Efficient Network Operation Research Articles

Roadmap development for the deployment of virtual coupling in railway signalling

Developments in the railway industry are continuously evolving and long-term transition strategies can enable an efficient implementation of signalling technologies that provide a significant increase in network capacity and operation efficiency. Virtual Coupling (VC) advances moving block signalling by further reducing train separation to less than an absolute braking distance using train-to-train communication and cooperative train control within a Virtually-Coupled Train Set (VCTS). This paper proposes a method to develop scenario-based roadmaps based on a SWOT and hybrid Delphi-AHP multi-criteria analysis. Step-changes are identified and initially assessed in a Swimlane based on priorities and time order collected from stakeholders through a survey and further developed in a workshop. Optimistic and pessimistic scenarios are assessed regarding various factors and timelines. Step-changes are initially defined in a Swimlane and then enriched with optimistic and pessimistic scenarios to ultimately derive scenario-based roadmaps. Durations for each of the step-changes are developed into scenario-based roadmaps that can be used as an efficient tool for stakeholders to identify and solve potential criticalities/risks to the deployment of VC as well as to setup investment and development plans. The approach is applied to deliver implementation roadmaps of VC for different market segments with particular focus on mainline railways.

Self-Attention Mechanism-Based Multi-Channel QoT Estimation in Optical Networks

It is essential to estimate the quality of transmission (QoT) of lightpaths before their establishment for efficient planning and operation of optical networks. Due to the nonlinear effect of fibers, the deployed lightpaths influence the QoT of each other; thus, multi-channel QoT estimation is necessary, which provides complete QoT information for network optimization. Moreover, the different interfering channels have different effects on the channel under test. However, the existing artificial-neural-network-based multi-channel QoT estimators (ANN-QoT-E) neglect the different effects of the interfering channels in their input layer, which affects their estimation accuracy severely. In this paper, we propose a self-attention mechanism-based multi-channel QoT estimator (SA-QoT-E) to improve the estimation accuracy of the ANN-QoT-E. In the SA-QoT-E, the input features are designed as a sequence of feature vectors of channels that route the same path, and the self-attention mechanism dynamically assigns weights to the feature vectors of interfering channels according to their effects on the channel under test. Moreover, a hyperparameter search method is used to optimize the SA-QoT-E. The simulation results show that, compared with the ANN-QoT-E, our proposed SA-QoT-E achieves higher estimation accuracy, and can be directly applied to the network wavelength expansion scenarios without retraining.

T-Vision: A hybrid subsurface radar inspection system for intelligent asset management of railway tunnels

As global rail network demand grows and historic tunnels continue to age, it is vital to detect Hidden Critical Elements (HCEs) – e.g. blind shafts and ring separation – in Rail Tunnel Subsurface Inspection (RTSSI) surveys to maintain safe and efficient network operation. Presently, no standalone RTSSI technology can swiftly nor accurately detect HCEs throughout a 3600 tunnel subsurface profile. In response, our European research project T-Vision investigates and proves the feasibility of the first air-launched, hybrid rotational ground penetrating radar system for RTSSI. We deploy the system's unique raster and helical scanning functionality in Kirton Tunnel (UK) to inspect the subsurface. Artefacts corresponding to blind shafts validate raster scanning efficacy for concealed shaft detection; whilst development scope for novel 3600 Hackle-Helix Point Clouds for RTSSI is discussed.

A Security Assessment of HTTP/2 Usage in 5G Service-Based Architecture

Fifth generation (5G) networks are designed to bring enhanced network operational efficiency to serve a wide range of emerging services. Toward this purpose, 5G adopts a service-based architecture (SBA) that features web-based technologies such as Hypertext Transfer Protocol version 2 (HTTP/2) used for signaling and application programming interfaces (APIs) for service delivery. Several works in the literature have reported that the shift toward the aforementioned technologies brings potential cybersecurity challenges to the 5G network. In this article, we discuss different security features introduced by 5G SBA and explore these security challenges and their solutions in this new architecture. We carefully examine HTTP/2 features, standards, and custom headers, and discuss their security implications in 5G SBA. We comment on the applicability of some known HTTP/2 attacks in 5G SBA in light of the standardized APIs, and discuss the security opportunities and research directions brought by this protocol and its related technologies.

Optimization of the performance parameters of the Giga passive optical networks for high transmission data rates with various modulation schemes

Abstract This article has demonstrated the performance parameters optimization of the Giga passive optical networks (PONs) can enhanced with high transmission data rates with various modulation schemes. These modulation schemes are Mach–Zehnder modulator (MZM), electroabsorption modulator (EAM), amplitude modulator (AM), dual drive MZM measured (DDMZMM), electroabsorption modulator measured (EAMM), and LiNb MZM. The network reach can be extended to 30 km with 20 Gb/s. Maxi Q and mini bit error rate can be measured at both PIN and APD receivers. The study has emphasized AM modulation scheme is the best candidate modulation scheme for upgrading the network performance efficiency. LiNb MZM is the worst modulation scheme for the estimation of PONs performance. Optical power signals after fiber optic channel based different modulation schemes are clarified. EAM modulation scheme is the best technique for upgrading optical power signal through the network reach.

Rapid transient operation control method of natural gas pipeline networks based on user demand prediction

The natural gas pipeline networks play a vital role in Integrated Energy System (IES). Simultaneously, with the increase in the number and types of users, the operational model needs to be transformed from plan-oriented to demand-oriented. Hence, higher requirements are put forward for the flexibility and rapidity of the control methods. In this paper, a novel rapid operation control method for natural gas pipeline networks is proposed based on multi-user demand prediction and pipeline flow state inversion. First, the Nonlinear autoregressive (NAR) neural network is used to analyze the historical consumption of various users and predict each user's demand in the short term. Second, the rapid transient inversion method is derived from the partial differential control equation to realize the inverse of the pipeline flow state. Third, combined with the equipment model, the control method is formed to realize rapid control of the pipeline networks. The control method is applied to classical triangular networks and existing pipeline networks to demonstrate accuracy and effectiveness. The time-consuming ratio of the commercial software and proposed model for the triangular pipeline networks is 1:0.22. For the existing pipeline networks, six users' average demand prediction errors are 5.58%, and the control errors of pressure at three markets are 1.00%, 1.22%, and 0.81%. The results show that the proposed method can respond to users' demands rapidly and provide decision support for the safe and efficient operation of the natural gas pipeline networks.

From Network Inference Errors to Utility Suboptimality: How Much Is the Impact?

Accurate and timely network inference, known as network tomography, is a vital ingredient in efficient network operation, supporting more sophisticated and ambitious traffic optimization algorithms. In practice, however, precise knowledge of the dynamic internal network bottlenecks/states can be impossible to obtain under restricted network visibility on network edge where only end-to-end measurements are available without cooperation of internal network components. In this paper, we acknowledge that network tomography on the edge is often imperfect, leading to dynamic, time-varying inference errors that could change from iteration to iteration on the same time-scale as distributed network optimization algorithms. We quantify the impact of such imperfect bottleneck/state inference on algorithm convergence and optimality. In particular, we show that under arbitrary, bounded inference errors (belonging to three common classes including absent and incorrect bottlenecks, and inaccurate capacity), the solution of the distributed optimization algorithm still converges to a bounded neighborhood of the optimal solution. The resulted optimality gap is quantified in closed form and shown to be proportional to average inference errors. These results are evaluated using extensive network simulations and on real-world IoT data sets. The work provide a theoretical support for understanding the impact of imperfect inference on distributed network optimization.

What is in a Meter? A Qualitative Exploration into the Implementation of Electricity Metering Across Mumbai Communities Using Normalisation Process Theory

Metering is fundamental in the efficient operation of electricity networks, as meters facilitate controlled usage and improve health and well-being. However, across the Global South, meters have often been found to be lacking or not fit for purpose. Therefore, this study sought to determine residents’ perceptions and access to electricity metering across a community in Mumbai, with the goal of developing recommendations to support the implementation of meters in the future. Fifty semi-structured interviews were conducted by phone, with participants from different areas and socioeconomic classes, within Greater Mumbai. The sample consisted of 20 low-income, 20 middle-income, and 10 high-income participants. The Normalisation Process Theory (NPT) was used to inform the interview schedule and to organise the thematic analysis. Meter accessibility and location was variable across the participant groups, as was the education and awareness of metering technology. Socio-political factors were found to directly affect the use of meters, specifically in the low-income group. The high cost associated with metering was a prominent finding; with a preconception that introducing meters would only increase utility expenditure. Future work should focus around ensuring meters are easy to use, practical and accessible to all residents and supporting education programmes around how to use a meter and how they can reduce utility expenditure. The cost of meters should also be investigated, to establish that the costs, associated with introducing new meters, are not passed disproportionately to consumers.

Dynamic Path Planning Based on Service Level of Road Network

Vehicle–road coordination is an important application scenario in the sustainable development of urban transportation. In this scenario, by navigating the vehicles in the road network, the vehicles can run more smoothly in the city, reduce unnecessary detours and parking, and realize energy savings and emission reductions. Although vehicle–road coordination in a large area has not been fully realized, people’s travel is increasingly dependent on navigation. If the trips of most vehicles follow the same navigation suggestion in a short period of time, some sections in the given route of the navigation will bear excessive traffic load. In order to solve this potential problem, this paper relies on the vehicle–road collaboration scenario and combines the service level of the road network factors between vehicles to plan the travel path of the vehicle. This keeps the traffic load of each road section in the path at a reasonable level. Within the scope, considering the overall utilization of road resources and the efficiency of road network traffic, we established the road network evaluation index through the simulation comparison with the Dijkstra algorithm. Under the path planning method proposed in this paper, the total travel time of the vehicle is reduced by 23.4%, and the road network operation efficiency is improved by 6.6%, which proves that the method can be used. This method can effectively alleviate the load of the road network, improve operation efficiency, and finally achieve the purpose of energy saving and emission reduction.

Network Rebalance and Operational Efficiency of Sharing Transportation System: Multi-Objective Optimization and Model Predictive Control Approaches

Sharing transportation systems can significantly promote travelers convenience and efficiency. As a vital part, bike-sharing system (BSS) has effectively solved “the-last-mile” problem during transportation interchange, but bike imbalance between docks severely deteriorates operational efficiency of BSS. In this paper, we present multi-objective optimization and predictive control approaches to tackle the bike rebalancing problem, where optimal redistributing strategies can maximize the operational efficiency of BSS with respects to equilibrium state and redistribution cost. A dock-based BSS dynamic network is modeled based on a proximity graph, in which connection relation, bike usage, and redistribution flow are formulated. To measure the operational efficiency, a performance metric is presented to consider both benefits of user and operator. To satisfy bike renting, returning, and redistributing requirements, model predictive control (MPC) is then employed to compute feasible and optimal redistribution strategies based on the network model. The effectiveness of multi-objective optimization and MPC is verified on different topologies of BSS. Experimental results show that when the BSS reaches the equilibrium state, the operational efficiency will be maximized in the proposed approaches.

Research on Structural Toughness of Railway City Network in Yellow River Basin and Case Study of Zhengzhou 7–20 Rainstorm Disaster

With the gradual networking of inter-city relations and the increase in acute impact and chronic stress, the measurement of the resilience of urban network structures is particularly prominent. Based on the construction of the urban network by passenger train trips in the Yellow River Basin, this paper analyzes and assesses the characteristics of the structural resilience of the urban network, and probes into the network resilience and urban response under the circumstances of node failure and line failure in Zhengzhou. The main conclusions are as follows: (1) The urban network in the Yellow River Basin was clearly hierarchical, with a significant spatial distribution of “low in the north and high in the south”, and the overall characteristics of “robustness” in small areas and “fragility” in large areas. The network connection forms were diversified and open. The network transmission efficiency was high, and the edge cities depended on the core cities with prominent characteristics, and the risk load of regional core cities rose. (2) The network structure was “robust” as it maintained high operational efficiency and connectivity under random attacks. Under deliberate attacks, the city network operated efficiently with a small increase in connectivity before the 60% threshold, and after the threshold, the overall network started to split into many sub-networks, and the network fragmentation gradually increased until the network collapsed. (3) Zhengzhou node failure and line failure states in the Yellow River Basin urban network were resilient, in the sense that when suffering important nodes and lines going down it could still maintain good network operation efficiency, and the core nodes in the impact of natural disasters could adapt to the destructive nature of the network through the urban network structure self-regulation.

Систематизация гидравлического расчета металлических сетей водоснабжения и водоотведения с внутренними отложениями на стенках труб

Introduction. The purpose of the research is to establish the criterion for assessing the pipeline operation efficiency and verify the accuracy of hydraulic design of metal pipes that have internal deposits. Materials and methods. The authors provide calculated dependences to determine the values of the characteristics of the hydraulic potential of metal pipes having internal deposits. A specific case is employed to compare the characteristics of new pipes and pipes having different thicknesses of the layer of internal deposits. The percent discrepancy between pipe characteristics is provided. Results. The authors recommend evaluating the accuracy of the hydraulic design of water supply and sewerage networks having internal deposits in reliance on the value of the operation efficiency coefficient for water supply or sewerage networks. The authors offer a formula derived to determine the value of the efficiency coefficient for the operation of water supply and sewerage networks having internal deposits on inside walls of a pipe. The authors suggest systematizing the hydraulic design of metal water supply and sewerage networks that are made of metal and have deposits on the inside of pipe walls by taking into account the thickness of the layer of internal deposits, characterized by the value of the network operation efficiency coefficient. Conclusions. Systematization of hydraulic design of water supply and sewerage networks that are made of metal and have deposits on the inside of pipe walls allows for the acceptable accuracy of its hydraulic design that takes account of the actual value of the coefficient of efficiency of pipeline operation Kef, taking into account the value of actual thickness of deposit layer σact. In other words, systematization of hydraulic design of water supply and sewerage networks, having deposits on the inside surface of metal pipes, is based on the value of their efficiency coefficient Kef, that takes into account the actual hydraulic potential of pipes (pipelines) depending on the layer of internal deposits σact.

Information gap-based coordination scheme for active distribution network considering charging/discharging optimization for electric vehicles and demand response

The power fluctuation of load and distributed generation is a leading cause of load and voltage fluctuations and directly impacts the safe and efficient operation of the active distribution network in recent years due to the growing penetration of electric vehicles and distributed generation in the network. To optimize the load characteristics, a demand response model based on a logistic function is introduced, and the dynamic charging/discharging electricity price of electric vehicles based on the comprehensive load is considered in order to incentivize electric vehicle users to charge/discharge orderly. Then, a coordinated optimal model based on the information gap decision theory is constructed to cope with the uncertainty of distributed generation and ensure the operational economy and robustness of the active distribution network. Additionally, a population-based multi-objective state transition algorithm with a parallel search mechanism and communication mechanism was proposed to solve the demand response model, and second-order cone programming was utilized to solve the coordinated optimal model with complex constraints. Finally, the proposed comprehensive model and algorithm were investigated based on the IEEE 33 test system. The results show that the proposed model not only reduces the peak-to-valley difference in load by 24.38% but also significantly reduces the total operating cost by 13.41%. Simultaneously, the proposed algorithm increases the solving efficiency, verifying the feasibility and effectiveness of the optimal strategy and algorithm, which promotes the economical and reliable operation of the active distribution network.

A rolling-horizon decision framework for integrating relocation and user flexibility in one-way electric carsharing systems

One-way electric carsharing system has become the most widely used form of carsharing service in practice. However, the demand uncertainty, car imbalance, parking shortage, and battery charging jointly impose great challenges upon the operation. Many carsharing operators, e.g., GoFun, offer incentives to flexible users for accepting alternative itineraries and also adopt a full reservation policy. In this paper, we propose a rolling-horizon decision framework that integrates the operator’s car and staff relocation decisions, user flexibility, and charging of electric cars in the real-time setting. We formulate the problem at each decision epoch as a time–space network flow model that simultaneously optimizes the decisions. We develop an iterated local search heuristic algorithm to solve the model for large-scale practical problems. Based on theoretical insights and clustering of stations, we develop new local search procedures customized for the dynamic carsharing problem. We use real-world data from GoFun as an example to conduct extensive computational experiments. The results show that our algorithm significantly outperforms a particle swarm optimization algorithm and the popular greedy policy, and also has a low efficiency loss compared to the off-line heuristic solution. The transportation network size, clustering size, and operational efficiency are revealed to have significant impacts on the profit and service level.

Universal phase shifter regulator system modeling with robust GPC using neural networks for compensation power in transmission line

Electricity consumption is increasing gradually and this trend will continue in the future. In addition, rapid network control systems using the resources offered by power electronics and control microelectronics have been recently studied and developed, and are currently in normal application for some, for others, in pilot applications or as prototypes. This paper attempts to show that these systems are referred to by the general acronym flexible alternative current transmission systems (FACTS) similarly dethroned the traditional systems while offering better solutions and solving the energy quality problem such as the hybrid system (unified power flow controller (UPFC), or universal phase shifter regulator (UPSR)) which opens up new perspectives for more efficient operation of networks by continuous and rapid action on the various parameters of the network (voltage, phase shift, and impedance); thus, the power transits will be better controlled and the voltages better held, which will make it possible to increase the stability margins or tend towards the thermal limits of the lines. In this work, we used a classic control (PI-decoupled) and others while offering more flexibility of control thanks to the development of strategies identification/control based on generalized predictive control (GPC) with neural network to ensure robust control with advanced algorithms.

Medley: A Membership Service for IoT Networks

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings wherein nodes are connected via a wireless ad-hoc network. Our failure detector, named Medley, is fully decentralized, allows IoT nodes to maintain a local membership list of other alive nodes, detects failures quickly (and updates the membership list), and incurs low communication overhead. We adapt a failure detector originally proposed for datacenters (SWIM), for the IoT environment. This adaptation is non-trivial. In Medley each node picks a medley of ping targets in a randomized and skewed manner, preferring nearer nodes. We also provide optimizations to achieve time-bounded detection, as well as to reduce tail detection times. Via analysis, simulation, and Raspberry Pi deployments, we show that Medley can simultaneously optimize detection time and communication traffic.

An Enhanced Cell Transmission Model for Multi-Class Signal Control

Existing multi-class cell transmission model (CTM) based methodologies for signal timing or traffic assignment may transfer prioritized transit vehicles from one cell to the next one before processing their preceding passenger cars. In addition, existing CTM-based methodologies process a proportion of a slow-moving transit vehicle in each time step. As such a portion of each transit vehicle remains in each cell and it never clears them. This paper presents constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the CTM. The resulting optimization program is a mixed-integer nonlinear problem. We used a distributed receding horizon control framework to solve it in real-time. The proposed formulation is executed in a simulated arterial street with four signalized intersections in Springfield, IL with different traffic volume levels and transit vehicle frequencies. The results showed that the proposed algorithm addressed the mentioned issues of the existing multi-class CTM, and yielded more efficient network performance than the conventional transit signal priority-based (CTSP) systems. The proposed formulation reduced average bus delay by 1% to 70% and car delay by 52% to 76% compared to CTSP.

Resilience Assessment of Multimodal Urban Transport Networks

With the development of urbanization and the evolution of urban network systems, multimodal urban transport network (MUTN) systems play a vital role in improving network effects and operational efficiency. However, urban transport networks are easily affected by natural disasters and traffic incidents, which can lead to significant human and economic losses. Accordingly, it is vital to be able to assess the resilience of transport networks in the face of various disruptions. This study, therefore, utilizes complex network theory to analyze the resilience of multimodal urban transport networks, with the resilience accessed based on topological indices. The MUTN in Beijing is selected as a case study for simulation analysis. Based on the road network and subway network, a model MUTN is established, and the Monte Carlo method is used to simulate random attacks. The results show that the MUTN in Beijing has good resilience against disruptions. This study guides the evaluation of the overall resilience of multimodal urban transport networks and will be useful for transportation planners and decision-makers in dealing with emergencies and natural disasters in the future.

Spoofing Attack Mitigation in Address Resolution Protocol (ARP) and DDoS in Software-Defined Networking

Software Defined Networking (SDN) shows network operations to be performed for efficient network operations. Due to the increase in network devices, the percentage of attacks is also increased, and it is challenging to provide defense against such attacks. In SDN, the control plan is separated from the data plane. The control plan is implemented using some central devices called SDN controllers. In SDN Address Resolution Protocol (ARP), spoofing and Distributed Denial of Services (DDoS) attacks are carried out on an enormous scale. These are commonly launched attacks in SDN. Due to these attacks, the network performance is down, and network services are dead. This paper proposed a new auto detection methodology to detect ARP and DDoS attacks and mitigate SDN networks from these attacks. Additionally, we implemented two algorithms: one for flow rules and the second for attack detection. An individual server was installed to check the malicious traffic installation. We present the new forward flooding rules to detect and mitigate attacks. The experiments are performed using LINUX-based network implementation. Our proposal successfully improves network security and enhances network efficiency.

Smart contract-based caching and data transaction optimization in mobile edge computing

The emergence of mobile edge computing can provide technical support for the development of Internet services and for coping with massive data traffic, thereby reducing network latency and ensuring efficient network operations and service delivery. However, the mobile edge computing environment is prone to data loss and privacy leakage, and data security and reliability cannot be guaranteed. The application of blockchain technology ensures the stability and dependability of data caching and transactions. In order to guarantee the security of data caching in the mobile edge computing environment and minimize the response time of caching servers, this paper proposes a decentralized data caching strategy in the mobile edge computing environment. The strategy uses a greedy algorithm to make the transmission delay of the requested content as small as possible under the multiple constraints of the storage space of each server and whether the content is cached on the server. To address the problem that data trading platforms are unable to afford the computation and storage of massive amounts of data and cannot guarantee that the data will not be leaked, this paper proposes a secure decentralized data transaction program. The scheme promotes the increase of transaction turnover rate and improves the revenue of both participating parties by establishing a buyer–seller matching algorithm. According to the experimental results, the proposed data caching strategy can improve the cache hit rate and reduce the transmission delay; the proposed data transaction solution can increase the revenue of both the data holder and the data buyer.