Network Resilience Research Articles

Explainable semantic wireless anomaly characterization for digital twins

The shift towards software-centric network infrastructures is driven by the increasing need for networks to be responsive, flexible, and scalable in the face of an expanding set of connected devices. The digital twin (DT) approach, mirroring physical entities in a digital format, has emerged as a key enabler of network reliability and availability. Incorporating artificial intelligence (AI) into DTs enhances the resilience of networks by providing in-depth analysis and increasingly automated mitigation strategies against operational disruptions. In this work, we propose a new AI-based information extraction module that is part of the DT Monitoring component able to process RSS data, extract and characterize abrupt anomalies. The output of this component is used to maintain an anomaly history in the Link Abstraction within the DT and subsequently inform possible automatic mitigation actions. We design the AI-based information extraction module to identify and characterize three types of RSS based anomalies. Our extensive performance analysis on 10 versions of the “You Only Look Once” architecture reveals that YOLOv8n produces a good tradeoff between performance and computational complexity. We show that our approach performs on par with the state of the art for anomaly detection, while enabling anomaly characterization by location, duration, and severity. By employing two SotA explainability algorithms, we also provide insights into the important regions of the input that trigger the selected model’s classification and characterization decisions.

Predictive resilience assessment of road networks based on dynamic multi-granularity graph neural network

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: (1) Simulation methods for modeling rainfall impacts lack realism; (2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial–temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios.

Trade Network Resilience of the Global Photovoltaic Industry Chain

Based on the trade data of the global photovoltaic (PV) industry chain from 2005 to 2021, this paper constructs a global PV industry chain trade network model and analyzes its static resilience and dynamic resilience characteristics, reaching the following conclusions. The static resilience of the trade network is analyzed through aggregation, transmission, hierarchy and matching. The upstream polysilicon trade network is relatively loose, while the midstream and downstream trade networks appear to aggregate significantly. The average path length is short and the average clustering coefficient is high, indicating that the global PV industry chain trade network has high transmission efficiency and small-world characteristics. The global PV industry chain trade network is hierarchical. The degree distribution slope of the upstream polysilicon trade network is the largest and has a high level of hierarchy. Meanwhile, the global PV industry chain trade network is a heterogeneous network, which strengthens the connection between the core countries and edge countries, making the global PV industry chain trade network more resilient. Regarding the dynamic resilience of the trade network, the deterministic disturbance strategies have a greater impact on network performance than the random disturbance strategies, indicating that targeted impacts exert greater impact on the resilience of the global PV industry chain trade network. For the deterministic disturbance strategy, the interference based on intermediate centrality has a higher impact on the overall resilience of the network than the interference based on degree centrality, thus the influence of the country with strong trade radiating ability on the network elasticity is greater than that of the country with strong transit ability.

DoS attacks resilience of heterogeneous complex networks via dynamic event-triggered impulsive scheme for secure quasi-synchronization

This paper addresses the secure quasi-synchronization issue of heterogeneous complex networks (HCNs) under aperiodic denial-of-service (DoS) attacks with dynamic event-triggered impulsive scheme (ETIS). The heterogeneity of networks and the aperiodic DoS attacks, which hinder communication channels and synchronization goals, present challenges to the analysis of secure quasi-synchronization. The ETIS leverages impulsive control and dynamic event-triggered scheme (ETS) to handle the network heterogeneity and the DoS attacks. We give specific bounds on the attack duration and frequency that the network can endure, and obtain synchronization criteria that relate to event parameters, attack duration, attack frequency, and impulsive gain by the variation of parameter formula and recursive methods. Moreover, we prove that the dynamic ETS significantly reduces the controller updates, saves energy without sacrificing the system decay rate, and prevents the Zeno phenomenon. Finally, we validate our control scheme with a numerical example.

Designing a resilient agriculture supply network for mitigating the disruptions

Designing a resilient agriculture supply network for mitigating the disruptions

Designing a resilient reverse network to manage the infectious healthcare waste under uncertainty: A stochastic optimization approach

The global expansion of healthcare facilities has resulted in increased levels of infectious-hazardous waste, posing serious threats to the environment and public health. Existing waste management systems can become overwhelmed during health crises, such as epidemics or natural disasters, exacerbating the problem. This study formulates a mixed-integer linear programming model for developing a resilient infectious waste management reverse network during the outbreak of the COVID-19 pandemic. Health crisis are unpredictable in nature and it is almost impossible to predict their exact behavior. Therefore, in this paper, the uncertainty of ambiguous parameters is considered using a scenario-oriented approach. To make the proposed model resilient, three strategies, including establishing new collection centers, overtime, and cooperation with third-party logistics, are introduced. The results derived from running the developed model in GAMS software using the case study data showed that the active collection center cannot serve the network alone, and to make the network more resilient, the strategy of establishing a new collection center should be selected. The findings validate the model’s utility in designing a resilient waste management network during a health crisis, emphasizing the importance of resilience in infectious healthcare waste management networks.

A three-level robust scenario-based model for resilience-oriented placement of remote-controlled switches in distribution networks

This paper introduces an innovative strategy to improve the resilience of Distribution Networks (DN) following natural disasters. The approach involves a multi-stage model for the strategic placement of Remote-Controlled Switches (RCS) to expedite fault isolation and service restoration. The model integrates various resilience enhancement techniques, such as network reconfiguration and microgrid formation, throughout the pre-disaster, disaster progression, and post-disaster phases. To mitigate uncertainties related to line failures and ensure planning robustness, a novel three-level Robust Scenario-based Model (RSM) is introduced. The RSM optimizes the strategic RCS placement to enhance System Performance Criteria (SPC) in response to anticipated line failure scenarios, considering individual line vulnerabilities and disaster severity. Subsequently, the model identifies the most critical failures from each predicted scenario based on RCS placements and then recalibrates the placements to enhance DN resilience against both anticipated and critical failures. The RSM is formulated as a mixed-integer linear programming problem to maximize SPC by allocating resources optimally to serve maximum weighted loads while adhering to structural and operational constraints. The effectiveness and validity of the proposed model are demonstrated through case studies conducted on a modified IEEE 33-bus test system and a modified real DN.

Total Domination in Graphs: New Perspectives and Results

In this research paper, we initiate a study of such questions in the present context of total domination theory within graph theory from new perspectives and results concerned with formal definitions, mathematical formulations, theorems and practical applications. Description Total domination is a tool used to determine the graph that has minimum number of vertices can monitor or control all other vertices - factors of interest for problems related, amongst others fields as network optimization and resilience analysis. This paper covers fundamental definitions as well their most recent theoretical bounds, algorithmic complexities and practical examples arising in applications from the real world. The obtained results could be helpful in deciphering, exploiting and administering the concept of total domination further.

Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.

Rural Network Resilience: A New Tool for Exploring the Mechanisms and Pathways of Rural Sustainable Development

Enhancing rural resilience is crucial due to the significant vulnerabilities faced by rural areas, such as weak economic foundations, scarce resources, and deficient infrastructure. This paper aims to provide a comprehensive review of rural network resilience (RNR) to underscore its importance in understanding the mechanisms and pathways of rural sustainable development. To establish the theoretical foundations of RNR, we trace the evolution of resilience concepts and their applications across disciplines. The proposed analytical framework integrates evaluation methods from network science to assess RNR’s structural characteristics and outlines simulation techniques for predicting resilience under various risk scenarios. Furthermore, the paper examines the key influencing factors that shape RNR within complex rural regional systems, exploring the intricate evolutionary mechanisms involved. To bridge existing research gaps, a synergistic development perspective is introduced, emphasizing the interconnected, multi-layered nature of rural networks across societal, economic, and ecological domains. Additionally, a county-level analytical framework tailored for county-level rural resilience analysis is presented to offer strategic guidance for enhancing RNR and driving sustainable rural revitalization. This transdisciplinary synthesis propels RNR as an emerging field with significant policy implications.

A Critical Candidate Node-Based Attack Model of Network Controllability.

The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closeness-based, pagerank-based, and hierarchical attacks. Results show that the model outperforms these methods in both disruption effectiveness and computational efficiency. Extensive experiments on both synthetic and real-world networks validate the superior performance of this approach. This study provides valuable insights for identifying key nodes crucial for maintaining network controllability. It also offers a solid framework for enhancing network resilience against malicious attacks.

Dynamic multipath routing for energy‐efficient and reliable communication in 6G networks with MIMO

AbstractIn the era of 6G networks, Multiple Input Multiple Output (MIMO) technology offers unprecedented opportunities for high‐throughput and low‐latency communication. Existing communication frameworks, however, have difficulty optimizing both energy efficiency and reliability at the same time. In most cases, conventional routing protocols fail to meet the needs of MIMO systems, making them inefficient and prone to reliability problems due to their inability to dynamically adapt to different network conditions. This research addresses the intricate interplay between energy efficiency and reliability within the context of 6G networks with MIMO. The motivation for this research arises from the imperative to unlock the full potential of 6G networks with MIMO for achieving energy‐efficient and reliable communication. With the advancement of communication technology, seamless connectivity, minimal energy consumption, and robust reliability become increasingly critical. Currently, solutions cannot adapt dynamically to the diverse and dynamic conditions of a 6G environment. Through this research, we aim to bridge this gap, enhancing 6G network performance and sustainability with unprecedented gains in energy efficiency and reliability. We have developed the Dynamic Multipath Routing (DMR) algorithm by harnessing the advanced features of MIMO technology. The DMR algorithm strategically chooses paths to minimize the effects of fading, interference, and channel impairments, creating a resilient communication network. This improvement is essential for meeting the demanding connectivity needs of various 6G applications, covering ultra‐reliable low‐latency communication and massive machine‐type communication.

Graph Theoretical Models for Enhancing Highway Connectivity and Safety in Vehicular Networks

Vehicular networks play a crucial role in modern transportation systems, significantly impacting connectivity and safety on highways. This paper explores the application of graph theoretical models to enhance both connectivity and safety in vehicular networks. Graph theory, a branch of discrete mathematics, provides a robust framework for modeling and analyzing complex networks, including those formed by vehicles on highways. Our study begins by defining the vehicular network as a graph where nodes represent vehicles, and edges denote communication links between them. We employ various graph theoretical concepts such as connectivity, centrality, and network flow to evaluate and improve the network's performance. Key metrics, including the degree of nodes, clustering coefficients, and shortest path lengths, are utilized to quantify network connectivity and identify critical nodes and edges that influence overall network efficiency. One of the primary objectives is to ensure uninterrupted connectivity in the presence of dynamic and often unpredictable vehicular movement. To this end, we analyze the network's resilience to node failures and propose strategies to enhance robustness using redundancy and alternative routing paths. By incorporating concepts like k-connectivity and network diameter, we develop models that maintain high levels of connectivity despite the removal or failure of multiple nodes or edges. Safety is addressed through the lens of network stability and reliability. We investigate the impact of vehicular density, speed, and communication range on the network's ability to sustain reliable communication channels. Techniques such as dynamic topology management and adaptive power control are proposed to mitigate the risks associated with network fragmentation and communication delays. Furthermore, we introduce optimization algorithms that leverage graph partitioning and community detection to improve the management of vehicular clusters, facilitating efficient data dissemination and reducing the likelihood of congestion-related incidents. The proposed models are validated through simulations that mimic real-world highway conditions, demonstrating significant improvements in both connectivity and safety metrics. In conclusion, the application of graph theoretical models offers a promising approach to enhancing highway connectivity and safety in vehicular networks

Influence of Geopolitical Events on Procurement Strategies and Supply Chain Dynamics in Multinational Corporations

Purpose: The aim of the study was to examine the influence of geopolitical events on procurement strategies and supply chain dynamics in multinational corporations Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that geopolitical events exert a profound influence on procurement strategies and supply chain dynamics within multinational corporations (MNCs), compelling them to adapt swiftly to global uncertainties. The study underscored that events such as trade conflicts, sanctions, and political instability disrupt established supply chains, necessitating strategic adjustments in sourcing, supplier relationships, and operational practices. MNCs respond by diversifying sourcing locations, enhancing supply chain transparency, and integrating robust risk management frameworks to mitigate geopolitical risks effectively. Unique Contribution to Theory, Practice and Policy: Institutional Theory, Resource Dependence Theory & Transaction Cost Economics (TCE) may be used to anchor future studies on influence of geopolitical events on procurement strategies and supply chain dynamics in multinational corporations. Encourage MNCs to adopt agile procurement strategies that prioritize flexibility and responsiveness to geopolitical disruptions. This includes establishing multi-sourcing strategies, leveraging local suppliers, and implementing digital platforms for real-time risk monitoring and decision-making. Strengthen supplier relationship management practices to build resilient supply networks. Foster transparent communication and collaboration with suppliers to co-develop contingency plans and mitigate supply chain risks arising from geopolitical events. Advocate for transparent and stable trade policies that reduce uncertainty and facilitate global supply chain operations. Engage in constructive dialogues with policymakers to address trade barriers, tariffs, and regulatory inconsistencies that hinder cross-border trade and procurement efficiency.

A fairness-based multi-objective distribution and restoration model for enhanced resilience of supply chain transportation networks

Disruptive events play a significant role in the performance of supply chain networks (SCNs) due to their high dependency on transportation networks. Therefore, it is critical to develop coordinated strategies that consider (i) efficient restoration plans for the transportation network (TN) and (ii) distribution plans for downstream SCN, which consider critical aspects such as road congestion, travel time, and required service levels. Thus, we propose a novel multi-objective non-linear mixed-integer programming (NMIP) model to deal with disruptions in transportation networks while maximizing SCN performance. We developed a model for optimal restoration that reduces the effects of disruptions that disrupt the performance of the road TN and SCNs while considering a fair distribution of commodities. The model provides optimal restoration strategies for the TN after unpredicted events, not only based on a cost-effectiveness approach but also on the fair distribution of commodities among demand nodes on a complex network structure with a multi-supplier, multi-demand, and multi-commodity framework. To illustrate the model's capabilities, we study the transportation of commodities in Colombia and how disruptions impact their SCN performance. The results show that a fairness-based distribution helps to obtain satisfaction rates faster when compared to restoration plans based on cost-effectiveness only.

Competitive Analysis at the Cournot-Nash Equilibrium of an Interconnected Network

The electric power network has long been subject to monopolization, encompassing production, transportation, and distribution sectors. However, recent liberalization efforts have introduced competition into the electricity market. To understand and manage this competition, game theory, a prominent tool in economics, is frequently employed. Specifically, competition within the electricity market has been analyzed through various game-theoretical models, including Bertrand's atomicity, Cournot's homogeneity, and Nash's research competition. These models aim to achieve the Cournot-Nash equilibrium, where each participant in the market makes optimal decisions given the strategies of others. To effectively allocate production and ensure a balance between supply and demand, as well as to maintain the stability of the interconnected network, one has adopted a method that combines Load Flow techniques with game theory principles. This hybrid approach enables a strategic distribution of power production, taking into account the competitive dynamics of the market. By integrating these methodologies, one can address the complexities of competition while ensuring efficient and stable operation of the power grid. This innovative approach not only enhances the management of electricity production and distribution but also fosters a more competitive and resilient power network. Moreover, the application of game theory in this context allows for a deeper understanding of strategic interactions among market participants. It helps in predicting behaviors, formulating strategies, and anticipating market changes, thus providing a robust framework for decision-making. This is particularly crucial in a liberalized market where multiple entities vie for market share and profitability. By employing game-theoretical insights, one can simulate various market scenarios, optimize resource allocation, and enhance overall market efficiency. Furthermore, this approach supports the integration of renewable energy sources by ensuring that their variable nature is accommodated within the grid's operational dynamics. In summary, the intersection of game theory and load flow methods offers a comprehensive solution to the challenges posed by a competitive electricity market, paving the way for a sustainable and efficient energy future.

Resilience Strategies in Sustainable Supply Chain Management: Bibliometric Analysis and Future Research Directions

The emergence of the volatile, uncertainty, complexity, and ambiguity (VUCA) has adversely affected the supply chain network's resilience. The risks and difficulties presented by the VUCA environment can harm an organisation's performance. The project stakeholders are highly encouraged to prioritise sustainability, adaptability, and resilience while considering the constantly changing, volatile, and uncertain climate. Thus, Sustainable Supply Chain Management (SSCM) is a strategic response to the challenges the VUCA environment poses. Contracting firms can expand their capacity to mitigate risks, negotiate volatility, and generate long-term value for all stakeholders by integrating sustainability concepts into supply chain operations. However, SSCM become problematic because of diverse global supply chain disruptions and sustainable demands. Understanding the trends in this topic is crucial to discovering the pattern of resilience strategies in sustainable supply chains. The current literature is reviewed in this study, focusing on studies conducted between 2006 and 2024. The Scopus database and the VOS viewer software were used to arrange a systematic literature review and provide an overview of this field. One hundred fifty-nine (159) publications on resilience, risk management and SSCM were identified and written by hundred and nine (109) authors from forty-four (44) countries. The top number of publications was from Iran, with thirty-eight (38), followed by the United States, with twenty-six (26). The most frequently used keywords are sustainability, supply chain management, supply chains, and sustainable development, reflecting the current significant research direction. Standard bibliometric indicators were utilised in this study, and data analysis was done using VOSviewer, Microsoft Excel, and Harzing's Publish and Perish software. The core input of this study is analysing available information to address resilience issues in mitigating risk in the sustainable supply chain. To build a more sustainable system, it offers suggestions for more research in these areas that scholars may pursue and that project stakeholders could implement in their organisations.

Research on AdHoc network routing algorithm for link stability prediction

Abstract This study aims to improve network communication efficiency and reliability by researching routing algorithms for predicting link stability in AdHo networks. AdHoc network is a self-organizing, decentralized network structure in which nodes establish temporary connections through wireless communication, making the network resilient and flexible. However, due to the continuous changes in network topology and the instability of wireless links, routing problems in AdHoc networks have become particularly complex. This article proposes a backup path routing algorithm LS-BPR based on link stability prediction. This mechanism takes the path with the minimum delay as the main path, uses the cost function as the standard for selecting backup paths, and then uses the ARIMA model to predict links with good link stability as backup paths. We simulate and compare LS-BPRAODV and AODV protocols using the QualNet simulation platform. The simulation results show that the LS-BPRAODV, with the addition of a prediction mechanism, improves network performance, reduces the number of source node routing discoveries, reduces the number of route breaks, significantly reduces the packet loss rate and information reception delay in the network, and improves the average throughput of the network. This study provides new theoretical and empirical support for the routing algorithm for link stability prediction in AdHo networks, which is of great significance for promoting the development of wireless self-organizing networks.

Evaluating the Scalability and Combinatorial Effectiveness of Design‐for‐Resilience Heuristics

AbstractThis study investigates the scalability of biologically inspired design‐for‐resilience heuristics in System of Systems (SoS) of varying sizes, simulating SoS with ten, twenty, and thirty constituent systems. We build upon previous research that identified thirteen design heuristics for SoS resilience based on Ecological Network Analysis (a subset of graph theory). We both extend their application to larger network sizes and combine them to enhance efficiency. Our analysis validates the application of design heuristics for random networks to evaluate the effectiveness of individual and combined design heuristics in network resilience compared to random network modifications. The outcomes provide insights into the scalability and efficiency of these heuristics, revealing the potential for systematic SoS design and resilience improvement. Key contributions of this research include the validation of the scalability of bio‐inspired resilience metrics and the testing of nine new combined design heuristics for SoS. These contributions provide a tool for decision‐makers seeking to select resilient SoS architectures. The findings also pave the way for future research into broader applications beyond ecological networks and further exploration of the relationship between ecological robustness and resilience.

Enhanced Multipath TCP to Improve the Mobile Device Network Resiliency

Enhanced Multipath TCP to Improve the Mobile Device Network Resiliency