Real Network Research Articles

Dynamic Evaluation of Road Network Resilience to Traffic Accidents: An Emergency Management Perspective for Sustainable Cities in China

When assessing road network resilience, emergency management behavior should be considered, as this represents the road network’s capacity to adapt to and recover from traffic accidents. Given the timeliness and variability of emergency management behavior, deterministic approaches seem inadequate to represent real road network performance. Thus, this paper innovatively designs an emergency management perspective-based dynamic evaluation method of road network resilience to traffic accidents. Firstly, based on four stages of emergency management, a road network resilience evaluation index system encompassing resilience capabilities, resilience attributes and traffic accident emergency management ability indicators is constructed. Afterwards, the gray relational technique for order preference by similarity to the ideal solution (GRA-TOPSIS) evaluation method based on combination weighting, which integrates factor analysis with hesitant intuitionistic fuzzy expert scoring, is designed to quantify resilience. Finally, the obstacle degree model is utilized for identifying resilience constraints as the input of a long short-term memory (LSTM) model to predict the resilience variation trend. The fast road network of Shanghai in China is adopted as a case study, and the results indicate that road network resilience embodies significant spatial distribution characteristics. Road length, number of tractors, perception and response and disposal time of traffic accidents cast notable effects on resilience. Additionally, some roads are forecast to show descending resilience. The proposed method is valuable for helping policymakers identify current and potential vulnerable roads and to formulate proposals to effectively improve the resilience of urban agglomerations and promote sustainable cities.

DTL-5G: Deep transfer learning-based DDoS attack detection in 5G and beyond networks

Network slicing is considered as a key enabler for 5G and beyond mobile networks for supporting a variety of new services, including enhanced mobile broadband, ultra-reliable and low-latency communication, and massive connectivity, on the same physical infrastructure. However, this technology increases the susceptibility of networks to cyber threats, particularly Distributed Denial-of-Service (DDoS) attacks. These attacks have the potential to cause service quality degradation by overloading network function(s) that are central to network slices to operate seamlessly. This calls for an Intrusion Detection System (IDS) as a shield against a wide array of DDoS attacks. In this regard, one promising solution would be the use of Deep Learning (DL) models for detecting possible DDoS attacks, an approach that has already made its way into the field given its manifest effectiveness. However, one particular challenge with DL models is that they require large volumes of labeled data for efficient training, which are not readily available in operational networks. A possible workaround is to resort to Transfer Learning (TL) approaches that can utilize the knowledge learned from prior training to a target domain with limited labeled data. This paper investigates how Deep Transfer Learning (DTL) based approaches can improve the detection of DDoS attacks in 5G networks by leveraging DL models, such as Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Residual Network (ResNet), and Inception as base models. A comprehensive dataset generated in our 5G network slicing testbed serves as the source dataset for DTL, which includes both benign and different types of DDoS attack traffic. After learning features, patterns, and representations from the source dataset using initial training, we fine-tune base models using a variety of TL processes on a target DDoS attack dataset. The 5G-NIDD dataset, which has a sparse amount of annotated traffic pertaining to several DDoS attack generated in a real 5G network, is chosen as the target dataset. The results show that the proposed DTL models have performance improvements in detecting different types of DDoS attacks in 5G-NIDD dataset compared to the case when no TL is applied. According to the results, the BiLSTM and Inception models being identified as the top-performing models. BiLSTM indicates an improvement of 13.90%, 21.48%, and 12.22% in terms of accuracy, recall, and F1-score, respectively, whereas, Inception demonstrates an enhancement of 10.09% in terms of precision, compared to the models that do not adopt TL.

The impact of message losses and retransmissions on quantum cryptographic protocols

Quantum cryptography promises information theoretic security of several cryptographic primitives. Most of the proposed security proofs work in an ideal communication scenario where all messages are correctly delivered after the first attempt. However, real networks are subject to message losses and require retransmissions to cope with them. While the latter is hardly ever a problem in classical cryptography, with quantum communication copy-and-retransmit could be impossible due to the famous no-cloning theorem. In this work, we analyze some quantum cryptoschemes such as public-key encryption, authentication and quantum money, assuming that quantum messages may be lost as they travel through the communication medium. Although all these schemes are theoretically secure, we show that this degree of realism renders some protocols insecure or impractical, while others are completely unaffected. When possible, we provide mitigations such as teleportation or protocol modifications to circumvent these challenges.

A novel differentiated coverage-based lifetime metric for wireless sensor networks

This paper delves into optimizing network lifetime (NL) subject to connected-coverage requirement, a pivotal issue for realistic wireless sensor network (WSN) design. A key challenge in designing WSNs consisting of energy-limited sensors is maximizing NL, the time a network remains functional by providing the desired service quality. To this end, we introduce a novel NL metric addressing target-specific coverage requirements that remedies the shortcomings imposed by conventional definitions like first node die (FND) and last node die (LND). In this context, while we want targets to be sensed by multiple sensors for a portion of the network lifetime, we let the periods, during which cells are monitored by at least one sensor, vary. We also allow the ratios of multiple and single tracking times to differ depending on the target and incorporate target-based prioritization in coverage. Moreover, we address role assignment to sensors and propose a selective target-sensor assignment strategy. As such, we aim to reduce redundant data transmissions and hence overall energy consumption in WSNs. We first propose a unique 0-1 mixed integer programming (MIP) model, to analyze the impact of our proposal on optimal WSN performance, precisely. Next, we present comprehensive comparative studies of WSN performance for alternative NL metrics regarding different coverage requirements and priorities across a wide range of parameters. Our test results reveal that by utilizing our novel NL metric total coverage time can be improved significantly, while facilitating more reliable sensing of the target region.

Influence-based techniques to foster cooperation in real-life social networks using the prisoner’s dilemma game

Influence-based techniques to foster cooperation in real-life social networks using the prisoner’s dilemma game

Exploring Scalability of BFT Blockchain Protocols through Network Simulations

Novel Byzantine fault-tolerant (BFT) state machine replication protocols improve scalability for their practical use in distributed ledger technology, where hundreds of replicas must reach consensus. Evaluating the performance of BFT protocol implementations requires careful evaluation. We propose a new methodology using scalable network simulations to predict BFT protocol performance. Our simulation architecture allows for the integration of existing BFT implementations without modification or re-implementation, offering a cost-effective alternative to large-scale cloud experiments. We validate our method by comparing simulation results with real-world cloud deployments, showing that simulations can accurately predict performance at larger scales when network limitations dominate. In our study, we applied this methodology to assess the performance of several “blockchain-generation” BFT protocols, including HotStuff, Kauri, Narwhal & Tusk, and Bullshark, under realistic network conditions (with constrained 25 Mbit/s bandwidth) and induced faults. Kauri emerges as the top performer, achieving 6742 operations per second (op/s) with 128 replicas, outperforming BullShark (2318 op/s) and Tusk (1952 op/s). HotStuff, using secp256k1 and BLS signatures, reaches 494 op/s and 707 op/s, respectively, demonstrating the efficiency of BLS-signature aggregation for saving bandwidth. This study demonstrates that state-of-the-art asynchronous BFT protocols can achieve competitive throughput in large-scale, real-world scenarios.

Decentralized System Synchronization among Collaborative Robots via 5G Technology.

In this article, we propose a distributed synchronization solution to achieve decentralized coordination in a system of collaborative robots. This is done by leveraging cloud-based computing and 5G technology to exchange causal ordering messages between the robots, eliminating the need for centralized control entities or programmable logic controllers in the system. The proposed solution is described, mathematically formulated, implemented in software, and validated over realistic network conditions. Further, the performance of the decentralized solution via 5G technology is compared to that achieved with traditional coordinated/uncoordinated cabled control systems. The results indicate that the proposed decentralized solution leveraging cloud-based 5G wireless is scalable to systems of up to 10 collaborative robots with comparable efficiency to that from standard cabled systems. The proposed solution has direct application in the control of producer-consumer and automated assembly line robotic applications.

Unsupervised Social Bot Detection via Structural Information Theory

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, for example, Graph Neural Network, Transformer, and so on, which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective, and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: posting type distribution , posting influence , and follow-to-follower ratio . Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Third, a new community labeling method is presented to distinguish social bot communities by computing the user’s stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with 10 representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on 4 real social network datasets.

Dynamic Contact Networks in Confined Spaces: Synthesizing Micro-Level Encounter Patterns through Human Mobility Models from Real-World Data.

This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal-dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. Typically, large-scale epidemiological simulations overlook the specifics of human mobility within confined spaces or rely on overly simplistic models. By focusing on the distinct aspects of infection propagation within specific locations, our approach strengthens the realism of such pandemic simulations. The resulting models shed light on the role of spatial encounters in disease spread and improve the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers a new perspective on temporal network generation for epidemiological modeling.

Percolation Transitions in Edge-Coupled Interdependent Networks with Reinforced Inter-Layer Links.

Prior research on cascading failures within interdependent networks has predominantly emphasized the coupling of nodes. Nevertheless, in practical networks, interactions often exist not just through the nodes themselves but also via the connections (edges) linking them, a configuration referred to as edge-coupled interdependent networks. Past research has shown that introducing a certain percentage of reinforced nodes or connecting edges can prevent catastrophic network collapses. However, the effect of reinforced inter-layer links in edge-coupled interdependent networks has yet to be addressed. Here, we develop a theoretical framework for studying percolation models in edge-coupled interdependent networks by introducing a proportion of reinforced inter-layer links and deriving detailed expressions for the giant and finite components and the percolation phase transition threshold. We find that there exists a required minimum proportion of the reinforced inter-layer links to prevent abrupt network collapse, which serves as a boundary to distinguish different phase transition types of a network. We provide both analytical and numerical solutions for random and scale-free networks, demonstrating that the proposed method exhibits superior reinforcement efficiency compared to intra-layer link reinforcement strategies. Theoretical analysis, simulation results, and real network systems validate our model and indicate that introducing a specific proportion of reinforced inter-layer links can prevent abrupt system failure and enhance network robustness in edge-coupled interdependent networks.

A unified framework of semi-supervised community detection integrating network topology and node content

Detecting the community structure within networks is important because it aids in the analysis of complex networks. However, the existence of diverse complex structures in the network topology can limit the topological information’s ability to represent communities. For instance, the accuracy of many traditional methods based on topology alone decreases sharply when a link is missing or noisy. To solve the above-mentioned issues simultaneously, we propose a unified framework of semi-supervised community detection integrating content information. Three models are proposed, instead of a special model-handling problem. The framework used performs the same interpretation for a target problem. The inputs contain the adjacent and node content matrix. The proposed models describe the network topology using a non-negative matrix factorization (NMF) algorithm–based generative model or modularity maximization algorithm. Additionally, we use the equivalence theory of the pLSI, and NMF models the content information based on NMF. Community membership is also used as the relationship between communities and node contents. The models uniformly integrate content information and network topology. Further, a constraint matrix that depicts must-links is built by calculating the similarity of neighbors between any two nodes. Must-link pairs can represent the network topology’s prior information. The models’ community memberships are adjusted by using a strong constraint based on the connected component of this matrix. The models also introduce prior information into the topological and content information in the mentioned models. We compared the proposed models with the state-of-the-art community detection models on artificial networks and five real networks. The experimental results show that the proposed models outperformed the models combining topology and content and semi-supervised models. Fusing the topology with the content and the prior information in the models was also effective for community detection. Thus, the models under the framework are more advantageous than other semi-supervised models when incorporating the same amount of prior information into the models. The semi-supervised framework used also has a certain level of competitiveness in detecting communities.

DarkMor: A framework for darknet traffic detection that integrates local and spatial features

The dark web, as an integral part of the multi-layered structure of the internet, provides anonymity and high levels of concealment absent on the surface web. Users can engage in various online activities without leaving any trace, making the dark web a hub for illicit activities, such as drug and weapon trafficking. Consequently, this poses a significant threat to social order and network security. However, due to the high concealment of the dark web, traditional detection methods suffer with insufficient extraction of characteristic information from darknet traffic and inadequate consideration of feature correlations. As a result, the accuracy of these conventional detection methods in detecting the dark web in real network environments is suboptimal. This paper proposes a darknet traffic detection framework called DarkMor to address these challenges. It integrates local and spatial features, automates feature mining and fusion, and models spatial relationship between features to fully exploit their potential. The core components of DarkMor consist of the feature fusion module and the traffic perception module. Using an improved feature tokenizer transformer architecture, the feature fusion module enhances the extraction of local features within high-dimensional feature clusters, effectively combining local feature information with global context. Additionally, the traffic perception module leverages a temporal model that incorporates self-attention mechanisms to learn the spatiotemporal characteristics of fused features, thereby further enhancing the model’s detection. Experimental results demonstrated that DarkMor achieved an accuracy of 97.78% on real network datasets, surpassing the latest cross-modal darknet traffic detection models. Furthermore, DarkMor maintained an accuracy of 97.57% even in network environments with reduced training samples, confirming the feasibility and robustness of the proposed detection framework.

Measuring Entanglement in Physical Networks.

The links of a physical network cannot cross, which often forces the network layout into nonoptimal entangled states. Here we define a network fabric as a two-dimensional projection of a network and propose the average crossing number as a measure of network entanglement. We analytically derive the dependence of the average crossing number on network density, average link length, degree heterogeneity, and community structure and show that the predictions accurately estimate the entanglement of both network models and of real physical networks.

A multi-objective Roadside Units deployment strategy based on reliable coverage analysis in Internet of Vehicles

The deployment of Roadside Units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement.

CLBA: A Coulomb’s law based algorithm for community detection in directed networks

Many networks in the real world are directed networks, e.g., email networks, citation networks, etc. Community structure also exists in directed networks, and community detection can help us analyze the structure and function of the network. Currently, the number of community detection algorithms proposed for directed networks is small. The common method for detecting communities in directed networks is transforming the directed network into the undirected network. However, this method ignores the information carried by directed edges, which makes the quality of detected communities poor. In this paper, we propose a community detection algorithm for directed networks, which is based on Coulomb’s law in physics, named CLBA. Moreover, we propose a new method to measure the node importance in directed networks that considers the situation of different networks in which the out-degree and in-degree of a node have different effects to the node importance. We abstract the nodes in the directed network as sets of charged particles and measure the charge of a node by its importance. Based on Coulomb’s law, we can obtain the attraction between nodes and assign the attraction as the weight to the edges, which serves as the basis for the label propagation in directed networks. We also prove that our model is consistent with the point proposed by Kim et al. in 2010 for directed networks. The CLBA algorithm not only has low time complexity, but also, experimental results on benchmark and real networks show that our algorithm can detect communities effectively and efficiently.

Output Stabilizing Control of Complex Biological Networks Based on Boolean Algebra Analysis.

Output stabilizing control of biological systems is of utmost importance in systems biology since key phenotypes of biological networks are often encoded by a small subset of their phenotypic marker nodes. This study addresses the challenge of output stabilizing control for complex biological systems modeled by Boolean networks (BNs). The objective is to identify a set of constant control inputs capable of driving the BN toward a desirable long-term behavior with respect to specified output nodes. Leveraging the algebraic properties of Boolean logic, we develop a novel control algorithm that reformulates the output stabilizing control problem into a simple graph theoretic problem involving auxiliary BNs, the scale of which significantly decreases compared to the original BN. The proposed method ensures superiority over previous results in terms of both the number of control inputs and computational loads, since it searches for the solution within the reduced BNs while retaining essential structures needed for output stabilization. The efficacy of the proposed control scheme is demonstrated through extensive numerical experiments with complex random BNs and real biological networks. To support the reproducible research initiative, detailed results of numerical experiments are provided in the supplementary material, and all the implementation codes are made accessible at https://github.com/choonlog/OutputStabilization.

Scalable determination of multipartite entanglement in quantum networks

Quantum networks comprised of entangled end nodes serve stronger than the classical correlation for unparalleled quantum internet applications. However, practical quantum networking is affected by noise, which at its worst, causes end nodes to be described by pre-existing classical data. In such untrusted networks, determining quantum network fidelity and genuine multi-node entanglement becomes crucial. Here, we show that determining quantum network fidelity and genuine N-node entanglement in an untrusted star network requires only N + 1 measurement settings. This method establishes a semi-trusted framework, allowing some nodes to relax their assumptions. Our network determination method is enabled by detecting genuine N-node Einstein-Podolsky-Rosen steerability. Experimentally, using spontaneous parametric down-conversion entanglement sources, we demonstrate the determinations of genuine 3-photon and 4-photon quantum networks and the false positives of the widely used entanglement witness, the fidelity criterion of 1/2. Our results provide a scalable method for the determination of multipartite entanglement in realistic quantum networks.

Percolation of conditional dependency clusters based on edge-coupled interdependent networks

Considering the existence of multiple edge dependencies in realistic interdependent networks, we propose a model of edge-coupled interdependent networks with conditional dependency clusters (EINCDCs). In this model, the edges in network A depend on the edges in dependency clusters of size m in network B. If the failure rate of edges within the dependency clusters in network B exceeds the failure tolerance α, the corresponding edges in network A that depend on those clusters in network B will fail accordingly. By adopting the self-consistent probabilities approach, a theoretical analytical framework is established to quantitatively address this model. Specifically, we study the robustness of the system verified with numerical simulations in the effect of the cluster size and failure tolerance under random attacks on systems composed of two networks A and B constructed with Random Regular (RR), Erdös-Rényi (ER) and Scale Free (SF) models. Our results show that both networks A and B undergo a first-order or hybrid phase transition when the dependency cluster size does not exceed 2. However, when the cluster size of dependency clusters exceeds 2, a non-monotonic behavior is observed. In particular, when the failure tolerance is in the range from 0 to 0.5, the robustness of the system weakens with the growing in the number of dependency clusters of size 2. While, this tendency reverses when the failure tolerance is in the range from 0.5 to 1. Moreover, we observe that due to the asymmetric interdependency between the two networks, network B always undergoes first-order phase transition, whereas network A could exhibit different types of phase transitions, which depends on the size of dependency clusters. In addition, the failure tolerance may have opposite effects on the two networks with the growing of dependency cluster sizes. The conclusions of the study may provide useful implications and enrich the understanding in the robustness of edge-coupled interdependent networks.

Perimeter Control-Based Coordinated Ramp Metering with Linked Queue Management for Freeway Networks

This paper proposes a macroscopic fundamental diagram (MFD)-based perimeter control approach for coordinated ramp metering, aiming to minimize the total time spent (TTS) of a freeway network (stretch) with multiple bottlenecks. To address problems incurred from limited queue storage space and minimum green time at on-ramps, the proposed method extends previous approaches in two aspects. Firstly, a coordinated ramp control approach is proposed to ensure that the total metered on-ramp flow remains below the value determined by the MFD-based perimeter regulator, thereby preventing network over-saturation. Secondly, a novel queue management approach is developed to prevent on-ramp queue saturation and its associated side effects, for example, spillback or insufficient control actions. This approach determines the quasi-optimal flow of the next upstream (slave) on-ramp, based on the predicted queue of each (master) on-ramp suffering from excessive queues. The proposed perimeter controller was tested using SUMO, a microscopic simulator, on a realistic freeway network, and compared with other existing strategies, including HERO and perimeter controllers with other metered on-ramp flow distribution approaches. The simulation results suggest that the proposed strategy can ( i) effectively mitigate congestion and maintain capacity within the freeway network, resulting in reduced overall TTS, and ( ii) appropriately delay or prevent queue saturation at on-ramps.

Assessing vehicle interdiction strategies on a complex transportation network: A simulation-based study

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker’s escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender’s waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By employing this approach and conducting in-depth analyses, the aim is to provide valuable insights into the efficiency and practicality of the developed methods on real-world transportation networks.