Self-healing Networks Research Articles

Epoxy-based vitrimeric semi-interpenetrating network/MXene nanocomposites for hydrogen gas barrier applications.

Herein, we report MXene-filled epoxy-based vitrimeric nanocomposites featuring a semi-interpenetrating network (S-IPN) to develop a hydrogen gas (H2) barrier coating with self-healing characteristics for compressed H2 storage applications. The reversible epoxy network was formed by synthesizing linear epoxy chains with pendent bis-hydroxyl groups using amino diol, which were then crosslinked with 1,4-benzenediboronic acid to generate dynamic boronic ester linkages. To achieve the S-IPN-type molecular arrangement, the epoxy chains were in situ crosslinked in the presence of poly(ethylene-co-vinyl alcohol) (EVOH), giving rise to a self-healing network (EEP) with a healing efficiency of 87%. Into the S-IPN vitrimer (EEP), a 2D platelet-type nanofiller MXene was incorporated to introduce a tortuous path for H2 gas diffusion along with improved mechanical properties. The nanocomposite coating was applied to nylon 6 liner material, which is conventionally used in all-composite H2 storage vessels. The application of a 2 wt% MXene/EEP nanocomposite coating showed a permeability coefficient of 0.062 cm3 mm m-2 d-1 atm-1 exhibiting ∼96% reduction in gas permeability compared to uncoated nylon 6. The same nanocomposite exhibited a healing efficiency of 79%. Increasing the MXene loading to 10 wt% further reduced the permeability coefficient to 0.002 cm3 mm m-2 d-1 atm-1; however, the healing efficiency decreased due to restricted chain mobility. In essence, the current work highlights the potential of vitrimeric S-IPN nanocomposite coatings for H2 gas-barrier applications, enhancing safety and performance.

Revolutionizing Fault Detection in Self-Healing Network via Multi-Serial Cascaded and Adaptive Network

Self-Healing Network (SHN) plays a crucial role in the realm of digital networking and the telecommunications industry. Fault detection in SHN is a critical aspect of network management and maintenance, which acts as a pivotal role in maintaining network health and minimizing disruptions. The SHN networks aim to manage faults and system failures by automating the detection process and pinpointing their origins. This proactive approach is essential to maintain network integrity and ensure a seamless user experience. In this paper, a novel multi-Serial cascaded and Adaptive Network based fault Detection in SHN (SAND-SHN) technique has been proposed for detecting faults in the SHN network. The proposed method uses the Eigen-Entropy Synthetic Minority Oversampling Technique (EE-SMOTE) to balance imbalanced data for classification and the Multi-Serial Cascaded and Adaptive Network (MSCAN), which integrates deep learning (DL) techniques to attain the final classified outcome. The proposed SAND-SHN method has been evaluated using a Python environment in terms of specific parameters such as accuracy, precision, recall, specificity, and F1-Score. The proposed technique has been evaluated using two datasets as EFCD dataset, and the SFDD dataset. The proposed SAND-SHN technique achieves a higher accuracy of 74% for RSO-MSCAN, 39.2% for DMO-MSCAN, 48.8% for BFGO-MSCAN, and 43.6% for FHO-MSCAN respectively.

Synthesis of room-temperature self-healing network polymers based on multiple metal–ligand coordination interactions

Synthesis of room-temperature self-healing network polymers based on multiple metal–ligand coordination interactions

Autonomic Resilience in Cybersecurity: Designing the Self-Healing Network Protocol for Next-Generation Software-Defined Networking

Autonomic Resilience in Cybersecurity: Designing the Self-Healing Network Protocol for Next-Generation Software-Defined Networking

LLZTO crosslinks form a highly stretchable self-healing network for fast healable all-solid lithium metal batteries

Flexible composite solid electrolytes (CSEs) show great potentials in high-energy all-solid-state lithium metal batteries owing to their easy fabrication, good electrochemical properties, and high safety. However, it remains challenging to achieve good interfacial compatibility between the inorganic fillers and polymer matrix, which affects the lithium-ion transport efficiency and electrochemical performances of CSEs. Herein, a dynamic crosslinked network with Li6.4La3Zr1.4Ta0.6O12 (LLZTO) nanoparticles as the cross-linking point is proposed for fabrication of a rapidly self-healing flexible CSE, which is signed as SHENE. The amino groups in the 3-aminopropyltriethoxysilane modified LLZTO behave as the bridge sites to react with the copolymer of 2-hydroxyethyl methacrylate-g-4-formylbenzoic acid and poly (ethylene glycol) methoxy acrylate to form imine bonds, which effectively connect the organic/inorganic interface. Compared to the traditional physical contact, the covalent imine bonds can not only enhance the interaction between the two phases, but also improve the dispersion of the ceramic LLZTO nanoparticles. Additionally, the dynamic reversibility of imine bonds allows for enhanced mechanical strength and self-repairing capabilities of SHENE. Therefore, a Li||Li symmetrical cell assembled with SHENE can stably cycle for 5000 h at 0.05 mA cm−2 and 25 °C. The corresponding Li/SHENE/LiFePO4 full battery also exhibits good rate performance under various C-rates. Additionally, the Li/SHENE/LFP pouch cell exhibits superior safety under various abuse conditions. Therefore, this work provides new ideas for the uniform dispersion of ceramic nanoparticles in CSEs by the facile design of forming dynamic crosslinked networks.

GH-Twin: Graph Learning Empowered Hierarchical Digital Twin for Optimizing Self-Healing Networks

Communication networks are witnessing a fast evolution towards Beyond 5G (B5G), bringing unprecedented complexities and challenges for optimizing networks in guaranteeing self-healing abilities and maintaining quality of services (QoS). To this end, this study presents a Graph Learning-driven Hierarchical Digital Twin framework, called GH-Twin, to build a reliable virtual replica of network components and their communications between different layers, leading to inclusive network representation. The proposed framework introduces graph cross-learning (GCL) distributed across different participants to devise competent predictive modelling of network performance collaboratively and preemptively recognize abnormalities in network settings. To preserve local privacy, differential privacy is applied by injecting some Gaussian into the parameters of local GCL before sharing it with the global coordinator. Proof of concept simulations has demonstrated that GH-Twin can precisely predict flow-level QoS and recognize anomalous links and nodes under different network topologies.

Multi-scale analysis of virgin/aged/recycled asphalt self-healing performance based on molecular simulation and micro-macro tests

Conventional macro-micro experiments have not fully elucidated the thermal self-healing mechanism of virgin, aged, and recycled asphalt at the molecular level at different temperatures. Molecular dynamics and macro-micro experiments were used to investigate the multi-scale mechanism of thermal self-healing of nano-cracks in three asphalt types. The molecular characteristics (concentration, energy changes, and molecular diffusion properties) of the three asphalt types were simulated during self-healing using molecular dynamics simulations Macro-micro experimental tests were conducted to validate the simulation results. The self-healing process was analyzed at multiple scales, and correlation analysis between the self-healing parameters and the macroscopic healing characteristics was performed at nano- and microscopic scales. The results showed that the relative concentration increased significantly from 0 mol/L to 0.6 mol/L in the 60–80 Å range during the long-distance self-healing phase (0–40 ps), promoting the rapid reorganization of molecules around cracks and the formation of self-healing networks. The van der Waals forces were the dominant forces affecting self-healing, and the transition from repulsive to attractive forces promoted the self-healing of cracks. This study clarifies the thermal self-healing mechanism of asphalt cracks at multiple scales, ensuring the sustainable use of asphalt materials.

Design of an Iterative Method for Hybrid Device-to-Device and Network Integration via Dynamic Spectrum Sharing and Intelligent Resource Management

In the evolving landscape of wireless communication, the integration of Device-to-Device (D2D) communication within traditional network infrastructures emerges as a pivotal solution to meet the burgeoning demand for efficient, scalable, and reliable communication. Despite the promise of D2D to augment network capabilities, existing frameworks often falter in optimizing resource utilization, ensuring privacy and security, and maintaining high-quality service amidst dynamic network conditions and escalating data traffic. Addressing these limitations, this work introduces a comprehensive hybrid model that synergizes D2D communication with network integration through the innovative application of Dynamic Spectrum Sharing (DSS), Reinforcement Learning (RL)-based Resource Allocation, Mobile Edge Computing (MEC), Differential Privacy Techniques, Swarm Intelligence-based Routing, Cognitive Radio Networks, Self-Healing Network Protocols, and Low-Power Communication Protocols. DSS is employed to dynamically allocate spectrum, enhancing communication flexibility between D2D and network modes. RL algorithms adaptively manage resources, optimizing network performance in real-time scenarios. MEC decentralizes data processing, slashing latency and bandwidth demands. Differential privacy fortifies data security within D2D interactions in different communication scenarios. Swarm intelligence and cognitive radios bolster network resilience and spectral efficiency through self-organized routing and adaptive spectrum access. Self-healing protocols and energy-efficient designs ensure robust, sustainable network operations. The proposed model markedly elevates network efficiency, slashes latency, and augments scalability by intelligently offloading traffic, distributing computing tasks, and preserving user privacy. Its implementation fosters a resilient, self-organizing network fabric capable of meeting contemporary demands. The impacts of this work are profound, offering a blueprint for next-generation wireless networks that are more adaptive, secure, and sustainable, thereby significantly enhancing user experience and trust in D2D communication systems.

Self-Healing Networks AI-Based Approaches for Fault Detection and Recovery

The reliability and robustness of communication networks are critical for ensuring seamless connectivity in today's interconnected world. However, network faults and failures can disrupt services, leading to significant downtime and financial losses. [1]In response to these challenges, self-healing networks empowered by artificial intelligence (AI) have emerged as a promising solution for proactively detecting and autonomously recovering from network faults. This paper presents a comprehensive review and analysis of AI-based approaches for fault detection and recovery in self-healing networks, aiming to enhance network reliability and minimize service disruptions. It provides an overview of the key challenges associated with traditional fault detection and recovery methods in communication networks.[2] It discusses the limitations of reactive approaches that rely on manual intervention or predefined rules to identify and mitigate network faults, highlighting the need for more proactive and adaptive solutions. Subsequently, the paper explores the role of artificial intelligence, particularly machine learning and deep learning techniques, in enabling self-healing capabilities in communication networks. By leveraging vast amounts of network data, AI algorithms can learn complex patterns and anomalies indicative of network faults, enabling timely detection and response. The paper reviews various AI-based fault detection techniques, including anomaly detection, supervised learning, and reinforcement learning approaches. It examines their strengths and limitations in different network environments, considering factors such as data availability, scalability, and real-time processing requirements. Furthermore, the integration of AI-based fault detection with network monitoring systems and telemetry data sources is discussed, highlighting the importance of comprehensive data-driven approaches for accurate fault identification. In addition to fault detection, the paper investigates AI-driven recovery mechanisms for self-healing networks. It explores proactive recovery strategies, such as predictive maintenance and network reconfiguration, enabled by AI-based predictive analytics. By harnessing the power of machine learning and deep learning, communication networks can achieve greater resilience, adaptability, and autonomy in mitigating network faults and ensuring uninterrupted service delivery.

Mobile Adhoc Network Risk Profiles - An Overview of Existing Network Traffic Datasets to DetermineIdeal Axiom Criteria

A Mobile Adhoc networks also known as MANET or Wireless Adhoc Network is a network that usually has aroutable networking environment on top of a Link Layer ad hoc network. It consist of a set of mobile nodes connected wirelessly in a self-configured, self-healing network without having a fixed infrastructure. Recent studies and fieldwork have pointed in the direction of making MANETS a publicly viable option in the event of another world event/crisis such as the recent COVID-19 pandemic. As opposed to their traditional military and emergency uses, this has become a focal point due to the evident strain that was observed on mainstream Internet Service Providers as substantial adjustments had to be made to facilitate a new ’working-from-home’ public. A primary aspect that must be considered before public adoption is addressing the issue of MANET risk and Security which leads into identifying and classifying risks associated with MANETS.

The Post-Curing of Waterborne Polyurethane-Acrylate Composite Latex with the Dynamic Disulfide-Bearing Crosslinking Agent.

The development of a dynamic network for commodity polymer systems via feasible methods has been explored in the context of a society-wide focus on the environment and sustainability. Herein, we demonstrate an adaptive post-curing method used to build a self-healable network of waterborne polyurethane-acrylate (WPUA) composite latex. The composite latex was synthesized via the miniemulsion polymerization of acrylates in the dispersion of waterborne polyurethane (PU), with commercial acetoacetoxyethyl methacrylate (AAEM) serving as the functional monomer. Then, a dynamic disulfide (S-S)-bearing diamine was applied as the crosslinking agent for the post-curing of the hybrid latex via keto-amine condensation, which occurred during the evaporation of water for film formation. It was revealed that the microphase separation in the hybrid films was suppressed by the post-curing network. The mechanical performance exhibited a high reliability as regards the contents of the crosslinking agents. The reversible exchange of S-S bonds meant that the film displayed associative covalent-adaptive networks in the range of medium temperature in stress relaxation tests, and ≥95% recovery in both the stress and the strain was achieved after the cut-off films were self-healed at 70 °C for 2 h. The rebuilding of the network was also illustrated by the >80% recovery in the elongation at break of the films after three crushing-hot pressing cycles. These findings offer valuable insights, not only endowing the traditional WPUA with self-healing and reprocessing properties, but broadening the field of study of dynamic networks to polymer hybrid latex.

FedFog - A federated learning based resource management framework in fog computing for zero touch networks

Fog computing offers an optimal answer to the expansion challenge of today’s networks. It boasts scaling and reduced latency. Since the concept is still nascent, many research questions remain unanswered. One of these is the challenge of Resource Management. There is a pressing need for a reliable and scalable architecture that meets the Resource Management challenge without compromising the Quality of Service. Among the proposed solutions, Artificial Intelligence based path selection techniques and automated link detection methods can provide lasting and reliable answer. An optimal approach for introducing intelligence in the networks is the infusion of Machine learning methods. Such futuristic, intelligent networks form the backbone of the next generation of Internet. These self-learning and self-healing networks are termed as the Zero-Touch networks. This paper proposes FedFog, a Federated Learning based optimal, automated Resource Management framework in Fog Computing for Zero-touch Networks. The paper describes a series of experiments focusing on Quality of Service parameters such as Network latency, Resources processed, Energy consumption and Network usage. The simulation results from these experiments depict superiority of the proposed architecture over traditional, existing architecture.

Room-temperature self-healing polysiloxane networks for highly sensitive piezoresistive pressure sensor with microdome structures

Soft piezoresistive pressure sensor is an important component for next generation flexible electronics. Micropatterning of such sensors significantly improves the pressure sensitivity, however, the short device lifetime due to scratches and motions limits the translation to industrial applications. Herein, we report a novel poly(ODMS-co-MAA) for self-healing piezoresistive sensor to effectively overcoming such shortcoming. Self-healing capability was achieved by incorporating dynamic Fe(III)-carboxylate linkages in the polymer networks. These linkages work as sacrificial bonds and dissipate energy caused by mechanical damage. The bonds subsequently reform and functionality of the sensor can be restored. The poly(ODMS-co-MAA)-based self-healing polymer networks (SPN) were effectively healed from damages showing the recovery of electrical resistance (>95%) within 10 hrs. SPN was fabricated with microdome structures and exhibited a superior pressure sensitivity (2 kPa−1) as well as stability compared to planar features (9.13 × 10−3 kPa−1), demonstrating its excellent potential as pressure sensor. Combined results demonstrate the potential of the self-healing networks for next generation piezoresistive pressure sensors.

Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Wireless sensor networks (WSN) keep developing in recent days concerning the self-covered network, self-healing network, and association of system component circuit selections that enable the implementation process. Wireless sensor network lifetime stabilization is essential to providing a higher quality experience to consumers. The wireless sensor network is associated with classifiers that keep learning the data pattern and further modify the cluster selection to produce dynamic results. The presented system is focused on creating an efficient wireless sensor network in which cluster head selection is dynamically programmed to improve the life span of the devices. The energy utilized by each node is pre-programmed with random assignments. The values are configured by the machine learning techniques to improve the hop death. The models developed using the parameters help project energy consumption. The proposed system considers a support vector machine (SVM), and the Gaussian regression process (GRP) enabled the comparative study of lifespan analysis and support systems to make cluster selection efficient. The proposed model is used to test the current selection of cluster heads using a support rectangle machine and further modify the regression process using the Gaussian regression model. Evaluation of network lifetime and flexibility obtained in cluster selection.

AI-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments

The dynamic evolution of next-generation data centers, driven by cloud-native and hybrid architectures, has necessitated a paradigm shift in cybersecurity. Traditional security models, designed for static and on-premise environments, struggle to address the complexities of cloud-connected infrastructures and the rapidly evolving threat landscape. Emerging challenges, such as advanced persistent threats (APTs), ransomware, and insider attacks, demand sophisticated and adaptive security solutions. In this context, artificial intelligence (AI) emerges as a transformative technology capable of redefining threat detection and response mechanisms. This review explores the conceptualization of AI-driven security for next-generation data centers, focusing on autonomous threat detection and response. By leveraging AI and machine learning (ML), security systems can achieve real-time anomaly detection, advanced behavior analysis, and predictive risk assessment. These capabilities enhance the accuracy and speed of identifying malicious activities while reducing false positives. Additionally, autonomous response mechanisms, such as self-healing networks and adaptive security policies, enable rapid containment and mitigation of threats, minimizing potential damages. The review also discusses the integration of AI with existing Security Operations Centers (SOCs), highlighting its potential to augment human decision-making and automate repetitive tasks. Furthermore, it examines the role of advanced encryption, identity management, and compliance tools in fortifying security frameworks. Future trends, including the impact of 5G and edge computing, are explored, emphasizing their implications for real-time applications and IoT security. This study underscores the importance of proactive, AI-driven strategies in securing next-generation data centers, ensuring scalability, resilience, and robust protection in an increasingly interconnected digital landscape. By bridging the gap between cloud-native and on-premise environments, AI-powered security frameworks offer a promising path toward achieving autonomous, adaptive, and future-proof cybersecurity.

Fuzzy-ZRP: An Adaptive MANET Radius Zone Routing Protocol

A Mobile Ad-hoc Network (MANET) is a group of active mobile nodes wirelessly connected in a self-configuring and self-healing network without a preexisting centralized infrastructure. Several studies have been conducted to improve the stability and lifetime of routes for communicating between source and destination nodes, integrating new techniques with existing protocols. This paper presents a fuzzy-based approach to improve the performance of the standard Zone Routing Protocol (ZRP) by selecting the optimal value of the zone radius. Each node has a fuzzy inference system that is periodically fed with parameters, such as the remaining energy and mobility of the node, to calculate the optimal value of the zone routing radius, which makes the node autonomous and intelligent. The simulation results obtained using the NS-2 simulator showed that the proposed fuzzy radius approach outperformed the standard ZRP, OVBAZRP, and PSOZRP routing protocols in all measures considered: PDR, NRL, and E2ED.

Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review.

Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.

Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning

Today’s Network Operation Centres (NOC) consist of teams of network professionals responsible for monitoring and taking actions for their network’s health. Most of these NOC actions are relatively complex and executed manually; only the simplest tasks can be automated with rules-based software. But today’s networks are getting larger and more complex. Therefore, deciding what action to take in the face of non-trivial problems has essentially become an art that depends on collective human intelligence of NOC technicians, specialized support teams organized by technology domains, and vendors’ technical support. This model is getting increasingly expensive and inefficient, and the automation of all or at least some NOC tasks is now considered a desirable step towards autonomous and self-healing networks. In this article, we investigate whether an autonomous NOC can achieve superintelligence; i.e., it can recommend or take actions that lead to better results than those achieved by the collective human intelligence, in this case rules designed by human experts. Our investigation is inspired by the superintelligence achieved in computer games recently. Specifically, we build an Action Recommendation Engine based on Reinforcement Learning, a type of Artificial Intelligence method, and we train it with expert rules and lets it explore actions by itself. We then show that it can learn new and more efficient strategies that outperform expert rules designed by humans. This can be used in face of network problems to either quickly recommend actions to NOC technicians or autonomously take actions for fast recovery.

Extremely strengthening fatigue resistance, elastic restorability and thermodynamic stability of a soft transparent self-healing network based on a dynamic molecular confinement-induced bioinspired nanostructure.

Soft self-healing materials are crucial for the development of next-generation wearable electronics that could function in dynamic environments and resist mechanical damage. However, several challenges remain, including fatigue fracture, poor elasticity, and thermodynamic lability, which significantly limit their practical applications. Here, with a model system of soft self-healing polyurea, we propose a molecular engineering strategy of transforming inherently fragile materials with an island-like structure into resilient ones with a bicontinuous nanophase separation structure using 2-ureido-4-pyrimidinone (UPy) supramolecular motifs as structural regulators. The dynamic and continuous hard domains modified by UPy formed a repairable bicontinuous network similar to those of the reticular layer in animal dermis. This design allows for a simultaneous and tremendous improvement in the fatigue threshold (34.8-fold increase), elastic restorability (the maximum elongation for full dimensional recovery increasing from 6 times to 13 times), and thermodynamic stability (4 orders of magnitude improvement in the characteristic flow transition relaxation time), without significantly compromising the compliance, autonomous self-healing, and optical transparency. These mechanical and thermodynamic improvements address current limitations in unfilled soft self-healing materials as reliable substrates for transparent strain-electronics.

A polyacrylamide–chitosan semi-interpenetrating self-healing network with embedded Keplerate {Mo132} for pH-controlled release of Eu-fluorescent tags

Using {Mo132} as a nanoscale template, we demonstrate a green synthetic strategy for the fabrication of injectable, self-healing hydrogels with pH-controlled release of an Eu-fluorescent tag under physiologically relevant conditions.