Interconnected Infrastructure Research Articles

The approach to determining vulnerable elements in critical energy infrastructures

The paper presents the main provisions of the methodology for identifying vulnerable elements in the fuel and energy complex, which is studied as a set of industry critical energy infrastructures. This methodology is based on the use of industry models system in the fuel and energy sector, on the analysis of the interconnected critical energy infrastructure's work in normal functioning and during emergency situations. The paper proposes a schematic diagram for the formation of vulnerable elements list for fuel and energy complex based on critical elements of industries. The scheme of work with a two-level system of models is given. The results of testing the methodology and developed models are presented (illustrative example).

Applications of Graph Spectral Techniques to Water Distribution Network Management

Cities depend on multiple heterogeneous, interconnected infrastructures to provide safe water to consumers. Given this complexity, efficient numerical techniques are needed to support optimal control and management of a water distribution network (WDN). This paper introduces a holistic analysis framework to support water utilities on the decision making process for an efficient supply management. The proposal is based on graph spectral techniques that take advantage of eigenvalues and eigenvectors properties of matrices that are associated with graphs. Instances of these matrices are the adjacency matrix and the Laplacian, among others. The interest for this application is to work on a graph that specifically represents a WDN. This is a complex network that is made by nodes corresponding to water sources and consumption points and links corresponding to pipes and valves. The aim is to face new challenges on urban water supply, ranging from computing approximations for network performance assessment to setting device positioning for efficient and automatic WDN division into district metered areas. It is consequently created a novel tool-set of graph spectral techniques adapted to improve main water management tasks and to simplify the identification of water losses through the definition of an optimal network partitioning. Two WDNs are used to analyze the proposed methodology. Firstly, the well-known network of C-Town is investigated for benchmarking of the proposed graph spectral framework. This allows for comparing the obtained results with others coming from previously proposed approaches in literature. The second case-study corresponds to an operational network. It shows the usefulness and optimality of the proposal to effectively manage a WDN.

ProNoC: A low latency network-on-chip based many-core system-on-chip prototyping platform

Network-on-chip (NoC) is an emerging interconnect infrastructure to address the scalability limitation of conventional shared bus architecture for many-core system-on-chip (MCSoC). Current field-programmable gate arrays (FPGAs) have over million lookup tables, making it possible to prototype a complete NoC-based MCSoC on a single FPGA device. FPGA prototyping allows rapid system verification and optimum design parameters estimation. However, existing NoC-based MCSoC prototypes are usually adopting simple NoC architectural functionality. These NoC prototypes cannot represent a realistic projection of the state-of-the-art application-specific integrated circuit (ASIC) NoCs as these prototypes have limited overall system performance. This paper presents ProNoC, an integrated tool for rapid prototyping and validation of NoC-based MCSoC projects targeting FPGA devices. ProNoC adopts most advanced NoC features such as the support of virtual channel (VC), virtual network, low latency routing and different routing algorithms. Results show that NoC interconnect in ProNoC outperforms CONNECT, the most recent VC based prototype NoC with lower logic cell utilization, higher maximum operating frequency, higher average saturation throughput, and lower average communication latency. Moreover, ProNoC is equipped with graphical user interface to facilitate the development of MCSoC prototypes on FPGA platforms.

Closing the low-carbon material loop using a dynamic whole system approach

The transition to low carbon energy and transport systems requires an unprecedented roll-out of new infrastructure technologies, containing significant quantities of critical raw materials. Many of these technologies are based on general purpose technologies, such as permanent magnets and electric motors, that are common across different infrastructure systems. Circular economy initiatives that aim to institute better resource management practices could exploit these technological commonalities through the reuse and remanufacturing of technology components across infrastructure systems. In this paper, we analyze the implementation of such processes in the transition to low carbon electricity generation and transport on the Isle of Wight, UK. We model two scenarios relying on different renewable energy technologies, with the reuse of Lithium-ion batteries from electric vehicles for grid-attached storage. A whole-system analysis that considers both electricity and transport infrastructure demonstrates that the optimal choice of renewable technology can be dependent on opportunities for component reuse and material recycling between the different infrastructure systems. Hydrogen fuel cell based transport makes use of platinum from obsolete catalytic converters whereas lithium-ion batteries can be reused for grid-attached storage when they are no longer useful in vehicles. Trade-offs exist between the efficiency of technology reuse, which eliminates the need for new technologies for grid attached storage completely by 2033, and the higher flexibility afforded by recycling at the material level; reducing primary material demand for Lithium by 51% in 2033 compared to 30% achieved by battery reuse. This analysis demonstrates the value of a methodology that combines detailed representations of technologies and components with a systemic approach that includes multiple, interconnected infrastructure systems.

Using interdependency matrices to mitigate targeted attacks on interdependent networks: A case study involving a power grid and backbone telecommunications networks

Analysis of the interdependencies between interconnected critical infrastructures can help enhance the robustness of the individual infrastructures as well as the overall interconnected infrastructures. One of the most studied interdependent critical infrastructure network scenarios is a power grid connected to a backbone telecommunications network. In this interdependent infrastructure scenario, the robustness of the entire system is usually analyzed in the context of cascading failure models in the power grid. However, this paper focuses on targeted attacks, where an attack on a telecommunications network node directly affects a connected power grid node, and vice versa. Cascading failures are outside the scope of this paper because the objective is to enhance the robustness of the interconnections between the infrastructures. In order to mitigate the impacts of targeted attacks on the interdependent infrastructures, three interdependency matrices for connecting the infrastructures are specified and analyzed. The analysis identifies the interdependency matrix that best reduces the impacts of targeted attacks and the propagation of failures between the infrastructures. Additionally, the impacts of interconnecting a power grid to different telecommunications networks, each with different susceptibilities to targeted attacks, is evaluated.

On the Gaussian Pulse Propagation Through Multilayer Graphene Plasmonic Waveguides—Impact of Electrostatic Screening and Frequency Dispersion on Group Velocity and Pulse Distortion

In this paper, we analytically describe the characteristics of propagating and diffusive plasma waves in multilayer graphene heterostructures targeted toward implementing on-chip interconnects. When the effect of collision-dominated resistance of the graphene sheet is negligible, the plasma waves propagate through the media without pulse reshaping. However, in the presence of electron scatterings due to phonons and charged impurities in the sample, the plasma waves acquire a diffusive character and undergo significant frequency-dependent pulse reshaping. We analytically model the propagation speed and pulse-width distortion of a narrow-band Gaussian signal centered at THz frequencies by accounting for the finite electrostatic screening between the multiple layers of graphene. The consequences of signal distortion and attentuation on limiting the signal bit-rate and the impact on energy dissipation are quantified. A partition length of the plasmonic waveguide that distinguishes between $LC$ - and $RC$ -dominated regimes of signal transport is derived. For on-chip interconnect application, the upper bound on the propagation length of plasma waves is determined by both the energy dissipation and the bit-rate requirement of the interconnect infrastructure. Upon comparison with on-chip electrical interconnects, we identify the energy budget that can be allocated toward plasmonic transceivers, such that the overall plasmonic communication is more energy efficient compared to electrical communication.

A Framework for Interconnection-Aware Domain-Specific Many-Accelerator Synthesis

Many-accelerator Systems-on-Chip (SoC) have recently emerged as a promising platform paradigm that combines parallelization with heterogeneity, in order to cover the increasing demands for high performance and energy efficiency. To exploit the full potential of many-accelerator systems, automated design verification and analysis frameworks are required, targeted to both computational and interconnection optimization. Accurate simulation of interconnection schemes should use real stimuli, which are produced from fully functional nodes, requiring the prototyping of the processing elements and memories of the many-accelerator system. In this article, we argue that the Hierarchical Network-on-Chip (HNoC) scheme forms a very promising solution for many-accelerator systems in terms of scalability and data-congestion minimization. We present a parameterizable SystemC prototyping framework for HNoCs, targeted to domain-specific many-accelerator systems. The framework supports the prototyping of processing elements, memory modules, and underlying interconnection infrastructure, while it provides an API for their easy integration to the HNoC. Finally, it enables holistic system simulation using real node data. Using as a case study a many-accelerator system of an MRI pipeline, an analysis on the proposed framework is presented to demonstrate the impact of the system parameters on the system. Through extensive experimental analysis, we show the superiority of HNoC schemes in comparison to typical interconnection architectures. Finally, we show that, adopting the proposed many-accelerator design flow, significant performance improvements are achieved, from 1.2 × up to 26 × , as compared to a x86 software implementation of the MRI pipeline.

Critical infrastructure interdependence in New York City during Hurricane Sandy

Purpose This study aims to investigate the impact of Hurricane Sandy from the perspective of interdependence among different sectors of critical infrastructure in New York City and to assess the interconnected nature of risks posed by such a hurricane. Design/methodology/approach This study uses indirect damages of each sector to estimate the degree of functional interdependence among the sectors. The study examines the impact of the hurricane on different critical infrastructures by combining hazard maps of actual inundation areas with maps of critical infrastructure. The direct damages of each sector are calculated from the inundation areas in the flood map. The indirect damages are estimated by considering the areas that were not inundated but affected by Sandy through the interconnected infrastructure. Findings The electricity sector was the key sector to propagate risks to other sectors. The examination of new initiatives to increase the resilience of critical infrastructures in New York City after Sandy reveals that these initiatives focus primarily on building hard infrastructures to decrease direct damages. They understate the importance of interdependent risk across sectors. Future disaster risk reduction strategies must address interdependent infrastructures to reduce indirect damages. Originality/value This paper focuses on estimating the direct and indirect damages caused by Hurricane Sandy in each critical infrastructure sector, using GIS mapping techniques. It also introduces a Bayesian network as a tool to analyze critical infrastructure interdependence.

A Novel Approach to Optimize Fault-Tolerant Hybrid Wireless Network-on-Chip Architectures

Wireless Network-on-Chip (WNoC) architectures have emerged as a promising interconnection infrastructure to address the performance limitations of traditional wire-based multihop NOCs. Nevertheless, the WNoC systems encounter high failure rates due to problems pertaining to integration and manufacturing of wireless interconnection in nano-domain technology. As a result, the permanent failures may lead to the formation of any shape of faulty regions in the interconnection network, which can break down the whole system. This issue is not investigated in previous studies on WNoC architectures. Our solution advocates the adoption of communication structures with both node and link on disjoint paths. On the other hand, the imposed costs of WNoC design must be reasonable. Hence, a novel approach to design an optimized fault-tolerant hybrid hierarchical WNoC architecture for enhancing performance as well as minimizing system costs is proposed. The experimental results indicate that the robustness of this newly proposed design is significantly enhanced in comparison with its the fault-tolerant wire-based counterparts in the presence of various faulty regions under both synthetic and application-specific traffic patterns.

A Holistic Approach Towards Intelligent Hotspot Prevention in Network-on-Chip-Based Multicores

Traffic hotspots, a severe form of network congestion, can be caused unexpectedly in a network-on-chip (NoC) due to the immanent spatio-temporal unevenness of application traffic. Hotspots reduce the NoC's effective throughput, where in the worst-case scenario, network traffic flows can be frozen indefinitely. To alleviate this problematic phenomenon several adaptive routing algorithms employ online load-balancing schemes, aiming to reduce the possibility of hotspots arising. Since most are not explicitly hotspotagnostic, they cannot completely prevent hotspot formation(s) as their reactive capability to hotspots is merely passive. This paper presents a pro-active Hotspot-Preventive Routing Algorithm (HPRA) which uses the advance knowledge gained from network embedded artificial neural network-based (ANN) hotspot predictors to guide packet routing in mitigating any unforeseen near-future hotspot occurrences. First, these ANN-based predictors are trained offline and during multicore operation they gather online statistical data to predict about-to-be-formed hotspots, promptly informing HPRA to take appropriate hotspot-preventive action(s). Next, in a holistic approach, additional ANN training is performed with data acquired after HPRA interferes, so as to further improve hotspot prediction accuracy; hence, the ANN mechanism does not only predict hotspots, but is also aware of changes that HPRA imposes upon the interconnect infrastructure. Evaluation results, including utilizing real application traffic traces gathered from parallelized workload executions onto a chip multiprocessor architecture, show that HPRA can improve network throughput up to 81 percent when compared with prior-art. Hardware synthesis results affirm the HPRA mechanism's moderate overhead requisites.

Suppressing failure cascades in interconnected networks: Considering capacity allocation pattern and load redistribution

In the modern society, most networked critical infrastructures are coupled together and can be modeled as interconnected networks. Cascading failures caused by overload in interconnected networks are extremely complicated and still lack of corresponding researches. It is crucial to explore a systematic design and load management method for complex interconnected network. In this paper, a probabilistic heterogeneous capacity allocation model and a preventive load redistribution model are firstly proposed. Based on the novel models failure cascades in interconnected networks are studied in detail. Numerical simulations and analysis results show that robustness of interconnected network depends on the network topology, capacity allocation pattern and load management strategy. The novel models have guiding significance for the design and optimal management of robust interconnected infrastructure networks.

A layered approach to model interconnected infrastructure and its significance for asset management

Physical infrastructures facilitate much of societal and economic wellbeing of countries, regions and urban areas. In our increasingly urbanizing world, infrastructures in urban areas are densely located and interconnected. The effects of this interconnectedness are being studied increasingly, particularly in light of climate change effects. In this paper, we develop an agent-based simulation model that allows us to study interconnected infrastructure. We present a layered approach that is analogous to GIS overlay approaches, which allows us to integrate representations of different infrastructures. We explore how this approach can help asset managers to gain insights in interconnected infrastructure by estimating their total damage and repair requirements during a flood event. The results show a difference in these estimates, when compared to non-integrated models, highlighting the need for asset managers to consider interconnectedness in infrastructure.

Securing the smart grid: threats and remediation

The implementation of the smart grid brings many improvements over the traditional energy grid by leveraging a vast interconnected infrastructure that allows two way communication and automation throughout the entire grid. A key difference over the existing grid is the introduction of the advanced metering infrastructure (AMI) which contains many new components such as the smart meter and the communication gateways. The focus of this paper is to examine the possible effects on the energy supply should these devices become compromised. Smart meters are one of the most important components in the smart grid implementation which allow a real time flow of information to/from the consumer on their energy usage. The paper also discusses how closing coal and oil power plant has increased our demand on gas power stations in the UK. Our experiments show that manipulating smart meter readings can create a massive shortfall in gas provisioning.

Guest Editorial New Trends of Demand Response in Smart Grids

The papers in this special section focus on technological and system developments in designing power grids. The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. Now, traditional grids are facing kinds of challenges, and the world is proposing to modernize legacy and make strides toward smart grid. It is widely recognized that demand response is the core feature of smart grid, which can be formally defined as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. With the support of the advanced information and communication technologies, demand response is able to improve the efficiency, reliability, economics, and sustainability of power generation, distribution, and utilization.

Crosstalk Mitigation for High-Radix and Low-Diameter Photonic NoC Architectures

Photonic Network-on-chip (PNoC) is a promising alternative to design low-power and high-bandwidth interconnection infrastructure for multicore chips. The micro ring resonators, which are essential building blocks for designing PNoCs are susceptible to crosstalk that can notably degrade signal-to-noise ratio (SNR), reducing reliability of PNoCs. This paper proposes two novel encoding mechanisms to improve worst-case SNR by reducing crosstalk noise in microring resonators used within high-radix and low-diameter crossbar-based PNoCs.

Deploying 5G-Technologies in Smart City and Smart Home Wireless Sensor Networks with Interferences

Deploying 5G technologies in a combination of smart homes and smart city opens for a new ecosystem with big potentials. The potentials lie in the creation of an advanced ICT infrastructure with support for connected and entangled services possibilities including technologies for efficient communication in an Internet of Things (5G) contexts. In this paper we discuss some of the key challenges that exist in the smart city and smart home networks in the light of possible 5G-solutions. Focus is on deploying cognitive radio technologies (5G) which enables the smart city networks to support interconnected infrastructure elements, to handle big-data from the smart homes, and to be compatible with existing infrastructures. The considered cognitive radio technology is based on pre-coded OFDM which offers the needed flexibility to deal with the key challenges found in the smart home networks. Thus, it is able to overcome the WiFi interferences and the wall penetration losses for a limited power cost. By simulation it has been found that power saving in the range of 10---23 % can be achieved for a small bandwidth cost. Additionally, it has been found that the choice of the smart home gateway location can change its power consumption with 99 %. The developed simulator incorporates interferences, wall losses, and packet error rates, which is elaborated in this paper.

SpiNNaker: Enhanced multicast routing

Abstract The human brain is a complex biological neural network characterised by high degrees of connectivity among neurons. Any system designed to simulate large-scale spiking neuronal networks needs to support such connectivity and the associated communication traffic in the form of spike events. This paper investigates how best to generate multicast routes for SpiNNaker, a purpose-built, low-power, massively-parallel architecture. The discussed algorithms are an essential ingredient for the efficient operation of SpiNNaker since generating multicast routes is known to be an NP-complete problem. In fact, multicast communications have been extensively studied in the literature, but we found no existing algorithm adaptable to SpiNNaker. The proposed algorithms exploit the regularity of the two-dimensional triangular torus topology and the availability of selective multicast at hardware level. A comprehensive study of the parameters of the algorithms and their effectiveness is carried out in this paper considering different destination distributions ranging from worst-case to a real neural application. The results show that two novel proposed algorithms can reduce significantly the pressure exerted onto the interconnection infrastructure while remaining effective to be used in a production environment.

The national broadband network: challenges and opportunities for telecommunications infrastructure planning

The National Broadband Network (NBN) is one of the largest infrastructure projects in Australia's history. NBN Co is currently rolling out the NBN network across Australia which includes a network of interconnected infrastructure such as optical fibre cables, street cabinets, exchange buildings, depots, wireless towers and satellite base stations. Some of this infrastructure is installed in accordance with the utilising NBN Co's powers under schedule 3 of the Telecommunication Act 1997 (Cth) (the Telco Act) in areas of environmental significance as defined in the Telco Act. In these areas NBN Co seeks exemptions or approvals under a range of federal, state and territory legislation. To assist in this process on a national scale NBN Co has developed a mapping tool that combines telecommunications infrastructure design, the location of construction activities and mapped areas of Heritage, Environmental and Cultural (HEC) considerations. This tool forms part of a systematic analysis of potential impacts enabling early identification of potential approval requirements. NBN Co also works with key approval authorities to identify opportunities to streamline approval requirements and identify possible exemptions. Further, the significant improvements and advances in telecommunications infrastructure and access arising from the NBN may be harnessed by the wider planning community in current and future development, plans and strategies as the NBN is expected to change how people live, learn and work.

Simulation Framework for Asset Management in Climate-change Adaptation of Transportation Infrastructure

Climate change will have a big impact on our infrastructure assets. Asset managers need decision support tools that can consider climate change impacts on these infrastructures. Infrastructures have very long life spans and climate change is also a long-term phenomenon. Climate change is also associated with a lot of uncertainty, making it more challenging for asset managers to make investment plans on these infrastructures. Unfortunately, existing tools for asset management are based on short-term operational and financial aspects of maintenance rather than strategic long-term assessments. These tools also treat infrastructure in silos, often ignoring the impact of other interconnected infrastructure on them. In this paper, we present a simulation framework that can help asset managers in assessing long term strategic plans for their assets along with the possibilities to include the uncertain behavior of climate impacts and the impact from other interconnected infrastructure. We use agent-based modelling approach to represent assets, asset owners and asset users as agents and study their interactions and the evolving behavior based on the decisions the agents make to maintain or to use the assets. This approach helps in understanding: the consequences of long-term strategic plans of asset managers and of user behavior, in identifying vulnerable areas in a transportation network, and in estimating the impact from other assets. In our case study on a transportation network, we observed an underestimation of damages from climate events when infrastructures were treated in silos. The framework has a potential to answer wide range of questions for asset managers in the context of climate change and its impact on interconnected infrastructure.

Evaluating the effects of cascading failures in a network of critical infrastructures

Networks of interconnected critical infrastructures are the supporting mechanisms of every industrialised nation. Mutually reliant on each other, their service provisions cross borders. This reliance is also a great weakness. The level of dependence each infrastructure has on another means that a failure has the potential to cascade, resulting in devastating impact on the economy, e-government, defence and society as a whole. Predicting the effects of a cascading failure is a challenge. In this paper, an approach for identifying the effects of a cascading failure is portrayed. A simulation depicting a virtual city is presented, in order to assess the spread of faults originating from a telecommunications infrastructure. Subtle behaviour changes have the potential to spread, with both significant and minor impacts. These variations can be mitigated for using data classification techniques to assess behaviour changes, with an overall accuracy of 85.61% using the TreeC classifier.