Interconnected Infrastructure Research Articles

Machine Learning Techniques for Cyber Security in Internet of Robotic Things

Robots are becoming common in domestic, medical, industrial, entertainment, and educational routine activities. The use of robots automates the work processes thus minimizes human labor. The Robots perform complex and repetitive tasks with efficiency and agility, therefore, the conventional industrial manufacturing process are being replaced by smart manufacturing. Robotics encompasses the design and development of robot-based automated systems. It integrates various emerging technologies i.e. operational technology (OT), cloud computing, and artificial intelligence (AI). The Internet of Robotic Things (IoRT) seamlessly combines robots and Internet of Things (IoT) devices, enabling connectivity through the Internet. IoRT enables simple robots to coordinate with each other to achieve well-defined goals by creating a multi-robot system. Cyber security is an inherent challenge for IoRTs because of the interconnected infrastructure and reliance on critical industrial operations on the internet. Any cyber-attack can affect the ongoing operations and compromise the safety of robots. The growing interest among governments, researchers, and industries in robotics and automation demands a dependable cyber-security solution. This paper explores machine learning (ML) based cyber security solutions to mitigate cyber vulnerabilities and threats to IoRT and its dependent systems.

Correlated spatiotemporal downscaling of Euro‐CORDEX climatic data for infrastructure resilience assessment

AbstractA methodology is presented for downscaling the Euro‐CORDEX climatic projections in order to derive spatially and temporally correlated weather fields that can be used for risk and resilience assessment of large‐scale asset portfolios or interconnected infrastructure. The temporal resolution of the Euro‐CORDEX data is downscaled to a 10 min basis by employing a modified analogue‐type approach that utilizes the k‐NN algorithm along with measurements from weather stations. The aim is to generate composite “Frankenstein” days comprising 144 jigsaw pieces of observed 10 min timeseries that are scaled and/or shifted, and matched together to form a continuous daily record. These point‐estimates, valid only at the locations of the weather stations, are expanded spatially by employing high‐fidelity weather intensity measure fields that provide variable yet synchronous patterns of weather parameters at all locations of interest. As a case study, the Euro‐CORDEX projections for wind, temperature, and precipitation are downscaled for the Metsovo‐Panagia segment of Egnatia Odos highway in Greece, by employing high‐fidelity Computational Fluid Dynamic simulations that account for the topography of the site to simulate turbulent wind flows. These are combined with measurements of two local weather stations to generate the Frankenstein timeseries and corresponding weather fields that can be used for estimating operability, recovery and direct/indirect loss statistics on an event‐by‐event basis for an ensemble of interconnected highway assets.

Exploring Shifting Patterns in Recent IoT Malware

The rise of malware targeting interconnected infrastructures has surged in recent years, driven largely by the widespread presence of vulnerable legacy IoT devices and inadequately secured networks. Despite the strong interest attackers have in targeting this infrastructure, a significant gap remains in understanding how the landscape has recently evolved. Addressing this knowledge gap is essential to thwarting the proliferation of massive botnets, thereby safeguarding end-users and preventing disruptions in critical infrastructures. This work offers a contemporary analysis of Linux-based malware, specifically tailored to IoT malware operating in 2021-2023. Using automated techniques involving both static and dynamic analysis, we classify malware into related threats. By scrutinizing the most recent dataset of Linux-based malware and comparing it to previous studies, we unveil distinctive insights into emerging trends, offering an unparalleled understanding of the evolving landscape. Although Mirai and Gafgyt remain the most prominent families and present a large number of variants, our results show that (i) there is an increase in the sophistication of malware, (ii) malware authors are adding new exploits to their arsenal, and (iii) malware families that originally attacked Windows systems have been adapted to attack Linux-based devices.

A scalable optimization approach to the intervention planning of complex interconnected infrastructures

The functioning of infrastructure networks is vital for modern communities. Maintenance should be planned to ensure infrastructure's functionality and safety at the lowest cost. Interconnected infrastructure networks can affect each other's functionality, and maintenance on one network can impact the serviceability of others. Planned intervention grouping across infrastructures reduces set-up costs and service interruption, improving infrastructure availability and serviceability at lower costs.Finding the best grouping strategy is a known NP-hard problem, with several optimization strategies have been proposed, mainly based on nonlinear models which are computationally expensive and do not guarantee scalability. Furthermore, infrastructure intervention planning models mostly focus on grouping of interventions which are considered as given. In this paper, we propose a new efficient optimization model to optimize intervention grouping for interconnected infrastructure networks. We develop a scalable two-step optimization model where we first plan each individual intervention type based on a preventive maintenance policy accounting for the degradation behavior of objects, then group interventions to minimize the net costs, considering dependencies within and accross infrastructure networks.We formulate the grouping problem as an Integer Linear Program, which can be solved exactly with standard solvers. The model accounts for interactions between infrastructure networks and considers the impact on all stakeholders. It also accommodates various intervention types like maintenance, removal, and upgrading.Using a demonstrative application, we show that our model significantly reduces net costs and outperforms alternative nonlinear formulations and related heuristics in terms of both solution quality and computation performance. Additionally, the optimal intervention plan shows repetitive patterns, which suggests that a rolling horizon strategy could be used where the optimization problem is solved for shorter time horizons, leading to significant computational benefits.

Securing Industrial Control Systems: A Deep Dive into AI Approaches

In the era of increasingly interconnected infrastructures and the emergence of the Internet of Things (IoT), industrial control systems (ICS) are encountering a growing susceptibility to cyber threats. This paper delves into the growing adoption of artificial intelligence (AI) as a means to mitigate cybersecurity risks for ICS. AI-driven solutions play a pivotal role in detecting anomalies, pinpointing potential threats, and executing real-time responses to curtail risks. The authors extensively discuss the application of AI-based cybersecurity measures for ICS, encompassing techniques like anomaly detection, intrusion detection, and behavioral analysis. Furthermore, the narrative explores the challenges associated with implementing AI-centric cybersecurity solutions, such as concerns related to data privacy, system intricacies, and the imperative for continuous monitoring and updates. The authors draw upon case studies showcasing successful deployments of AI-driven cybersecurity solutions in industrial settings. Concluding the discussion, the authors propose a novel hybrid machine learning approach designed for anomaly detection in ICSs. This chapter serves as a comprehensive resource, shedding light on the role of AI in fortifying cybersecurity for industrial control systems. Its insights aim to enhance resilience against cyber-attacks, minimize the potential for system disruptions, and mitigate the risk of data breaches.

Assessment criteria for inter-organizational collaboration in interconnected infrastructure projects

PurposeSocieties depend on interconnected infrastructures that are becoming more complex over the years. Multi-disciplinary knowledge and skills are essential to develop modern infrastructures, requiring close collaboration of various infrastructure owners. To effectively manage and improve inter-organizational collaboration (IOC) in infrastructure construction projects, collaboration status should be assessed continually. This study identifies the assessment criteria, forming the foundation of a tool for assessing the status of IOC in interconnected infrastructure projects.Design/methodology/approachA systematic literature study and in-depth semi-structured interviews with practitioners in interconnected infrastructure construction projects in the Netherlands are performed to identify the criteria for assessing the status of IOC in infrastructure construction projects, based on which an assessment tool is developed.FindingsThe identified assessment criteria through the literature and the practitioner’s perspectives results in the designing and development of a collaboration assessment tool. The assessment tool consists of 12 criteria and 36 sub-criteria from three different categories of collaborative capacity: individual, relational, and organizational.Originality/valueThe assessment tool enables practitioners to monitor the status of IOC between infrastructure owners and assists them in making informed decisions to enhance collaboration. The assessment tool provides the opportunity to assess and analyze the status of collaboration based on three categories (i.e., individual, relational, and organizational).

Integrated Bayesian Network and Strongest Path Method (BN-SPM) for effective multi-hazard risk assessment of interconnected infrastructure systems

This study proposes an integrated framework based on Bayesian Network (BN) and Strongest Path Method (SPM) to provide a robust assessment of the natural hazard risks to interconnected infrastructure systems. Leveraging Bayesian inference, the BN transforms prior failure probabilities into posterior ones, while SPM prioritizes network entities based on global impact, global vulnerability, and risk indices, calculated from key parameters including the likelihood of failure, degree of impact, and degree of dependence. The proposed integrated BN-SPM model calculates risk indices for complex interconnected networks with conditional dependencies on external nodes, such as multi-hazard, or mitigation and redundancy strategies, without predetermined parameters. Using this framework we evaluate diverse scenarios in Saint Lucia, encompassing non-hazard condition, flooding hazards, and presumptive strategies like mitigation and redundancy. The high likelihood of failure and complex interdependencies heighten risks even under non-hazard condition. Particularly, the Hewanorra International Airport (HIA) emerges as the most vulnerable entity. The established risk assessment framework offers the potential for application in evaluating risks within complex interconnected systems such as power grids, transportation networks, and communication systems facing various multi-hazard scenarios. This framework also assesses the implementation of mitigation and redundancy strategies, reducing the risk index within the affected network domain.

Determining critical nodes in optimal cost attacks on networked infrastructures

A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender’s side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times.

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system’s total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics.

DomiRank Centrality reveals structural fragility of complex networks via node dominance

Determining the key elements of interconnected infrastructure and complex systems is paramount to ensure system functionality and integrity. This work quantifies the dominance of the networks’ nodes in their respective neighborhoods, introducing a centrality metric, DomiRank, that integrates local and global topological information via a tunable parameter. We present an analytical formula and an efficient parallelizable algorithm for DomiRank centrality, making it applicable to massive networks. From the networks’ structure and function perspective, nodes with high values of DomiRank highlight fragile neighborhoods whose integrity and functionality are highly dependent on those dominant nodes. Underscoring this relation between dominance and fragility, we show that DomiRank systematically outperforms other centrality metrics in generating targeted attacks that effectively compromise network structure and disrupt its functionality for synthetic and real-world topologies. Moreover, we show that DomiRank-based attacks inflict more enduring damage in the network, hindering its ability to rebound and, thus, impairing system resilience. DomiRank centrality capitalizes on the competition mechanism embedded in its definition to expose the fragility of networks, paving the way to design strategies to mitigate vulnerability and enhance the resilience of critical infrastructures.

Time-Dependent Unavailability Exploration of Interconnected Urban Power Grid and Communication Network

This paper presents a time-dependent reliability analysis created for a critical energy infrastructure use case, which consists of an interconnected urban power grid and a communication network. By utilizing expert knowledge from the energy and communication sectors and integrating the renewal theory of multi-component systems, a representative reliability model of this interconnected energy infrastructure, based on real network located in the Czech Republic, is established. This model assumes reparable and non-reparable components and captures the topology of the interconnected infrastructure and reliability characteristics of both the power grid and the communication network. Moreover, a time-dependent reliability assessment of the interconnected system is provided. One of the significant outputs of this research is the identification of the critical components of the interconnected network and their interdependencies by the directed acyclic graph. Numerical results indicate that the original design has an unacceptable large unavailability. Thus, to improve the reliability of the interconnected system, a slightly modified design, in which only a limited number of components in the system are modified to keep the additional costs of the improved design limited, is proposed. Consequently, numerical results indicate reducing the unavailability of the improved interconnected system in comparison with the initial reliability design. The proposed unavailability exploration strategy is general and can bring a valuable reliability improvement in the power and communication sectors.

Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

Maintaining interconnected infrastructures such as transportation, communication, power grids, and pipeline networks is paramount in emerging economies. One of the critical interruptions is the targeted attacks on the operating cyber-physical systems to disconnect operations, inspection, or monitoring of the system. Therefore, adopting a cybersecurity system (or platform) that provides holistic protection is vital for protecting the integrity of critical infrastructure networks. As such, this research aspires to provide a decision support system for cybersecurity managers or practitioners (in the Indian power sector) to select the best and appropriate platform for protection against cyber-attacks. A three-phase method is adopted. First, a literature search followed by an expert panel discussion identified alternatives (cybersecurity platforms) and selection criteria. Next, a questionnaire was developed. Thirdly, a hybrid Best-Worst Improved and COmprehensive distance-Based RAnking (BWM-I and COBRA) method was proposed and applied to evaluate the cybersecurity platform alternatives. Four alternatives (Cloud-Based Platforms, Web-Based Platforms, Application-Based Platforms, and AI-Based Platforms), six primary criteria, and fifteen unique sub-criteria were identified. Responses were collected from 80 power utility managers on a pan-India basis, ranking "End-to-End Coverage" criteria and the AI-Based platform as best. This approach identified the best cybersecurity platform that, if adopted, can be extended to other critical infrastructures, with an appropriate adjustment in the selection criteria. The study can be helpful to practitioners in evaluating cybersecurity platforms. Furthermore, it addresses a research gap in its application in a developing country like India.

Performance Assessment and Analysis of a 1 MW Three-Phase Photovoltaic Power Station Connected to a Factory’s Electrical Grid in Morocco

In this study, a performance assessment and analysis of a 1 MW three-phase photovoltaic (PV) power station connected to the electrical grid of a factory in Morocco are presented. The main objective of this research is to assess the performance of the PV power station and analyze its efficiency, energy generation, and operational characteristics. To accomplish this, a combination of field measurements, data analysis, and simulation techniques are used. The study begins by providing an extensive overview of the PV power station, including a thorough description of its main elements such as solar panels, inverters, transformers, and grid interconnection infrastructure. Furthermore, field measurements are conducted to collect data on solar irradiance, ambient temperature, and PV system power, as well as electrical parameters such as voltage, current, and power factor. The findings of this study provide valuable insights into the performance and economic viability of the PV power station. These insights can serve as guidance for renewable energy stakeholders, investors, and policymakers, facilitating the development of sustainable solar energy projects and contributing to renewable energy targets.

An experimental study on the smoke spread in underground interconnected infrastructure with longitudinal ventilation

The underground interconnected infrastructures are under development in the metropolitan areas, and simultaneously, also raise many safety issues, especially with regards to the smoke control once a fire occurred. Owing to the interconnected structure, the smoke spread in underground interconnected infrastructures can be more complex than that in “normal” tunnels, where the smoke spread is commonly simplified as one-dimensional spreading. In this study, the properties of maximum temperature, temperature decay, and smoke spread in a longitudinally ventilated underground interconnected infrastructure were studied. Experiments were conducted in a reduced-scale model which was divided into the tunnel part A and part B, and the scenario of a fire occurring at the junction point was mainly considered. For such a scenario, it is found that the temperature decay depends on the ventilated velocities of both upstream and connected tunnel sections. The spreading of the smoke in the upstream tunnel section depends on the ventilated velocity of the upstream tunnel section and can be well described by previous back-layering correlation. Whereas for the spreading of smoke in the connected tunnel section, both the ventilated velocities in the upstream and connected tunnel sections show a distinct impact. The estimation of whether the smoke spreads to the connected tunnel is established by comparing the buoyance force of the smoke and the inertia force of the ventilated flow. It is indicated that the smoke flow will be less inclined to spread to the connected tunnel under the scenarios of weak buoyancy force and large ventilated velocity. The findings of this work may contribute to an improved understanding of thermal-driven smoke propagation in underground interconnected infrastructures.

Towards standardized guidelines in digital data acquisition for monitoring and maintenance of European transport infrastructures

AbstractThe digital transformation of the architecture, engineering, and construction sector is associated with an enormous production of data. The appearance of new technologies is leading the way for more sustainable and interconnected infrastructures. Therefore, data analytics is crucial to know the current state of infrastructure and will help towards the efficient management of assets. The main issue is how to collect and exploit meaningful data throughout the lifecycle of infrastructure assets to further allow human experts and computers to make accurate diagnostics, predictions, and decisions. This contribution presents the insights gained in the course of IM‐SAFE project, showing the strategy followed to create guidelines for data collection to monitor and maintain critical assets in the infrastructure network (bridges and tunnels). It tackled a variety of surveying technologies, from destructive and non‐destructive, to chemical methods, among others. The results obtained will support the European Committee for Standardization (CEN) in the standards for monitoring and maintenance of bridges and tunnels, accelerating the adoption of new standards by public authorities and stakeholders in transport infrastructure.

Evaluation of post-disaster home livability for coastal communities in a changing climate

With sea level rise and hurricane intensification projected in coming years due to climate change, the risk to coastal communities is greatly amplified. In the wake of potential future hurricane scenarios, various infrastructure systems, including residential buildings, power and water systems, and transportation networks, could be damaged or disrupted, leading to uninhabitable homes and communities for extended periods following disasters. To quantify the potential effects of climate change on the post-disaster livability of coastal homes and communities, a framework is proposed integrating the vulnerabilities of various interconnected infrastructure systems under different climate change scenarios. A novel metric, the post-disaster home livability index, is developed to quantify the resilience and habitability of homes and communities following a natural disaster. To evaluate climate change effects on the community livability under a future Hurricane Sandy scenario in the year 2100, a case study is conducted for a community in the state of Connecticut. To compare different possible emissions scenarios, two potential future representative concentration pathway (RCP) climate change scenarios, RCP2.6, and RCP8.5, are considered alongside uncertainties in the future climate's effects. The framework is used to evaluate the benefits of different structural adaptations, such as building elevation or power system maintenance. Such actions are found to have the potential to reduce storm impacts such that future Sandy's impacts on post-disaster damages and livability could be of similar or even lesser severity than the original Sandy's. These findings can help inform enhanced adaptation strategies to improve community resilience.

Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility

The growing interconnectedness of urban infrastructure networks presents challenges to their ability to handle unforeseen disruptions, particularly in the context of extreme weather events resulting from climate change. Understanding the resilience of interconnected infrastructure systems is imperative to effectively manage such disruptions. This study investigates the role of interconnectedness in enhancing the resilience of public transportation systems in Hong Kong, a city heavily reliant on public transit. Our results demonstrate that interconnected transportation systems improve resilience by reducing topological vulnerabilities, increasing attack tolerance, and enhancing post-disruption interoperability. Findings also identify the potential to integrate vulnerable systems for greater robustness and highlight the marginal benefits of enhancing intermodal transfer. Strengthening interconnectedness among modes of urban public transit fosters a safe-to-fail system, presenting a distinct resilience-by-design approach. This complements conventional resilience-by-intervention approaches that focus on improving individual systems or introducing entirely new systems.

Assessing adaptation planning strategies of interconnected infrastructure under sea-level rise by economic analysis

This study assesses the commonly adopted adaptation planning strategies of infrastructures in Northwest Florida (USA) based on economic analysis under different objective years. Specifically, the economic analysis considers both direct and indirect impacts of sea level rise by deploying the interdependence of infrastructures. We demonstrate the difference and significance of considering indirect economic impacts in the process of cost-benefit analysis under sea level rise. Based on the results, we recommend that the most effective strategy is partial protection of land use plus inundated transportation network upgrade, even though the total shoreline protection can make more benefits. Furthermore, we compare the performance of objective planning year from two criteria: total benefits and cost-effectiveness. The result indicates that the year 2080 could be the most economical if it is set as the objective year for the long-term infrastructure planning. The result also highlights that the economic analysis of infrastructure should be conducted over time since the total costs are distributed over many years. It is not to say that the farther the year is, the more effectiveness the strategy would be, although the total benefits would be greater.

Silica micro-rod resonator-based Kerr frequency comb for high-speed short-reach optical interconnects.

Conventional data center interconnects rely on power-hungry arrays of discrete wavelength laser sources. However, growing bandwidth demand severely challenges ensuring the power and spectral efficiency toward which data center interconnects tend to strive. Kerr frequency combs based on silica microresonators can replace multiple laser arrays, easing the pressure on data center interconnect infrastructure. Therefore, we experimentally demonstrate a bit rate of up to 100 Gbps/λ employing 4-level pulse amplitude modulated signal transmission over a 2 km long short-reach optical interconnect that can be considered a record using any Kerr frequency comb light source, specifically based on a silica micro-rod. In addition, data transmission using the non-return to zero on-off keying modulation format is demonstrated to achieve 60 Gbps/λ. The silica micro-rod resonator-based Kerr frequency comb light source generates an optical frequency comb in the optical C-band with 90 GHz spacing between optical carriers. Data transmission is supported by frequency domain pre-equalization techniques to compensate amplitude-frequency distortions and limited bandwidths of electrical system components. Additionally, achievable results are enhanced with offline digital signal processing, implementing post-equalization using feed-forward and feedback taps.

Application of the longitudinal ventilation system in the underground interconnected infrastructure: Assessment of flow distribution and confluence ratio

Longitudinal ventilation is widely employed in underground interconnected infrastructure, where the characteristic of the ventilated flow is distinctly different from the “normal” tunnels with a single structure. In this paper, the characteristic and the distribution of the ventilated flow in the underground interconnected infrastructure with longitudinal ventilation is studied. A small-scale experiment was carried out, where the effect of pressure rises and the location of the jet fan was taken into account. Results show that the ventilated velocity in a specified tunnel section is affected by the jet fan in the local tunnel section and the interconnected tunnel sections. For two interconnected tunnel sections that confluent at the junction point, with an increasing pressure rise in a specified tunnel section, the ventilated velocity in the local tunnel section increases whereas the ventilated velocity in another tunnel section decreases. The flow direction can even reverse to the opposite direction when the pressure rise of one tunnel section is large enough. To assess the variation of ventilated flow, the parameter of confluence ratio is defined and the prediction is established based on the theory of ventilation network, which can well describe the variation trend of the ventilation flow among different tunnel sections.