preserve hypogastric nerves (HNs), pelvic splanchnic nerves (PSNs), and the bladder branches from the inferior hypogastric plexus (IHP). However, the literature lacks critical photos and/or illustrations that are necessary to understand the precise anatomy needed to preserve the pelvic autonomic fibers. DesignNarrated laparoscopic video footage for identifying, dissecting, and preserving the autonomic nerve bundles during pelvic surgery. SettingTertiary level hospital—“IRCCS Istituto Nazionale dei Tumori”, Milano, Italy. InterventionsVisceral pelvic innervation is established by the superior hypogastric plexus(SHP) located anteriorly to the aortic bifurcation and the median sacral vessels and carries mostly sympathetic fibers. SHP divides in front of the sacrum into the right and left HN. At the level of the paracervix, the HNs join the parasympathetic PSNs coming out from sacral root S2, S3, S4 to form the IHP.2–5 Here, we performed laparoscopic surgery, before “Laparoscopic Approach to Cervical Cancer” trial (LACC) era, identifying key anatomic landmarks useful to highlight the path of the most commonly encountered autonomic pelvic nerves in gynecologic radical surgery: during the narration we describe and illustrate the procedure to identify all autonomic pelvic nerves, the sympathetic fibers, the PSNs, and the bladder branch emerging from the IHP in order to preserve their anatomic and functional integrity. This technique is anatomically and surgically indicated for adequate removal of the parametrical issues and vagina while preserving the total pelvic nervous system. ConclusionNerve-sparing surgery reduces bowel-, bladder- and sexual­ dysfunction without decreasing surgical efficacy.1,2 To accomplish safe and effective surgery, comprehension of the 3 dimensional structure of the vascular and nerve anatomy in the pelvis is essential. This video provides a great resource to educate surgeons, especially the youngest ones, about the retroperitoneal nervous networking: we identified the autonomic nerve pathway from adjacent tissues along the pathway consisting of cardinal, sacro-uterine, rectouterine/vaginal, and vesico-uterine ligaments.

CANDIL: A federated data fabric for network analytics

The availability of data sources during the Big Data era provides the opportunity for new analytical applications in the networking domain, which are envisioned as one of the main enablers of the future autonomous networks. But the proliferation of heterogeneous data sources has resulted into a sea of data silos, in which finding data, understanding data, and dealing with the complexities of each data source becomes a challenge. Aiming to tackle the connection of data silos, the data fabric has appeared as a new paradigm that provides a uniform access to all the data, abstracting consumers from the underlying complexities of the data sources. In this regard, the knowledge graph has raised as a promising solution that can integrate data from heterogeneous silos based on common concepts captured in ontologies. Building upon knowledge graph standards, this paper introduces CANDIL, a federated data fabric to support the integration of data from distributed systems, mainly focused on networking domain aspects. CANDIL defines an ontology that captures network topology and interface concepts, along with a reference architecture to ingest and integrate data in a federated knowledge graph that spans across the Edge-Cloud continuum. The proposal is validated with a prototype implementation and two example use cases of network analytics.

Autonomous UAV Networks Based on Artificial Intelligence

Autonomous UAV Networks Based on Artificial Intelligence

A Moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles

The evolution of Autonomous Vehicles (AVs) has blurred the distinction between drivers and passengers, resulting in increased demand for in-car entertainment. Simultaneously, social networking platforms are evolving to adopt AR (Augmented Reality)/VR (Virtual Reality)-centric metaverses. Self-driving cars will free up the drivers and passengers to engage in leisure activities such as watching VR videos and playing games while travelling. Before driverless cars can become a “third living space”, open challenges confront the provision of immersive content (e.g., VR videos and gaming) with a high level of Quality of Experience (QoE). Current autonomous vehicular network technologies focus on navigation and safety and rely on ubiquitous high-speed 5G/6G network connectivity to provide a seamless streaming experience in AVs. The compounded risks of cyber- and motion-sickness are identified as barriers to the enjoyment of in-car VR entertainment technologies. The influence of AV car networks in different traffic conditions is identified as a key factor to minimize a person’s risk of sickness and increase overall QoE. However, assessing the QoE of immersive media consumption in AV to its full potential is a difficult task that requires proper standard guidelines and directions. Existing surveys focus only on a specific metaverse or transportation system. These surveys lack a comprehensive view of the entire immersive media consumption process, QoE factors and aspects, and QoE standards for assessing immersive content in vehicular networks. To address these issues, we present an overview of the research landscape in the context of QoE and network standards that highlights the need for experimental studies to inform and consolidate the methodologies and recommendations for standardization of immersive content QoE assessment in AVs. Research and user studies have not yet yielded standards or recommendations describing assessment methodologies to evaluate immersive systems, such as exist for traditional video and images. A framework is outlined to provide a comprehensive understanding of the QoE influencing factors and aspects that will assist in adapting, augmenting, and producing standards for immersive entertainment in autonomous vehicles.

Cardiac Rhythm Aberrations in Subacute Sclerosing Panencephalitis: Insights From Heart Rate Variability Analysis.

Subacute sclerosing panencephalitis (SSPE) is a fatal neurological disorder resulting from persistent measles virus infection within the brain. Although neurological manifestations have been well-documented, the impact of SSPE on cardiac autonomic function, assessed through heart rate variability (HRV), remains understudied. In this prospective single-center study conducted from January 2022 to March 2023 in Southern India, 30 consecutive SSPE patients and age- and sex-matched controls underwent electrocardiogram recordings for HRV analysis. Various HRV parameters were assessed, including time-domain metrics (SD of normal-to-normal intervals, root mean square of successive differences between normal heartbeats, percentage of successive normal interbeat intervals greater than 50 msec), SD1 and SD2 for Poincaré plot analysis, and frequency-domain metrics (low frequency %, high frequency %, low frequency:high frequency ratio). In the study, SSPE patients exhibited markedly reduced HRV. Specifically, SD of normal-to-normal intervals (P = 0.003), percentage of successive normal interbeat intervals greater than 50 msec (P = 0.03), and SD2 (P = 0.0016) were significantly lower compared with controls. Frequency-domain analysis did not reveal significant distinctions. Correlation analysis demonstrated a negative relationship between percentage of successive normal interbeat intervals greater than 50 msec and SSPE severity (r = -0.37, P = 0.042). Heart rate variability did not significantly differ between SSPE stages or with clinical variables. The interbeat interval range showed a narrower distribution in SSPE subjects. Our study highlights the clinical relevance of HRV analysis in SSPE and autonomic dysfunction throughout the disease course underscoring its importance in SSPE. This investigation provides valuable insights into cardiac autonomic dysfunction probably because of affliction of the central autonomic networks caused by the disease process and may be a contributing factor to mortality in SSPE.

L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor Environments

Robotic factory floors will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, these robots will need low-latency, reliable connectivity throughout the work zone through links established by wireless access points (APs). This will allow the robot to assuredly respond to programming directives that rely on the real-time relaying of robot-generated sensor data to the Mobile Edge Computing (MEC) server. In this paper, we propose L-NORM, a multi-AP and multi-robot coordination framework, as a multi-tiered solution for such autonomous edge networks. First, multi-robot motion planning through reinforcement learning occurs at the MEC, using as input multi-modal robot sensor data. Second, multi-AP resource orchestration is performed using another reinforcement learning-based method that maps a subset of available APs to each robot toward meeting their sensor data delivery requirements. Furthermore, we suggest diversity combination of uplink channels with the 802.11ax scheduled access mode that will (i) support high reliability of multi-robot uplink sensor packets and (ii) enable multi-AP coordination, for optimized resource utilization. Through extensive simulation studies, we show that the probability of robot deviation to remain within 0.5 m from its optimal path, is 19% more in L-NORM compared to classical 802.11ax based edge network solution, considering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1 MB of sensor data per robot.

Hate Speech Detection

Abstract: The rise in popularity of microblogging sites such as Facebook, Instagram, and Twitter has resulted in more people from different backgrounds indirectly communicating with one another. Our study aims to design an autonomous Deep Neural Network (DNN) algorithm for social media hate speech detection to tackle this problem. Using cutting-edge Natural Language Processing (NLP) techniques, the objective is to build a strong system that can recognize and categorize hate speech material in text data with accuracy. Our DNN algorithm allows for the real-time detection and moderation of offensive information, providing a proactive strategy against online hate speech. With the deployment of this technology, everyone will be able to access a safer and more welcoming online environment.

Eco-friendly lane reservation-based autonomous truck transportation network design

As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an ϵ-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the ϵ-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis.

Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls

Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated.

Evaluating the impact of microservice-centric computations in internet of vehicles

This paper presents the design and implementation of a hierarchical and multilayered computing framework, specifically tailored to analyze the benefits of Named Data Networking (NDN) enabled microservice-centric in-network computations in autonomous vehicular networks. The proposed framework comprises three layers: an autonomous vehicular network layer, an Edge computing layer, and a centralized Cloud.Modifications were made to the vanilla NDN codebase to facilitate communication between the vehicular network layer, Edge computing and Cloud layer, utilizing the C++ Boost Asio library and IP tunneling mechanism. A microservices-based driver assistance application, consisting of various heterogeneous microservices, was emulated in .NET Core and deployed on different Edge computing terminals and the Cloud. Additionally, RESTful APIs have been developed to enable the vehicular network layer and the physical Edge layer to offload microservice-centric computation requests and retrieve the corresponding computational outcomes. The Entity framework is employed to ensure proper tracking and management of these requests. An HTML-based user interface has also been developed for a visual representation of the request pattern. Extensive testbed experiments reveal that the proposed system significantly optimizes bandwidth consumption, reduces latency, and increases the computed results delivery ratio when compared to conventional monolithic systems.

Implementation of a Wireless Sensor Network for Environmental Measurements

Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable approach to climate monitoring, daily parcel monitoring, early disease detection, crop plant counting, and risk assessment. Herein, an autonomous energy wireless sensor network for monitoring environmental variables is proposed. The network’s tree topology configuration, which involves master and slave modules, is managed by microcontrollers embedded with sensors, constituting a key part of the WSN architecture. The system’s slave modules are equipped with sensors for temperature, humidity, gas, and light detection, along with a photovoltaic cell to energize the system, and a WiFi module for data transmission. The receiver incorporates a user interface and the necessary computing components for efficient data handling. In an open-field configuration, the transceiver range of the proposed system reaches up to 750 m per module. The advantages of this approach are its scalability, energy efficiency, and the system’s ability to provide real-time environmental monitoring over a large area, which is particularly beneficial for applications in precision agriculture and environmental management.

Sympathetic and parasympathetic central autonomic networks

Abstract The central-autonomic network (CAN) comprises brain regions that are functionally linked to the activity of peripheral autonomic nerves. While parasympathetic CAN (i.e., the CAN projecting onto parasympathetic branches) has recently been investigated and is known to be involved in neurological and neuropsychiatric disorders, sympathetic CAN (i.e., the CAN projecting onto sympathetic nerves) has not been fully characterized. Using functional magnetic resonance imaging (fMRI) data from the Human Connectome Project in conjunction with heartbeat dynamics and its orthonormal autoregressive descriptors as a proxy for sympathetic activity estimation, namely, the sympathetic activity index (SAI), we uncover brain regions belonging to the sympathetic CAN at rest. We uncover a widespread CAN comprising both cortical (in all lobes) and subcortical areas, including the cerebellum and brainstem, which is functionally linked to sympathetic activity and overlaps with brain regions driving parasympathetic activity. These findings may constitute fundamental knowledge linking brain and bodily dynamics, including the link between neurological and psychiatric disorders and autonomic dysfunctions.

Effects of cortisol administration on heart rate variability and functional connectivity across women with different depression histories

Abnormalities within the hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system have been implicated in depression. Studies have reported glucocorticoid insensitivity and reduced heart rate variability (HRV) in depressive disorders. However, little is known about the effects of cortisol on HRV and resting-state functional connectivity (rsFC) of the central autonomic network (CAN) in depression. We collected resting-state fMRI and cardiac data for women with different depression histories (n = 61) after administration of cortisol and placebo using a double-blind crossover design. We computed rsFC for R-amygdala and L-amygdala seeds and assessed the change in HRV after cortisol (cortisol-placebo). Analyses examined the effects of acute cortisol administration on HRV and rsFC of the R-amygdala and L-amygdala. There was a significant interaction between HRV and treatment for rsFC between the amygdala and CAN regions. We found lower rsFC between the L-amygdala and putamen for those with a greater decrease in HRV after cortisol. There was also reduced rsFC between the R-amygdala and dorsomedial prefrontal cortex, putamen, middle cingulate cortex, insula, and cerebellum in those with lower HRV after cortisol. These results remained significant after adjusting for depression symptoms, age, and race. Our findings suggest that the effect of cortisol on CAN connectivity is related to its effects on HRV. Overall, these results could inform transdiagnostic interventions targeting HRV and the stress response systems across clinical and non-clinical populations.

Gradient-Based Optimization for Intent Conflict Resolution

The evolving landscape of network systems necessitates automated tools for streamlined management and configuration. Intent-driven networking (IDN) has emerged as a promising solution for autonomous network management by prioritizing declaratively defined desired outcomes over traditional manual configurations without specifying the implementation details. This paradigm shift towards flexibility, agility, and simplification in network management is particularly crucial in addressing inefficiencies and high costs linked to manual management, notably in the radio access part. This paper explores the concurrent operation of multiple intents, acknowledging the potential for conflicts, and proposes an innovative reformulation of these conflicts to enhance network administration effectiveness. Following the initial detection of conflicts among intents using a gradient-based approach, our work employs the Multiple Gradient Descent Algorithm (MGDA) to minimize all loss functions assigned to each intent simultaneously. In response to the challenge posed by the absence of a closed-form representation for each key performance indicator in a dynamic environment for computing gradient descent, the Stochastic Perturbation Stochastic Approximation (SPSA) is integrated into the MGDA algorithm. The proposed method undergoes initial testing using a commonly employed toy example in the literature before being simulated for conflict scenarios within a mobile network using the ns3 network simulator.

Blockchain for the digital twin-driven autonomous optical network

In recent years, optical networks have become increasingly large-scale and complex, resulting in mounting pressures on operation and maintenance management. The techniques of the artificial intelligence (AI)-driven digital twin (DT) have been applied to optical networks to pave the way for creating the self-optimizing and self-controlling autonomous optical network (AON). However, the AON is confronted with many security and reliability issues: the centralized architecture is prone to data loss during system disconnection and active/standby switchover, and the massive data, DT models, and control instructions in the AON all face the risk of being tampered with, directly affecting the effectiveness of network regulation. In addition, the reliability of complex autonomous processes should be ensured. To address these challenges, in this paper, we applied blockchain to a DT-driven AON with the designed architecture and deployment scheme to realize data loss prevention, anti-tamper storage, secure autonomous operations, access control, and network element controller decisions. A blockchain-based solution was studied by considering the quality of transmission (QoT) estimation and link optimization as examples. Four chaincodes were designed and developed to perform the model storage, service definition, telemetry subscription, data collection, model implementation, and instruction delivery in the AON. Furthermore, the proposed solution was developed and deployed using Hyperledger Fabric. The Hyperledger Caliper was used to test the performance of the proposed blockchain system. The distributed environment is used for performance testing to simulate the real scenario. The test results are analyzed to indicate the future development direction.

Central Autonomic Mechanisms Involved in the Control of Laryngeal Activity and Vocalization.

In humans, speech is a complex process that requires the coordinated involvement of various components of the phonatory system, which are monitored by the central nervous system. The larynx in particular plays a crucial role, as it enables the vocal folds to meet and converts the exhaled air from our lungs into audible sounds. Voice production requires precise and sustained exhalation, which generates an air pressure/flow that creates the pressure in the glottis required for voice production. Voluntary vocal production begins in the laryngeal motor cortex (LMC), a structure found in all mammals, although the specific location in the cortex varies in humans. The LMC interfaces with various structures of the central autonomic network associated with cardiorespiratory regulation to allow the perfect coordination between breathing and vocalization. The main subcortical structure involved in this relationship is the mesencephalic periaqueductal grey matter (PAG). The PAG is the perfect link to the autonomic pontomedullary structures such as the parabrachial complex (PBc), the Kölliker-Fuse nucleus (KF), the nucleus tractus solitarius (NTS), and the nucleus retroambiguus (nRA), which modulate cardiovascular autonomic function activity in the vasomotor centers and respiratory activity at the level of the generators of the laryngeal-respiratory motor patterns that are essential for vocalization. These cores of autonomic structures are not only involved in the generation and modulation of cardiorespiratory responses to various stressors but also help to shape the cardiorespiratory motor patterns that are important for vocal production. Clinical studies show increased activity in the central circuits responsible for vocalization in certain speech disorders, such as spasmodic dysphonia because of laryngeal dystonia.

Transforming transportation: Embracing the potential of 5G, heterogeneous networks, and software defined networking in intelligent transportation systems

&lt;p&gt;Intelligent transportation systems (ITS) emphasise the significance of vehicle networks. The growing need for services that are safer, more effective, more affordable, infotainment-focused, and sustainable, however, presents difficulties for these networks. To create innovative applications, researchers and businesses are working. Through effectively coordinating vehicle operations, ITS promotes safe driving, efficient traffic flow, and effective route planning. Referring to automobile heterogeneous, autonomous, flexible, and programmable networks is important given the requirement for convergence of technology in communications. For research and network development, new emerging technologies present intriguing gaps. In this paper, we provide an analysis of wireless technology and potential obstacles to delivering vehicle-to-x communication; including linked cars or autonomous vehicles, which that the initial robot to directly impact the everyday Millions of lived individuals. Study investigates the conceptual change in transportation made possible by the incorporation of modern technology into Intelligent Transportation Systems (ITS), including 5G, heterogeneous networks (HN), and Software Defined Networking (SDN). The incorporation of 5G ensures unparalleled velocity and minimal latency, allowing instantaneous communication between automobiles and infrastructure. Vehicles are easily switched between several network technologies due to heterogeneous networks’ seamless communication structure. Technology developments generated an important increase in the worldwide ITS market from 2018 to 2025. During the same time, the global market for SDN increased significantly, indicating a rising to need for programmable and dynamic network infrastructures. The simultaneous growth patterns in the SDN and ITS industries between 2018 and 2025 indicate to a general shift in the sector toward more intelligence and connectivity. It is predicted that this development continues for future. We pay particular attention to the SDN used in the 5G architecture and how it affects HN.&lt;/p&gt;

Brain-IT: Targeting the brain using information technology for secondary prevention of mild neurocognitive disorder

Brain-IT: Targeting the brain using information technology for secondary prevention of mild neurocognitive disorder