Network Simulator Research Articles

Reservoir computing and advanced synaptic plasticity of sputter-deposited ZnO memristors with controllable threshold and nonvolatile switching behavior

This study presents an ITO/ZnO/ITO/Si memristor fabricated via reactive sputtering for use in advanced analog synaptic plasticity and reservoir computing (RC) systems. The proposed device exhibited stable threshold and nonvolatile switching characteristics by effectively controlling the current compliance (ICC) limit. Multilevel data storage was achieved through controlled multistate switching via reset-stop voltage and ICC. X-ray diffraction analysis confirmed the formation of a polycrystalline ZnO film with a 12:8 oxygen-to-argon ratio, which facilitated the generation of oxygen-vacancy conductive filaments. The memristor effectively replicated key synaptic characteristics such as long-term potentiation, long-term depression, spike-amplitude/width-dependent plasticity, spike-rate-dependent plasticity, and the transition from short-term to long-term memory. The RC system processed binary 4-bit codes and recognized different digits, achieving 98.84% accuracy in handwritten digit recognition using a convolutional neural network simulation, highlighting its potential for efficient image processing applications.

Analyzing the parallelization and structural impact of machine learning algorithms in social networks: a simulation-based approach

Analyzing social media networks is crucial for understanding and uncovering common interests and characteristics among users within human societies. In this context, we simulated a simple application of human interaction in social networks, which involves users following others based on text similarity. We then investigated the effects of various machine learning (ML) algorithms employed in the applications to be used as recommendations to decision-making users. A novel agent-based social network simulator called distributed system and multinode processing is developed to assess the parallelization of the ML algorithms (i.e., K-means clustering, cosine similarity, support vector machine, multilayer perceptron) using bag of words (BoW) term frequency-inverse document frequency vectorization by evaluating their performance when executed in parallel across distributed heterogeneous resources. In addition, this simulator compares the effects of BoW with the Doc2Vec model on network structure by observing the differences in detected communities and resulting network graphs when a selected user follows the recommendations produced by an employed algorithm. Three real datasets were used in the experiments: Twitter, Scientific Research Papers, and Retail. This work’s contribution is a unique in-house agent-based simulator developed to analyze the impact of common ML algorithms, including supervised and unsupervised learning, on social networks.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brain regions. Recently, a new class of neural mass model has been developed from an exact mean field reduction of a network of spiking cortical cell models with a biophysically realistic model of the chemical synapse. Moreover, this new population dynamics model can naturally incorporate electrical synapses. Here we demonstrate the ability of this new modelling framework, when combined with data from the Human Connectome Project, to generate patterns of functional connectivity (FC) of the type observed in both magnetoencephalography and functional magnetic resonance neuroimaging. Some limited explanatory power is obtained via an eigenmode description of frequency-specific FC patterns, obtained via a linear stability analysis of the network steady state in the neigbourhood of a Hopf bifurcation. However, direct numerical simulations show that empirical data is more faithfully recapitulated in the nonlinear regime, and exposes a key role of gap junction coupling strength in generating empirically-observed neural activity, and associated FC patterns and their evolution. Thereby, we emphasise the importance of maintaining known links with biological reality when developing multi-scale models of brain dynamics. As a tool for the study of dynamic whole brain models of the type presented here we further provide a suite of C++ codes for the efficient, and user friendly, simulation of neural mass networks with multiple delayed interactions.

Software Defined Network Based Next Generation Mobile Communication Network Architecture

As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The nextgeneration mobile network (NMCN-SDN) architecture proposed in this study is based on Software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-toend latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions.

A Mobility Handover Decision Method Based on Multi-Topology

With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain service continuity. Traditional mobility support methods typically rely on a single switching path for all mobile data flows. However, if this path cannot meet the requirements of all the flows, it may lead to network congestion or a decline in user experience. To overcome this limitation, this paper proposes a mobility handover decision method based on multi-topology. It enables the dynamic allocation of mobile data flows across different switching paths within multiple logical topologies. The method models a multi-topology selection problem aimed at minimizing average packet transmission delay and packet loss rate, while considering network conditions and the Quality of Service (QoS) requirements for each flow. By solving the dual problem of the original optimization, a near-optimal solution is achieved. The proposed scheme and algorithm were implemented and tested using the Mininet network simulator. Results show that the proposed approach achieves low average packet transmission delay, low average packet loss rate, and high throughput, compared to traditional single-path switching methods and existing multipath routing methods.

A Dijkstra Based Algorithm for Optimal Splitter Location in Passive Optical Local Area Network (POLAN)

Passive Optical Local Area Networks (POLANs) are integral to modern broadband communication systems, offering high bandwidth and immunity to electromagnetic interference. Designing an efficient POLAN requires careful consideration of splitter placement to minimize network costs. This paper presents an algorithmic approach using Dijkstra's algorithm and the Google Maps API to optimize splitter locations in a POLAN. By treating Optical Network Terminals (ONTs) as nodes in a graph and calculating walking distances between them, the algorithm identifies potential splitter locations that minimize fiber length. Using the Dijkstra's algorithm, the total fiber length used to connect every optical network unit is approximately 274km. Finally, a simulation of the full PON network was carried out and the BER and Q-Factor for each ONU was gotten. An average BER value of 1.8e-11 and Q-Factor value of 13.3 was gotten.

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

Hybrid Mutual Authentication for Vehicle-to-Infrastructure Communication Without the Coverage of Roadside Units

The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.

Distributed Heterogeneous Spiking Neural Network Simulator Using Sunway Accelerators

Distributed Heterogeneous Spiking Neural Network Simulator Using Sunway Accelerators

A P2P Approach to Routing in Hierarchical MANETs

Abstract: We present an effective routing solution for the backbone of hierarchical MANETs. Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) common to many (structured) P2P overlays. The DHT provides routing information in a decentralized fashion, while supporting different forms of node and network mobility. We split a flat network into clusters, each having a gateway who participates in a DHT overlay. These gateways interconnect the clusters in a backbone network. Two routing approaches for the backbone are explored: flooding and a new solution exploiting the storage and retrieval capabilities of a P2P overlay based on a DHT. We implement both approaches in a network simulator and thoroughly evaluate the performance of the proposed scheme using a range of static and mobile scenarios. We also compare our solution against flooding. The simulation results show that our solution, even in the presence of mobility, achieved well above 90% success rates and maintained very low and constant round trip times, unlike the flooding approach. In fact, the performance of the proposed inter-cluster routing solution, in many cases, is comparable to the performance of the intra-cluster routing case. The advantage of our proposed approach compared to flooding increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach.

Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks

A possible solution to address the enormous increase in traffic demands faced by network operators is to rely on multi-fiber optical backbone networks. These networks use multiple optical fibers between adjacent nodes, and, when properly designed, they are capable of handling petabits of data per second (Pbit/s). In this paper, an artificial neural network (ANN) model is investigated to estimate both the capacity and cost of a multi-fiber optical network. Furthermore, a fiber assignment algorithm is also proposed to complement the network design, enabling the generation of datasets for training and testing of the developed ANN model. The model consists of three layers, including one hidden layer with 50 hidden units. The results show that for a large network, such as one with 100 nodes, the model can estimate performance metrics with an average relative error of less than 0.4% for capacity and 4% for cost, while achieving a computation time nearly 800 times faster than the heuristic approach used in network simulation. Additionally, the network capacity is around 5 Pbit/s.

A resilient routing strategy based on deep reinforcement learning for urban emergency communication networks

In the context of urban informatization, meeting the stringent requirements of emergency communication presents a significant challenge for Urban Emergency Communication Networks (UECNs). Mobile ad hoc networks deployed in these environments often experience node degradation and link disruptions due to the complex urban landscape, leading to frequent communication failures. This paper introduces a novel resilient routing strategy, termed Deep Reinforcement Learning-based Resilient Routing (DRLRR). The proposed routing strategy first utilizes node and link state information to accurately characterize dynamic changes in network topology. The routing decision-making process is then formalized as a Markov decision process, integrating multiple performance metrics into a reward function tailored for the specific demands of urban emergency communications. By leveraging deep reinforcement learning, DRLRR effectively adapts to the complexities of urban environment, enabling intelligent and optimal route selection during network topology fluctuations to ensure seamless data transmission during emergencies. Comparative simulations conducted using NS3(Network simulator 3) demonstrate that DRLRR significantly outperforms three other routing protocols, achieving notable improvements in packet delivery rate, average end-to-end delay, and throughput, thus fulfilling the requirements for reliable and consistent communication in urban emergency scenarios.

A workflow for the hybrid modelling and simulation of multi-timescale biological systems

With the steady advance of in-silico biological experimentation, model construction and simulation becomes a ubiquitous tool to understand and predict the behaviour of many biological systems. However, biological processes may contain components from different types of reaction networks, resulting in models with different (e.g., slow and fast) timescales. Hybrid simulation is one approach which can be employed to efficiently execute multi-timescale models. In this paper, we present a methodology and workflow utilizing (coloured) hybrid Petri nets to construct smaller and more complicated hybrid models. The presented workflow integrates algorithms and ideas from hybrid simulation of biochemical reaction networks as well as Petri nets. We also construct multi-timescale hybrid models and then show how these models can be efficiently executed using three different advanced hybrid simulation algorithms.

Unveiling the role of phytochemicals in autism spectrum disorder by employing network pharmacology and molecular dynamics simulation.

Autism Spectrum Disorder (ASD) comprises a myriad of disorders with vast pathologies, aetiologies, and involvement of genetic and environmental risk factors. Given the polygenic aspect of ASD, targeting several genes/proteins responsible for pathogenesis at once might prove advantageous in its remediation. Various phytochemicals have been proven to possess neuroprotective, anti-inflammatory, and antioxidant properties by alleviating symptoms and targeting a complex network of genes/proteins related to disease pathology. However, the effects of many of these phytochemicals on ASD are enigmatic, and their molecular targets and molecular mechanisms are still elusive. Here, we provide a comprehensive comparative study on the therapeutic potential of 6 phytochemicals viz. Cannabidiol, Crocetin, Epigallocatechin-3-gallate, Fisetin, Quercetin, and Resveratrol based on their neuroprotective properties in managing ASD. We aimed to identify and target a network of core proteins in the pathology of ASD via phytochemicals using network pharmacology, molecular docking, and simulation studies. The methodology includes screening genes/proteins implicated in ASD as targets of each phytochemical, followed by network construction using Protein-Protein Interactions, Gene Ontology, and enrichment analysis. The constructed network was further narrowed down to the hub genes in the network, followed by their spatio-temporal analysis, molecular docking, and molecular dynamics simulation. 6 core genes were obtained for ASD, 3 of which are directly involved in disease pathogenesis. The study provides a set of novel genes that phytochemicals can target to ameliorate and regulate ASD pathogenesis. Cannabidiol can inhibit ABCG2, MAOB, and PDE4B, Resveratrol can target ABCB1, and Quercetin can regulate AKR1C4 and XDH. This study demonstrated the potential of phytochemicals to target and regulate ABCG2, ABCB1, AKR1C4, MAOB, PDE4B, and XDH, which in turn modulate the dysfunctional network present in the ASD pathology and provide therapeutic potential in the management of ASD.

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.

Logic-based modeling of biological networks with Netflux.

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux (https://github.com/saucermanlab/Netflux). Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs

Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tensor networks. As the number of qubits and quantum gates grows larger in current quantum devices, traditional state-vector-based quantum circuit simulation methods prove inadequate due to the overwhelming size of the Hilbert space and extensive entanglement. Consequently, brutal force tensor network simulation algorithms become the only viable solution in such scenarios. The two main challenges faced in tensor network simulation algorithms are optimal contraction path finding and efficient execution on modern computing devices, with the latter determining the actual efficiency. In this study, we investigate the optimization of such tensor network simulations on modern GPUs and propose general optimization strategies from two aspects: computational efficiency and accuracy. First, we propose to transform critical Einstein summation operations into GEMM operations, leveraging the specific features of tensor network simulations to amplify the efficiency of GPUs. Second, by analyzing the data characteristics of quantum circuits, we employ extended precision to ensure the accuracy of simulation results and mixed precision to fully exploit the potential of GPUs, resulting in faster and more precise simulations. Our numerical experiments demonstrate that our approach can achieve a 3.96× reduction in verification time for random quantum circuit samples in the 18-cycle case of Sycamore, with sustained performance exceeding 21 TFLOPS on one A100. This method can be easily extended to the 20-cycle case, maintaining the same performance, accelerating by 12.5× compared to the state-of-the-art CPU-based results and 4.48–6.78× compared to the state-of-the-art GPU-based results reported in the literature. Furthermore, the strategies presented in this article hold general applicability for accelerating problems that can be tackled by contracting tensor networks such as graphical models and combinatorial optimizations.

Abstraction-based segmental simulation of reaction networks using adaptive memoization

Background Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model’s dynamics may take a prohibitively long time.Results We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system’s dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently.Conclusion We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.

Dynamic spatial network simulation accounting for multiple ecological factors provides practical recommendations for biosecurity early detection and rapid response (EDRR) strategies.

Globally the spread of invasive pests is being facilitated by increased human mobility and climate change. Simulation modelling can help assess biosecurity strategies for early detection and rapid response (EDRR), but has struggled to account for important factors in the invasion process, such as spatial and temporal variability in habitat suitability and connectivity; population dynamics; and multiple dispersal pathways. We developed a novel dynamic spatial network simulation approach based on spatial network theory that enables integration of a wider range of spatio-temporal factors than previous studies, calibrated it against extensive historical trapping data, and applied it to comprehensively analyse the EDRR strategy for Oriental fruit fly (Bactrocera dorsalis; OFF) in northern Australia. Simulations indicated that the chance of OFF reaching the mainland in the next 20 years could be up to 20% under the current EDRR strategy, depending on how optimistic or pessimistic model assumptions are, and highlighted possible improvements to the EDRR strategy for further consideration. Simulations under optimistic assumptions indicate that transport via wind is most important in OFF reaching the mainland, but under pessimistic assumptions transport via people carrying infected fruit becomes more important. Our new dynamic spatial network simulation approach can account for a wide range of spatio-temporal ecological factors to provide practical real-world recommendations. At a minimum, this approach only requires weather and population data, both of which are available globally from a variety of free and open sources, making it broadly applicable to assessing the EDRR strategies in place for different species in other locations. © 2024 Society of Chemical Industry.

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.