Problem In Networks Research Articles

Distributed consensus filtering in sensor networks considering correlated estimation errors

This paper focuses on the distributed state estimation (DSE) problem in sensor networks through the application of a consensus-based methodology. In practical scenarios, the estimation errors within the sensor network exhibit inherent correlation, primarily arising from the consideration of same prior estimate and process noise in a distributed estimation system. However, prevailing distributed approaches, particularly those embedded within consensus-based DSE frameworks, simply assume independence among estimation errors or utilize a suboptimal strategy that neglect cross-covariance matrix, which may lead to a degraded estimation results. To address this limitation, we propose the fusion algorithm Weighted Average Consensus considering Correlated Estimates Errors, abbreviated as WACCEE. The algorithm improves DSE performance by integrating the correlation among local estimation errors as indicated by the cross-covariance matrix into the fusion process. Furthermore, we prove that the proposed WACCEE fusion algorithm exhibits consensus. Additionally, the stability of the WACCEE filters is guaranteed through an analysis of mean-square exponential boundedness of the estimation error using stochastic stability theory. Finally, the effectiveness of the proposed algorithm substantiated by its application to a target tracking case study and a connected undirected sensor network.

A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

Toward a More Robust Estimation of Forest Biomass Carbon Stock and Carbon Sink in Mountainous Region: A Case Study in Tibet, China

Mountainous forests are pivotal in the global carbon cycle, serving as substantial reservoirs and sinks of carbon. However, generating a reliable estimate remains a considerable challenge, primarily due to the lack of representative in situ measurements and proper methods capable of addressing their complex spatial variation. Here, we proposed a deep learning-based method that combines Residual convolutional neural networks (ResNet) with in situ measurements, microwave (Sentinel-1 and VOD), and optical data (Sentinel-2 and Landsat) to estimate forest biomass and track its change over the mountainous regions. Our approach, integrating in situ measurements across representative elevations with multi-source remote sensing images, significantly improves the accuracy of biomass estimation in Tibet’s complex mountainous forests (R2 = 0.80, root mean squared error = 15.8 MgC ha−1). Moreover, ResNet, which addresses the vanishing gradient problem in deep neural networks by introducing skip connections, enables the extraction of complex spatial patterns from limited datasets, outperforming traditional optical-based or pixel-based methods. The mean value of forest biomass was estimated as 162.8 ± 21.3 MgC ha−1, notably higher than that of forests at comparable latitudes or flat regions in China. Additionally, our findings revealed a substantial forest biomass carbon sink of 3.35 TgC year−1 during 2015–2020, which is largely underestimated by previous estimates, mainly due to the underestimation of mountainous carbon stock. The significant carbon density, combined with the underestimated carbon sink in mountainous regions, emphasizes the urgent need to reassess mountain forests to better approximate the global carbon budget.

Predictors of significant distress in cervical cancer patients: a cross sectional study

PurposeThis cross-sectional study aims to investigate parameters that predict relevant levels of distress in women in a perioperative setting undergoing treatment for cervical cancer.Materials and methodsData from 495 patients with cervical cancer that were treated at the university hospital Aachen between 2010 and 2022 were analysed based on their respective National Comprehensive Cancer Network (NCCN) Distress Thermometer score (DT) and Problem List (PL) and their clinical history. 105 patients were enrolled in the study. 18 medical and demographic variables were analysed using multivariate logistic regression.ResultsThree variables contributed significantly to the prediction of a DT score ≥ 5. Significant distress was defined as a DT score of ≥ 5, which was observed in 70.5% of the participants (mean: 5.58 ± 2.892). Women who chose to receive psycho-oncological counselling were more likely to have a DT score ≥ 5 (Odds Ratio(OR) = 3.323; Confidence Interval (CI95%): 1.241–8.900; p-value: 0.017). In addition, women who did not receive chemoradiation had significantly higher DT scores (OR = 3.807; CI 95%:1.185–12.236; p-value: 0.025), as did women whose Distress Thermometer was assessed in the first month after their initial diagnosis (OR = 3.967; CI 95%:1.167–13.486; p-value: 0.027).ConclusionIncreased distress in women with cervical cancer is common especially in the first month after diagnosis, in patients who do not receive chemoradiation and in patients who seek psycho-oncological counselling. Surgical factors do not play a major role in patient distress.

A novel method for solving the multi-commodity flow problem on evolving networks

The minimum cost multi-commodity flow (MMCF) problem on evolving networks is the latest challenge to solving the min-cost network flow problem of multi-commodity flowing from multi-source and multi-sink on an evolving network. Previous researches primarily focus on the solution of MMCF problem within static networks. However, researchers ignored the MMCF problem within networks where the structure dynamically changes over time. Evolving networks have a dynamically changing and unpredictable network structure. The existing methods must recalculate when the network structure changes, leading to high computational complexity. This manuscript proposes a dynamic graph computing model for adaptive dynamic path selection and optimization. First, the model selects the path with the least cost for each commodity, assuming the route can transport a specified number of such commodities to find the optimal path faster. Second, we design path selection and flow update strategies for each node in the graph to adapt to changes in the graph structure. At the same time, we can prevent multiple commodity conflicts from exceeding the marginal capacity limit. Finally, the optimal path of each commodity is dynamically pruned based on the original optical path to obtain the tree structure and determine the absolute path to meet the specified transportation quantity of each commodity. Performance analysis results show that our method can efficiently cope with the dynamic changes of the network structure, which provides ideas for solving the multi-commodity network flow problem in dynamic networks.

SMITS: Social and Mobility aware Intelligent Task Scheduling in Vehicular Fog Computing — A Federated DRL Approach

Vehicular fog computing (VFC) has become an enticing research hot spot to provide resources for the Internet of Vehicles (IoV) application requests. Moreover, with the massive expansion of services in vehicular fog networks such as augmented reality, 3-D gaming, and autonomous driving, the allocation of fog computing resources to IoV applications constraining the deadline and energy consumption of the vehicles is becoming challenging. In this paper, we investigate a task scheduling problem in vehicular fog networks to jointly optimize the success rate of vehicular tasks and energy consumption of vehicles by considering the moving paths of the vehicles and the social relationships among the vehicles. In this problem, we model the social relationship among the vehicles based on their communication patterns and social characteristics to improve the success rate of the tasks. We also incorporate the mobility of vehicles using the Markov renewal process (MRP) technique. Initially, we formulate a mixed integer nonlinear programming (MINLP) problem for the proposed problem and further prove it to be an NP-hard problem. To solve the proposed problem, we design a federated deep reinforcement learning (FDRL) mechanism by converting the problem into a Markov decision process (MDP) problem. We compare our proposed algorithm with existing scheduling approaches, and simulation results show that the proposed algorithm performs efficiently for task scheduling problems. The proposed algorithm achieved a substantial improvement of 12% in the maximization of the success rate and 36% in the minimization of the energy consumption compared to the existing algorithms.

Bounded output‐feedback consensus‐based formation control of nonholonomic vehicles

AbstractIn multi‐agent systems, most commonly, consensus consists in all agents converging to a common equilibrium point. For nonholonomic vehicles, which move on a Cartesian plane and occupy a physical space, it is more appropriate to speak of consensus‐based formation, which implies that the vehicles are required to converge to a relative position in a given formation while, distributively, agreeing on the center of such formation and on their orientation. In this paper we solve such a problem via a smooth, time‐varying, output‐feedback control; more precisely, without velocity measurements. In addition, and this is our main contribution, the controller is designed to satisfy pre‐imposed bounds to avoid input saturation. To the best of the authors' knowledge this is the first controller that solves the consensus‐based formation problem in networks of nonholonomic vehicles via a bounded, output‐feedback controller. The paper also provides a simulation comparison between our proposal and its unbounded counterpart.

Robust set stability of large-scale Boolean networks with disturbance via network aggregation

This paper explores the robust set stability problem of large-scale Boolean networks (BNs) with disturbances by resorting to the network aggregation method. Firstly, the network graph of large-scale BNs is partitioned into several small subnetworks, which converts the robust set stability of large-scale BNs into the (robust) set stability of subnetworks under arbitrary switching signal. Secondly, by perceiving the input nodes from other subnetworks as a switching signal, several new criteria are proposed for the robust set stability of each subnetwork with disturbance inputs under arbitrary switching signal. Finally, by combining the (robust) set stability of all subnetworks, the robust set stability of large-scale BNs with disturbance inputs is discussed. Moreover, as an application, the robust partial stability of large-scale BNs is considered.

Social Behavior Analysis in Exclusive Enterprise Social Networks by FastHAND

There is an emerging trend in the Chinese automobile industries that automakers are introducing exclusive enterprise social networks (EESNs) to expand sales and provide after-sale services. The traditional online social networks (OSNs) and enterprise social networks (ESNs), such as X (formerly known as Twitter) and Yammer, are ingeniously designed to facilitate unregulated communications among equal individuals. However, users in EESNs are naturally social stratified, consisting of both enterprise staffs and customers. In addition, the motivation to operate EESNs can be quite complicated, including providing customer services and facilitating communication among enterprise staffs. As a result, the social behaviors in EESNs can be quite different from those in OSNs and ESNs. In this work, we aim to analyze the social behaviors in EESNs. We consider the Chinese car manufacturer NIO as a typical example of EESNs and provide the following contributions. First, we formulate the social behavior analysis in EESNs as a link prediction problem in heterogeneous social networks. Second, to analyze this link prediction problem, we derive plentiful user features and build multiple meta-path graphs for EESNs. Third, we develop a novel Fast (H)eterogeneous graph (A)ttention (N)etwork algorithm for (D)irected graphs (FastHAND) to predict directed social links among users in EESNs. This algorithm introduces feature group attention at the node-level and uses an edge sampling algorithm over directed meta-path graphs to reduce the computation cost. By conducting various experiments on the NIO community data, we demonstrate the predictive power of our proposed FastHAND method. The experimental results also verify our intuitions about social affinity propagation in EESNs.

Cost Minimization of Digital Twin Placements in Mobile Edge Computing

In the past decades, explosive numbers of Internet of Things (IoT) devices (objects) have been connected to the Internet, which enable users to access, control, and monitor their surrounding phenomenons at anytime and anywhere. To provide seamless interactions between the cyber world and the real world, Digital twins (DTs) of objects (IoT devices) are key enablers for real time monitoring, behavior simulations and predictive decisions on objects. Compared to centralized cloud computing, mobile edge computing (MEC) has been envisioning as a promising paradigm for low latency IoT applications. Accelerating the usage of DTs in MEC networks will bring unprecedented benefits to diverse services, through the co-evolution between physical objects and their virtual DTs, and DT-assisted service provisioning has attracted increasing attention recently. In this paper, we consider novel DT placement and migration problems in an MEC network with the mobility assumption of objects and users, by jointly considering the freshness of DT data and the service cost of users requesting for DT data. To this end, we first propose an algorithm for the DT placement problem with the aim to minimize the sum of the DT update cost of objects and the total service cost of users requesting for DT data, through efficient DT placements and resource allocation to process user requests. We then devise an approximation algorithm with a provable approximation ratio for a special case of the DT placement problem when each user requests the DT data of only one object. Meanwhile, considering the mobility of users and objects, we devise an online, two-layer scheduling algorithm for DT migrations to further reduce the total service cost of users within a given finite time horizon. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results show that the proposed algorithms are promising.

Satisfaction of Management Students towards online Classes during Covid-19 Pandemic in T.U.

This study examines the satisfaction level of Management students towards online classes during covid-19 pandemic in T.U. The effect of Covid-19 on educational institutions has interrupted the traditional method of teaching and learning which ultimately created the demand of online classes. This study aims to find out the perception and satisfaction of MBS Program students who have attended their classes through online mode during the Covid-19 pandemic. To achieve the research objective, the data were collected from the primary source mainly through structure questionnaire under convenience sampling basis from 384 respondents of MBS students of Kathmandu valley who has attended the online classes. Pearson correlation and multiple regression analysis has been used to determine the relationship and impact of the dimension of online classes and the satisfaction level of students. The study revealed that technology plays an important role for the effectiveness of online classes which supports for the enhancement of the satisfaction level of students towards online classes. The study showed that due to the frequently power cut off and unstable internet network problems faced by the students during their online classes. The internet facility available for teaching and learning activities is not so good so that the level of satisfaction of the students towards online classes is decreased. The study also revealed that the instructors and learners both should be updated with the latest and modern technology for the effectiveness of the online classes.

First train timetabling and passenger transfer routing problems in urban rail transit networks

This paper incorporates the shortest passenger travel paths into the first train timetabling problem in urban rail transit networks. We first propose a path-based first train timetabling problem, which is formulated as a mixed-integer nonlinear programming model to reduce the travel time for passengers. To solve this model, we present a tailored branch-and-bound combined with the timetable-based Dijkstra algorithm. To further enhance the efficiency of the exact algorithm, we design a rolling time heuristic algorithm as a substitute for the branch-and-bound algorithm. Then, we proved that the rolling time heuristic combined with the timetable-based Dijkstra algorithm achieves 97.86% and 99.39% reductions in CPU time while sacrificing only 0.007% and 0.057% of optimality gap by two numerical experiments. Finally, the results based on two test networks with different structures demonstrate that the path-based first train timetabling model effectively improves passenger travel efficiency. Specifically, the travel times are reduced by 744 mins and 761 mins, while the transfer waiting times are reduced by 37.87% and 58.91% compared to the existing timetables. Moreover, the results also demonstrate that loop line networks will increase the difficulty of timetabling and lead to many passengers taking detours. To further enhance the efficiency of commuting for passengers who take the first train, URT operators may consider relaxing the limitations on the departure time of the depot stations.

Quasi-synchronization of complex dynamical networks with switched coupling strengths based on a generalized Halanay’s inequality

This paper investigates the exponential quasi-synchronization problem of complex dynamical networks with switched coupling strengths. First, a generalized Halanay’s inequality has been established, which further extends the existing result in Mazenc et al. This inequality not only allows the gain to surpass the decay rate but also helps to cope with quasi-synchronization of complex dynamical networks with switched coupling strengths. Then, in virtue of the obtained inequality, Lyapunov function, and some other important inequalities, sufficient criteria ensuring the quasi-synchronization for the considered system are derived analytically. A numerical example is provided to verify the feasibility of the results.

Optimizing the performance of OpenFlow Protocol over QUIC

In the last decade, Software-defined networking (SDN) has been an eye-catching network architecture. As the essential protocol of SDN, OpenFlow provides the ability to control switches forwarding plane by the remote controller. Currently, OpenFlow protocol is mostly implemented based on TCP. However, due to the limitations of the TCP protocol (e.g., head-of-line blocking), OpenFlow suffers various problems in practical networks, such as performance degradation and increasing network overhead. This paper investigates these issues through experiments and introduces QUIC to handle these issues. Moreover, a scheduling algorithm, called Extended Performance Modular (EPM), is also proposed to further optimize the performance of OpenFlow protocol with QUIC. By considering both message properties and network conditions, EPM effectively utilizes multiple streams of QUIC to avoid head-of-line blocking and improves the efficiency of OpenFlow. The proposed OpenFlow-QUIC with EPM scheme has been implemented and evaluated on both RYU and Open vSwitch (OVS), and the code has been open-sourced. Extensive experiment results show that, compared to traditional OpenFlow, the proposed scheme can reduce applications latency over OpenFlow by 39.3%, while saving 51.6% network overhead on average.

Performance Evaluation of EvolutionaryAlgorithms on Solving the InfluenceMaximization Problem in Social Networks

Performance Evaluation of EvolutionaryAlgorithms on Solving the InfluenceMaximization Problem in Social Networks

Exploring the intra-platoon planning problem for heterogeneous train traffic under virtual coupling in the railway network via simulation

Study on the platoon planning of virtual coupling (VC) is indispensable for both passenger and freight railway operation in terms of capacity, scheduling, and timetabling. As an advanced technology in train operation and control systems, VC can significantly improve the performance of rail operations, with regards to capacity, flexibility and robustness. In this study, we mainly focus on the former one, i.e., to maximize the capacity gains, by employing the relative braking distance based spacing policy for heterogenous train traffic. To our knowledge, this article is the first to address the VC intra-platoon planning problem for a railway network. The main function/purpose of this study is to decide a leader train and allocate an order to the heterogeneous follower trains for the virtually coupled platoon in the off-line mode. The objective is transformed to minimize and compare the sum of minimum safe distance for platoons on the common shared route sections in the railway network from various perspectives, so as to seek the desirable orders of potential train consists within the platoons from the perspective of the whole railway network. Our contributions are mainly threefold. First, the conditions for train platooning under virtual coupling are synthesized. Second, the solution flow charts and algorithms for simulation are proposed, including general framework for simulation, classification of prioritizing the shared common route section, and flow charts and algorithms for decision of coupling order to train platoons. Finally, the proposed methodology was tested and discussed on the designed computational experiment (i.e., numerical simulations of a typical case study with two sets of test examples) via NetLogo platform. The numerical simulation results series revealed some key findings and validated the effectiveness of the proposed methodology. Results of this study can provide guidance to decision makers in timetabling and potential capacity bottleneck identification under VC. We believe that it is worth investigating and further advancing this research direction in the future.

Periodic solution problems of neutral-type stochastic neural networks with time-varying delays

This paper is devoted to investigating a class of stochastic neutral-type neural networks with delays. By using the fixed point theorem and the properties of neutral-type operator, we obtain the existence conditions for periodic solutions of stochastic neutral-type neural networks. Furthermore, we obtain the conditions for the exponential stability of periodic solutions using Gronwall-Bellman inequality and stochastic analysis technique. Finally, a numerical example is given to show the effectiveness and merits of the present results. Our results can be used to obtain the existence and exponential stability of periodic solution to the corresponding deterministic systems.

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

The Shear wave (S-wave) velocity is an essential parameter in reservoir characterization and evaluation, fluid identification, and prestack inversion. However, the cost of obtaining S-wave velocities directly from dipole acoustic logging is relatively high. At the same time, conventional data-driven S-wave velocity prediction methods exhibit several limitations, such as poor accuracy and generalization of empirical formulas, inadequate exploration of logging curve patterns of traditional fully connected neural networks, and gradient explosion and gradient vanishing problems of recurrent neural networks (RNNs). In this study, we present a reliable and low-cost deep learning (DL) approach for S-wave velocity prediction from real logging data to facilitate the solution of these problems. We designed a new network sensitive to depth sequence logging data using conventional neural networks. The new network is composed of one-dimensional (1D) convolutional, bidirectional long short-term memory (BiLSTM), attention, and fully connected layers. First, the network extracts the local features of the logging curves using a 1D convolutional layer, and then extracts the long-term sequence features of the logging curves using the BiLSTM layer, while adding an attention layer behind the BiLSTM network to further highlight the features that are more significant for S-wave velocity prediction and minimize the influence of other features to improve the accuracy of S-wave velocity prediction. Afterward, the nonlinear mapping relationship between logging data and S-wave velocity is established using several fully connected layers. We applied the new network to real field data and compared its performance with three traditional methods, including a long short-term memory (LSTM) network, a back-propagation neural network (BPNN), and an empirical formula. The performance of the four methods was quantified in terms of their coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The new network exhibited better performance and generalization ability, with R2 greater than 0.95 (0.9546, 0.9752, and 0.9680, respectively), RMSE less than 57 m/s (56.29, 23.18, and 30.17 m/s, respectively), and MAE less than 35 m/s (34.68, 16.49, and 21.47 m/s, respectively) for the three wells. The test results demonstrate the efficacy of the proposed approach, which has the potential to be widely applied in real areas where S-wave velocity logging data are not available. Furthermore, the findings of this study can help for a better understanding of the superiority of deep learning schemes and attention mechanisms for logging parameter prediction.

Demand Clustering and Spectrum Allocation (DCSA) problem in P2MP network with time-varying traffic

In this paper, the Demand Clustering and Spectrum Allocation (DCSA) problem in a point-to-multipoint (P2MP) network with time-varying traffic is studied. This article introduces several heuristic techniques that can be applied to traffic aggregation for P2MP optical network architectures employing digital subcarrier multiplexing. Based on the transmitter constraint, the algorithms assign individual subcarriers (SCs) to receiving nodes with uncorrelated traffic profiles. The simulation results show that the proposed algorithms outperform the clustering methods.

Bayesian tensor network structure search and its application to tensor completion

Tensor network (TN) has demonstrated remarkable efficacy in the compact representation of high-order data. In contrast to the TN methods with pre-determined structures, the recently introduced tensor network structure search (TNSS) methods automatically learn a compact TN structure from the data, gaining increasing attention. Nonetheless, TNSS requires time-consuming manual adjustments of the penalty parameters that control the model complexity to achieve better performance, especially in the presence of missing or noisy data. To provide an effective solution to this problem, in this paper, we propose a parameters tuning-free TNSS algorithm based on Bayesian modeling, aiming at conducting TNSS in a fully data-driven manner. Specifically, the uncertainty in the data corruption is well-incorporated in the prior setting of the probabilistic model. For TN structure determination, we reframe it as a rank learning problem of the fully-connected tensor network (FCTN), integrating the generalized inverse Gaussian (GIG) distribution for low-rank promotion. To eliminate the need for hyperparameter tuning, we adopt a fully Bayesian approach and propose an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior distribution sampling. Compared with the previous TNSS method, experiment results demonstrate the proposed algorithm can effectively and efficiently find the latent TN structures of the data under various missing and noise conditions and achieves the best recovery results. Furthermore, our method exhibits superior performance in tensor completion with real-world data compared to other state-of-the-art tensor-decomposition-based completion methods.