Network Topology Changes Research Articles

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain.

Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database via a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.

Formation optimization of blockchain-assisted swarm robotics systems against failures based on energy balance

The blockchain-assisted swarm robotics system, as an emerging distributed multi-robot system, has been widely discussed and studied for its outstanding advantages in secure communication, data logging and consensus achievement. Due to faults, formation changes, and obstructions, the network topology and communication rate change in the process of multi-robot collaboration, which inevitably affect the stability and operation efficiency of the swarm robotics system. However, few studies have quantitatively assessed the degradation of system stability performance after failure and how to respond to failure of team members and optimize the system for failure. In this paper, the concept of “energy” is introduced into the quantitative evaluation of system failure resistance, and an evaluation model based on communication distance, communication quality and communication volume is proposed, which can evaluate the stability of swarm robotics systems at different moments and any member in real-time collaboration. As the stability improves, the “energy” increases, and vice versa. Additionally, the “energy” balance is applied to the optimization process of multi-robot formation, and the optimization equation based on the efficiency of swarm robots is proposed. The optimal formation efficiency equation of the system at a certain moment is obtained by jointly solving the energy optimization equation and the system. The simulation results show that the proposed formation optimization method can improve the stability of the swarm robotics system at any time and in all directions, and maximize the operational efficiency of the queue.

Joint power control and multiple antennas optimization for reducing receiver blocking in dense VANETs

The dynamic topology of vehicular ad hoc networks (VANETs) has a significant impact on the transmission and reception of data. It will cause receiver blocking due to the increase in the number of vehicles on the road and the rapid change of network topology. How to reduce the blocking and improve the reliability of data transmission is still a challenging problem. In this paper, we first propose a joint distributed power control and multiple antennas (DPC-MA) scheme, which can minimize blocking and simultaneously improve throughput. Moreover, it works well with an increased number of vehicles. Then we build an optimization model to select the optimal transmission power and number of antennas for vehicles based on their neighborhood relationships. The formulated problem is a mixed-integer nonlinear programming problem (MINLP), which is NP-hard and usually has no feasible solution. To solve this problem, we propose a low-complexity efficient algorithm using Lagrangian duality, which decomposes the original problem into a series of sub-problems and solves them iteratively using the fixed-point iterative algorithm and sub-gradient method. Simulation results show that our proposed scheme has better performance in resolving blocking and achieving high throughput, especially in high-density vehicle scenarios.

Multidirectional recovery strategy against failure

The recovery strategy of complex networks is an important topic in the research of network characteristics. However, previous studies only restore nodes unilaterally and ignore the changes of network topology in the process of recovery and failure. In order to break through the sidedness of the previous researches, this paper proposes a multidirectional recovery strategy. This method takes into account the form of network fragmentation (clustering and isolated failure nodes) and reinforces the topology centered on the hub nodes. The method takes fully account of the cascading failure characteristics of the interdependent network. In addition, several synthetic networks are used to verify the effectiveness of the recovery strategy. We discuss the influence of the parameters a,β on the multidirectional recovery strategy and compare it with the strategy of important nodes and common boundary nodes. The experimental results show the multidirectional recovery strategy can effectively improve the robustness of the network at the appropriate cost. These findings provide an effective reference for the recovery strategy of multi-layer network and are of great significance to the recovery of actual networks.

EEG alpha band functional connectivity and network structure mark hub overload in Mild Cognitive Impairment during memory maintenance

IntroductionWhile decreased alpha-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences mark cognitive decline in the early stages of the disease.ObjectivesOur study aimed to analyze EEG FC and network differences in the alpha frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints.MethodsFC and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task. FC was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree approach.ResultsIncreasing memory load enhanced the mean alpha-band FC in the control group. In contrast to that, after an initial increase, the MCI group showed significantly (p<0.05) diminished FC in the highest memory load condition. Mean alpha AEC-c correlated significantly with the size and mean diffusivity of medial temporal lobe structures in the entire sample. The network analysis revealed a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution compared to the control group.ConclusionsAlpha-band FC correlates with cognitive load-related modulation, with medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism.DisclosureNo significant relationships.

An Enhanced Sparrow Search Based Adaptive and Robust Data Gathering Scheme for WSNs

Wireless Sensor Networks based on Mobile Sinks (MS) are becoming more popular because of their benefits in enhancing performance and minimizing unequal energy usage of the nodes. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in response to node failures. Also, it is difficult to plan an efficient path for data gathering by MS using a set of Rendezvous Points (RPs) that could reduce the MS travel time while reducing the WSN energy usage. In this paper, we propose an Enhanced Sparrow Search based Adaptive and Robust Data Gathering Scheme for WSNs (ESRP-MP) to create shorter travel route for MS and minimize data gathering latency. For effective utilization of RPs, the best RP locations that maximize the coverage of nodes and minimize the overlap in coverage with adjacent RPs are considered. Also, the algorithm is adaptive to node failures by updating the RPs and refining the planned MS path when necessary. In the first phase, we introduce an enhanced Sparrow Search based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, the shortest tour through the RPs is obtained using Ant Colony Optimization (ACO). The results reveal the effectiveness of ESRP-MP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime.

Optimal control of the reaction–diffusion process on directed networks

Reaction–diffusion processes organized in networks have attracted much interest in recent years due to their applications across a wide range of disciplines. As one type of most studied solutions of reaction–diffusion systems, patterns broadly exist and are observed from nature to human society. So far, the theory of pattern formation has made significant advances, among which a novel class of instability, presented as wave patterns, has been found in directed networks. Such wave patterns have been proved fruitful but significantly affected by the underlying network topology, and even small topological perturbations can destroy the patterns. Therefore, methods that can eliminate the influence of network topology changes on wave patterns are needed but remain uncharted. Here, we propose an optimal control framework to steer the system generating target wave patterns regardless of the topological disturbances. Taking the Brusselator model, a widely investigated reaction–diffusion model, as an example, numerical experiments demonstrate our framework’s effectiveness and robustness. Moreover, our framework is generally applicable, with minor adjustments, to other systems that differential equations can depict.

Transportation System Vulnerability Assessment considering Environmental Impact

Transportation system has a close bearing on the prosperity of society. However, transportation infrastructures are highly vulnerable to extreme events. Therefore, identifying the most critical component of the transportation system is among the first priorities of transportation network planners and managers. This paper proposes a novel framework to identify the most vulnerable component for a road transportation system. A key characteristic of vulnerability assessment is the travelers’ response to the changes in the transportation network topology and capacity after an extreme event. Hence, the problem is formulated as a nonlinear programme with equilibrium constraints, considering travelers’ route choice behavior. In the methodology, two types of vulnerability measures are developed to assess the vulnerability of road transportation system, namely, system travel time-based vulnerability (STTV) and system emissions-based vulnerability (SEV). The former is developed on the basis of short-term planning, while the latter is put forward on the basis of long-term planning. With these vulnerability measures, the proposed framework is then demonstrated using an extended Nguyen-Dupuis network under different demand levels and different capacity degradation levels, taking into account two modes: bus and car. The results indicate that different vulnerability measures can identify similar vulnerable components. Moreover, it is shown that the SEV can find more critical components than the STTV regardless of capacity degradation or demand growth. Our research helps to create a recovery plan by assigning priority to the critical transportation infrastructures.

Cascading Failure Analysis of Hierarchical Industrial Wireless Sensor Networks under the Impact of Data Overload

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.

Recursive state estimation for nonlinear coupling complex networks with time-varying topology and round-robin protocol

This paper is devoted to solving the recursive state estimation (RSE) issue for a class of complex networks (CNs) with Round-Robin (RR) protocol and switching nonlinearities (SNs). A random variable obeying the Bernoulli distribution with known statistical properties is introduced to describe the switching phenomenon between two nonlinear functions. A Gaussian noise and time-varying outer coupling strength are adopted to show the changeable network topology (CNT). The RR protocol is applied to regulate signal transmissions, which determines that the element in measurement output has access to the communication networks at each step. The purpose of this paper is to construct a recursive state estimator such that, for all SNs, time-varying topology and RR protocol, the expected state estimation performance is guaranteed. Specifically, based on two recursive matrix equations, the covariance upper bound (CUB) of state estimation error is obtained firstly and then minimized via designing estimator gain in a proper way. Moreover, a feasible criterion is given to guarantee that the trace of obtained CUB is bounded and a monotonicity relationship is established between state estimation error and time-varying outer coupling strength. Lastly, a simulation experiment is illustrated to verify the feasibility of the addressed estimation method.

Network specialization: A topological mechanism for the emergence of cluster synchronization

Real-world networks are dynamic in that both the state of the network components and the structure of the network (topology) change over time. Most studies regarding network evolution consider either one or the other of these types of network processes. Here we consider the interplay of the two, specifically, we consider how changes in network structure effect the dynamics of the network components. To model the growth of a network we use the specialization model known to produce many of the well-known features observed in real-world networks. We show that specialization results in a nontrivial equitable partition of the network where the elements of the partition form clusters that have synchronous dynamics. We also show that these synchronizing clusters inherit their ability to either locally or globally synchronize from the subnetwork from which they are specialized. To the best of the authors’ knowledge this may be the first example of a topological mechanism that induces spontaneous synchronization and real-world like growth. Thus, network specialization can be used to model the co-evolution of dynamic and topological features found in real-world systems.

Multi-Channel Recovery for Distributed Quality Management of Synchrophasor Data

This paper presents a distributed synchrophasor data management (DSDM) technique for wide-area monitoring systems. The proposed technique focuses on managing voltage/ current phasor data measured from a specific target bus by defining neighbor phasor measurement units (PMUs). An algorithm for searching the neighbor PMUs is developed by interpreting the power network topology as a tree of which a root is the target bus. By exploiting data of neighbor PMUs, a multi-channel structure to recover missing entries is constructed by parallel implementation of LSTM (Long-Short Term Memory) networks. A successive output decision process automatically decides whether to adjust the multi-channel outputs or not. The proposed DSDM can complement the existing centralized management techniques by well-preserving information on local dynamics. Especially, due to the multi-channel structure, DSDM has the capability to flexible and reliable responses to defective data in neighbor PMUs such as an unintended change in a network topology. The effectiveness of DSDM is verified by using the real-world synchrophasor data under different conditions of power systems including local events and unintended changes of network topology.

Self-Organized Disjoint Service Placement in Future Mobile Communication Networks

Future mobile communication networks will offer many ubiquitous services to its clients such as voice and video communication, access to data and files, use of virtual resources in cloud, etc. The provision of these services will have to face the different challenges posed by future wireless networks such as changing network topology, variable load conditions, clients’ distribution, QoS requirements etc. is a very difficult task and requires a high degree of self-organization in network operations. One important problem in this context is the self-organized service placement which refers to the problem of finding optimal nodes in the network that are most suitable for hosting a particular service type. An optimal placement of a service and its instances (replicas) not only minimizes the service costs but also reduces the overall network traffic and improves connectivity between clients and servers. This paper proposes a novel network service called Self-Organized Disjoint Service Placement (SO-DSP) service which manages other network services and their instances in order to achieve overall network optimization while keeping the individual service’s quality at the same level for its clients. The clients of SO-DSP are not the end-users of the network but the offered network services

A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks

The low earth orbit (LEO) constellation network has become a promising approach to provide global communication services, due to its advantages in wide global coverage, low transmission delay, and convenient networking. However, the instability of the intersatellite laser terminal and the high relative speed between adjacent satellites cause frequent network topology changing problems for data routing. In this paper, a disruption tolerant distributed routing algorithm (DTDR) is proposed, where the satellites calculate the alternate path for transmission when the network topology changes, which improves the performance of packet loss. Specifically, each satellite maintains the intersatellite link (ISL) information within a specified number of hops. When an ISL state changes within the specified number of hops, the corresponding satellite calculates and switches to the detour path. Furthermore, the traffic is balanced through the detour process. Various simulations were constructed and show that the proposed algorithm outperforms the existing algorithm in terms of packet loss ratio and transmission delay.

Non-Cooperative Game Forwarding Leveraging User Trustworthiness in Mobile Edge Networks

Given frequent changes of network topology caused by limited computing power, strong mobility and weak reliability of most nodes in mobile edge computing (MEC) networks, a Non-Cooperative Game forwarding strategy based on user Trustworthiness (NCGT) is proposed to deal with low security and efficiency of data transmission. NCGT firstly considers device residual energy ratio, contact probability, service degree and link stability between devices to measure the reliability of nodes. Then, leverages Entropy Weight (EW) method and Golden Section Ratio (GSR) to develop a security optimal neighbors screening model based on multi-attribute decision-making, which ensures that high-performance security nodes are selected as forwarding game objects. Third, NCGT takes forwarding and non-forwarding as the policy set, designs the benefit function, and gets forwarding probability of nodes through Nash equilibrium, to reduce a large number of redundancy, competition and conflict in forwarding requests and improve its broadcasting efficiency. The simulation results show, NCGT is more effective against black hole and witch attacks than S-MODEST and AODV+FDG when there exists malicious nodes. Meanwhile, with the increasing of network load, NCGT with or without GSR always performs best in the terms of data delivery rate, delay, transmission energy consumption and system throughput in MEC environment.

An adaptive message return strategy for same direction multilane in vehicular networks

Due to the roadside unit can not fully achieve seamless coverage and the dynamic change of network topology in vehicular networks, problems such as the interruption of vehicle service, poor stability of communication link, and high network delay will be caused. To solve these problems, this paper proposes a codirectional multilane adaptive message return (CMAMR) strategy based on edge computing. Firstly, we establish a CMAMR communication model. Then, according to the movement trajectory of the receiving node, we propose a single-hop and a multi-hop message return link strategy. The former combines the single-hop link quality and node location information to construct a node selection function to assist the head node in selecting an optimal forwarding node to form a single-hop message return link. The latter assists the RSU to select the optimal multi-hop message return link in the same lane by establishing a link adaptive quality evaluation function, thus improving the user’s quality of server. The simulation results show that the link network delay of the CMAMR strategy is 14.6%, 42.86%, and 38.47% lower than those of the random with forwarding progress (RFP), most forward with fixed radius (MFR), and traditional vehicle-to-infrastructure and vehicle-to-vehicle cooperative communication (TVCC) message return strategies, respectively; correspondingly, the link reliability is at least 9.13% higher for the CMAMR strategy.

Fuzzy-Based Multi-Layered Clustering and ACO-Based Multiple Mobile Sinks Path Planning for Optimal Coverage in WSNs

Wireless Sensor Network with Mobile Collectors (MCs) enable increased flexibility and convenience in data gathering for numerous large-scale applications. However, introducing MCs also brings a new set of challenges to overcome. To reduce the data delivery latency of the application, it is required to select the minimum number of Rendezvous Points (RPs) that allow most sensors to forward data in single-hop and best path must be planned for each MC to provide uniform path length and round-trip time for all MCs. In contrast to existing schemes, we propose a Fuzzy C-Means based multi-layered RP Clustering and ACO-based Route-Planning scheme (FCM-RP) which is a robust method to determine RP-positions and MC-assignments. More particularly, the existing works have given priority to either reducing network energy consumption or minimizing data gathering delay. In our paper, along with these priorities, we consider the robustness and adaptability of the algorithm; meaning that, our algorithm can modify the planned trajectories of deployed MCs in response to sensor node failures. This allows to adapt to any changes in network topology caused by node failures or external factors. Also, to maximize utilization of each RP and provide greater coverage for nodes, the ideal positioning of RPs with minimum coverage overlap with neighboring RPs is considered. Simulation analysis shows that the FCM-RP scheme defines better routes for MCs in terms of total path length and delay, and significantly outperforms the existing algorithms in terms of total energy consumption and network lifetime.

Dopamine transporter silencing in the rat: systems-level alterations in striato-cerebellar and prefrontal-midbrain circuits

Silencing of dopamine transporter (DAT), a main controlling factor of dopaminergic signaling, results in biochemical and behavioral features characteristic for neuropsychiatric diseases with presumed hyperdopaminergia including schizophrenia, attention deficit hyperactivity disorder (ADHD), bipolar disorder, and obsessive-compulsive disorder (OCD). Investigation of DAT silencing thus provides a transdiagnostic approach towards a systems-level understanding of common underlying pathways. Using a high-field multimodal imaging approach and a highly sensitive cryogenic coil, we integrated structural, functional and metabolic investigations in tandem with behavioral assessments on a newly developed preclinical rat model, comparing DAT homozygous knockout (DAT-KO, N = 14), heterozygous knockout (N = 8) and wild-type male rats (N = 14). We identified spatially distributed structural and functional brain alterations encompassing motor, limbic and associative loops that demonstrated strong behavioral relevance and were highly consistent across imaging modalities. DAT-KO rats manifested pronounced volume loss in the dorsal striatum, negatively correlating with cerebellar volume increase. These alterations were associated with hyperlocomotion, repetitive behavior and loss of efficient functional small-world organization. Further, prefrontal and midbrain regions manifested opposite changes in functional connectivity and local network topology. These prefrontal disturbances were corroborated by elevated myo-inositol levels and increased volume. To conclude, our imaging genetics approach provides multimodal evidence for prefrontal-midbrain decoupling and striato-cerebellar neuroplastic compensation as two key features of constitutive DAT blockade, proposing them as transdiagnostic mechanisms of hyperdopaminergia. Thus, our study connects developmental DAT blockade to systems-level brain changes, underlying impaired action inhibition control and resulting in motor hyperactivity and compulsive-like features relevant for ADHD, schizophrenia and OCD.

A Service Function Chain Deployment Scheme of the Software Defined Satellite Network

The aim of this study was to reduce the influence of highly dynamic substrate network topology and large transmission delay caused by long communication distance between satellites and ground in Software Defined Satellite Networks (SDSNs), and a Partial Observation Markov Decision Process (POMDP)-based service function chain (SFC) deployment scheme of SDSN is proposed. Under this SDSN architecture, the network topology changes could be obtained through the SDN centralized control ability. Due to the topology changes, which may cause inevitable observation errors and transmission delays, the complete actual topology and network states cannot be obtained in real time. Thus, we put forward a POMDP model–based SFC deployment scheme, and an approximate iterative algorithm to solve the problem, aiming at optimizing the end-to-end network delay in the SDSN. The simulation results show that our model can optimize the delay of SFC deployment process, and improve the resource utilization and network throughput of the SDSN.

Disequilibrium river networks dissecting the western slope of the Sierra Nevada, California, USA, record significant late Cenozoic tilting and associated surface uplift

Abstract The timing, rates, and spatial patterns of elevation change in the Sierra Nevada, California, USA, have long been the subject of vigorous debate owing to their importance in constraining the tectonic history of western North America and models of orogenesis. Here we present a systematic analysis of multiple measures of fluvial geomorphology along the entire length of the range and interpret these observations using 1-D and 2-D landscape evolution modeling based on the stream power fluvial erosion rule with the rate parameter calibrated from a large data set of millennial-scale erosion rates. We demonstrate that westward-draining rivers in the Sierra Nevada are in a disequilibrium state and that this state is consistent with the transient fluvial response expected from significant down-to-the-west tilting in the last 11 m.y. Assuming rigid-block tilting and using multiple independent measures of tilt magnitude, we find that the magnitude of surface uplift from late Cenozoic tilting appears to have been similar along strike and likely resulted in ~500–1300 m of surface uplift at the crest (0.3–0.8° tilt) from the Yuba through the Stanislaus rivers (~39.2–38.2°N) and 1000–1400 m of surface uplift at the crest (0.6–0.9° tilt) from the Tuolumne River south through the Kings River (~38.2–36.4°N). We show that the transient fluvial response to tilting in the northern Sierra is heavily modulated by heterogeneous lithology and drainage area exchange in a manner that reconciles the high spatial variability in basement incision observed in numerous prior studies. However, we find that heterogeneous lithology alone cannot explain both the transient state and observed patterns in channel steepness, which seem to require late Cenozoic changes in tectonic forcing. Beyond the regional implications of a short-lived rapid pulse of late Cenozoic surface uplift along the entire length of the range, our results demonstrate that a range-wide approach in which river networks are analyzed both in planform and profile can elucidate tectonic histories despite heterogeneous lithology and ongoing changes in network topology.