Editorial： special issue on “resilient networks： modeling, design, and applications”

The design of resilient networks is of utmost importance today, owing to the wide range of safety-critical applications. Resilient networks try to guarantee an acceptable level of Quality of Service （QoS）, even in cases of challenges and faults in the system. The causes of obstruction in normal system operation range from simple misconfiguration, hardware failures, and software failures to intended attacks and natural disasters.

Evolution, resilience and causes of global petroleum gas trade networks: 1995-2020

Based on the HS 4-digit code trade data in UNCOMTRADE from 1995 to 2020, this paper analyzes the characteristics of the evolution of the global PG trade network using the complex network approach and analyzes the changes in its resilience at the overall and country levels, respectively. The results illustrated that:(1) The scale of the global PG trade network tends to expand, and the connection is gradually tightened, experiencing a change from a “supply-oriented” to a “supply-and-demand” pattern, in which the U.S., Russia, Qatar, and Australia have gradually replaced Canada, Japan, and Russia to become the core trade status, while OPEC countries such as Qatar, Algeria, and Kuwait mainly rely on PG exports to occupy the core of the global supply, and the trade status of other countries has been dynamically alternating and evolving.(2) The resilience of the global PG trade network is lower than that of the random network and decreases non-linearly with more disrupted countries. Moreover, the impact of the U.S. is more significant than the rest of countries. Simulations using the exponential random graph model(ERGM) model revealed that national GDP, institutional quality, common border and RTA network are the determinants of PG trade network formation, and the positive impact of the four factors not only varies significantly across regions and stages, but also increases with national network status.

Spontaneous Recovery in Directed Dynamical Networks

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a jam.Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks.However,most real-world networks are directed.To fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks.We find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network dynamics.Due to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis behavior.In addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery processes.These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

Ecological Network Resilience of Shiyang River Basin: An Arid Inland Watershed of Northwest China

Due to the uncertainties posed by climate change,resilience has become an increasingly important variable for evaluating regional ecosystem stability.The assessment of Ecological Network Resilience(ENR)is crucial for establishing mitigation strategies and sustainable socioeconomic development in arid regions.Shiyang River Basin is an arid watershed in Northwest China with complex characteristics,its ENR and spatial differentiation characteristics in 2020 were investigated in this work based on the Complex Adaptive System(CAS)theory.The results indicated that the mean Ecological Network Resilience Index(ENRI)value for the Shiyang River Basin was 0.390 in 2020,and the mean values in the southern mountainous,middle oasis,and northern desert regions of the basin were 0.598,0.461,and 0.237,respectively,demonstrating evident spatial differences.Additionally,the ENR of the basin exhibited distinct distribution characteristics across different dimension,whereas the trend of the integrated ENR of the basin was consistent with that of elemental,structural,and functional resilience,indicating the constructed three-region ENR model based on the logical relationship of element-structure-function was suitable for evaluation of the ENR in arid inland river watersheds.Furthermore,strategies for enhancing regional ENR were proposed from the perspective of adapting to climate change.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Increasing public safety broadband network resiliency through traffic control

Long Term Evolution （LTE）-based cellular networks are being deployed around the world to provide public safety with enhanced capabilities and access to broadband technology. In the United States, the First Responder Network Authority （FirstNet） is on the verge of deploying a nationwide network called the National Public Safety Broadband Network （NPSBN）. Commercial networks typically aim at maximizing network capacity, i.e. the aggregate data rate, in order to increase revenue. However, in public safety networks, coverage, not capacity, is paramount, especially during an outage when sites are down. Through traffic control and preemption, the service level of low-priority users is reduced or denied, fleeing up resources to restore coverage to high-priority users, e.g. users responding to an incident. In this study, we examine the effect of outages on network coverage and throughput. As our main contribution, we propose three traffic-control schemes that exploit variable modulation and coding, a feature that LTE enhances with respect to its 3G predecessors. The schemes differ based on the proportion of low- and high-priority users preempted. We show that the network coverage can be restored significantly and we investigate the tradeoff between the three schemes. Finally, we perform sensitivity analysis to confirm the effectiveness of the schemes across a wide range of scenarios.

Design of Resilient Sensor Networks Balancing Resilience and Efficiency

In recent years,the notion of resilience has been developed and applied in many technical areas,becoming exceptionally pertinent to disaster risk science.During a disaster situation,accurate sensing information is the key to efficient recovery efforts.In general,resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality,but resilient design may require redundancy and could increase costs.In this article,we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience.The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both twoand three-dimensional spaces.The method provides optimal sensor placement,taking into account critical functionality and a desired level of resilience and considering sensor type and availability.The problem formulation,resilience requirements,and application of the optimization algorithm are described in detail.Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement.Both tasks are successfully solved by the described method,which can be applied to strengthen the resilience of sensor networks by design.The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

Fog-computing based mobility and resource management for resilient mobile networks

Mobile networks are facing unprecedented challenges due to the traits of large scale,heterogeneity,and high mobility.Fortunately,the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity,wide-spread geographical distribution,and elastic resource sharing.In this paper,we propose a novel mobile networking framework based on fog computing which outperforms others in resilience.Our scheme is constituted of two parts:the personalized customization mobility management(MM)and the market-driven resource management(RM).The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits;the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound prices.Synergistically,our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network,which has been practically corroborated by numerical experiments。

Two-step Optimal Allocation of Stationary and Mobile Energy Storage Systems in Resilient Distribution Networks

Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.

Measuring the resilience of an airport network

Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characteristics after the disturbance and measure the robustness of the network with respect to connectivity. The dynamic processes occurring at the node and link levels are often ignored. Here we analyze airport network resilience by considering both structural and dynamical aspects. We develop a simulation model to study the operational performance of the air transport system when airports operate at degraded capacity rather than completely shutting down. Our analyses show that the system deteriorates soon after disruptive events occur but returns to an acceptable level after a period of time. Static resilience of the airport network is captured by a phase transition in which a small change to airport capacity will result in a sharp change in system punctuality. After the phase transition point, decreasing airport capacity has little impact on system performance. Critical airports which have significant influence on the performance of whole system are identified, and we find that some of these cannot be detected based on the analysis of network structural indicators alone. Our work shows that air transport system’s resilience can be well understood by combining network science and operational dynamics.

Resilience-Driven Road Network Retrofit Optimization Subject to Tropical Cyclones Induced Roadside Tree Blowdown

This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones.The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks.A trilevel,two-stage,and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network.In the model,a new metric was designed to evaluate the performance of a road network;resilience was considered from robustness and recovery efficiency of a road network.The proposed model is at least a nondeterministic polynomialtime hardness(NP-hard)problem,which is unlikely to be solved by a polynomial time algorithm.Pareto-optimal solutions for this problem can be obtained by a proposed trilevel algorithm.The random forest method was employed to transform the trilevel algorithm into a singlelevel algorithm in order to decrease the computation burden.Roadside tree retrofit of a provincial highway network on Hainan Island,China was selected as a case area because it suffers severely from tropical cyclones every year,where there is an urgency to upgrade roadside trees against tropical cyclones.We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown,at the same time that it promotes robustness and expected recovery efficiency of the road network.

Urban rail transit disruption management: Research progress and future directions

Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism;ii) substantial negative effects, notably the delay of numerous passengers;iii) an escalating frequency, attributable to the gradual aging of facilities;and iv) severe penalties, including substantial fines for abnormal operation. This article systematically reviews URT disruption management literature from the past decade, categorizing it into pre-disruption and post-disruption measures. The pre-disruption research focuses on reducing the effects of disruptions through network analysis, passenger behavior analysis, resource allocation for protection and backup, and enhancing system resilience. Conversely, post-disruption research concentrates on restoring normal operations through train rescheduling and bus bridging services. The review reveals that while post-disruption strategies are thoroughly explored, pre-disruption research is predominantly analytical, with a scarcity of practical pre-emptive solutions. Moreover, future research should focus more on increasing the interchangeability of transport modes, reinforcing redundancy relationships between URT lines, and innovating post-disruption strategies.

扩展中国保护区以增强气候变化的响应韧性

Expanding the network of connected and resilient protected areas(PAs)for climate change adaptation can help species track suitable climate conditions and safeguard biodiversity.This is often overlooked when expanding PAs and quantifying their benefits,resulting in an underestimate of the benefits of expanding PAs.We expanded PAs through terrestrial mammalian species distribution hotspots,Key Biodiversity Areas(KBAs),and wilderness areas.Then,we constructed climate connectivity networks using a resistance-based approach and further quantified the network resilience to propose resilient climate response strategies in China.The results showed that existing PAs suffered from location biases with important biodiversity areas.The existing PAs represented about half of the KBAs and wilderness areas,yet only 12.08%of terrestrial mammalian species distribution hotspots were located within existing PAs.Compared with the existing PA network,the network efficiency and resilience of the expanded PAs'climate connectivity increased to 1.80 times and 1.78 times,respectively.With 56%of the nodes remaining,the network efficiency of the expanded PAs was equivalent to that of the existing PAs with all nodes.The network resilience of preferentially protecting and restoring low human footprint patches was approximately 1.5–2 times that of the random scenario.These findings highlighted that confronted with the unoptimistic situation of global warming,nature conservation based on existing PAs was no longer optimal.It was critical to construct a connected and resilient conservation network relying on both important biodiversity areas and low human footprint patches.

Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

Rapid plant immune responses in the appropriate cells are needed for effective defense against pathogens.Although transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is challenging.We reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in Arabidopsis.Double-peak time-course patterns are prevalent among thousands of upregulated genes.We implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each gene.The decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated genes.We demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to AvrRpt2.Thus,the peak decomposition has extracted spatial information from the time-course data.The echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered immunity.Activation of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor types.We discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

A Probabilistic Approach to the Evaluation of Seismic Resilience in Road Asset Management

Road networks are classified as critical infrastructure systems.Their loss of functionality not only hinders residential and commercial activities,but also compromises evacuation and rescue after disasters.Dealing with risks to key strategic objectives is not new to asset management,and risk management is considered one of the core elements of asset management.Risk analysis has recently focused on understanding and designing strategies for resilience,especially in the case of seismic events that present a significant hazard to highway transportation networks.Following a review of risk and resilience concepts and metrics,an innovative methodology to stochastically assess the economic resources needed to restore damaged infrastructures,one that is a relevant and complementary element within a wider resilience-based framework,is proposed.The original methodology is based on collecting and analyzing ex post reconstruction and hazard data and was calibrated on data measured during the earthquake that struck central Italy in 2016 and collected in the following recovery phase.Although further improvements are needed,the proposed approach can be used effectively by road managers to provide useful information in developing seismic retrofitting plans.