Real-time Network Simulation Research Articles

EWFAIGF: Design of an Efficient Model for Enhancing Energy Efficiency in IoT-Based Wireless Sensor Networks through Fuzzy AHP and Iterative Grey Wolf Jelly Fish Optimization

In the realm of Wireless Sensor Networks (WSNs), the quest for enhanced energy efficiency remains paramount, given their pivotal role in facilitating the Internet of Things (IoT) applications. Existing methodologies often grapple with the dual challenge of optimizing energy consumption while ensuring robust data transmission, thereby limiting their efficacy in dynamic, resource-constrained environments. This work introduces a novel paradigm that meticulously addresses these constraints, thereby heralding a significant leap towards optimizing WSNs' operational efficiency. At the core of our proposed model lies the integration of Fuzzy Analytic Hierarchy Process (Fuzzy AHP)-based clustering, which ingeniously segregates nodes by leveraging multi-criteria such as location, energy efficiency, and temporal performance. This clustering serves as a precursor to the application of an innovative Iterative Grey Wolf Jelly Fish Optimizer (GWJFO). The GWJFO stands out by its strategic prowess in delineating optimal routing paths, thus minimizing energy expenditure and enhancing data relay efficiency. Furthermore, our model is fortified with a Q Learning Method, ingeniously designed to identify and execute optimal alternate routing paths through a Make Before Break strategy. This addition not only mitigates potential faults but also significantly boosts computational efficiency and packet delivery performance. Empirical validation through real-time network simulations underscored the model's superiority, demonstrating a 9.5% improvement in communication speed, an 8.5% increase in energy efficiency, a 4.5% improvement in packet delivery performance under fault conditions, a 10.4% rise in throughput, and a 5.9% enhancement in network consistency over existing benchmarks. This groundbreaking work not only paves the way for more energy-efficient WSNs but also sets a new standard in achieving high-performance metrics essential for the next generation of IoT applications. The implications of these advancements extend beyond mere technical enhancements, offering a beacon for future research in the domain, potentially revolutionizing the way we deploy and manage sensor networks in an array of applications.

LEOCN: Real-time and complete network simulation framework for LEO constellation networks1

With the development of the space industry, LEO simulation platforms are increasingly important. Prior work mainly focused on the dynamics of constellation topology, routing algorithms against the topological motion, and the behavior of LEO constellation networks, but neglected real-time and completeness properties. In our work, we design a real-time and complete network simulation framework, named LEOCN, that is more meaningful and detailed for researchers than previous work on LEO satellite constellation simulations. LEOCN is supported by Linux System, which makes the framework more realistic and relieves the computational burden. Meanwhile, LEOCN deeply considers topology and routing algorithms, visualization, satellite mobility, communication protocols, and network traffic problems. On LEOCN, we can deploy routing algorithm experiments via SDN. We evaluate the performance of LEOCN in large-scale constellation scenarios, and the results demonstrate the superiority of the real-time and complete network simulation framework.

An EVs charging guiding strategy for the coupling system of road network and distribution network based on the PT3

• EVs charging guiding strategy. • The third-generation prospect theory. • Distribution network, road network, fast charging station. • Travel time, charging load. As an important load, electric vehicles (EVs) have great randomness. The integration of a large number of electric vehicles will have a great impact on the stable operation of the distribution network (DN) and road network. Therefore, in this paper a charging guiding strategy based on the third-generation prospect theory (PT3) is proposed combined with the fast charging station (FCS) integrated with photovoltaic-energy storage- charging. Based on the dynamic reference point, a real-time coupling relationship model is proposed. And the full state prospect description model of coupling system is proposed and described by the prospect value. Based on the proposed model, the aggregate of the prospect value is used to describe the full state of the coupling system. Therefore, the proposed strategy can analyze the uncoordinated issues by the full state prospect description model, and balance the coupling system by guiding EVs. Finally, an algorithm based on multi time scale is used to achieve real-time traffic-distribution network simulation. It is verified that the proposed method can not only fully coordinate the distribution of charging load, but also reasonably relieve the traffic pressure.

Deception in Network Defences Using Unpredictability

In this article, we propose a novel method that aims to improve upon existing moving-target defences by making them unpredictably reactive using probabilistic decision-making. We postulate that unpredictability can improve network defences in two key capacities: (1) by re-configuring the network in direct response to detected threats, tailored to the current threat and a security posture, and (2) by deceiving adversaries using pseudo-random decision-making (selected from a set of acceptable set of responses), potentially leading to adversary delay and failure. Decisions are performed automatically, based on reported events (e.g., Intrusion Detection System (IDS) alerts), security posture, mission processes, and states of assets. Using this codified form of situational awareness, our system can respond differently to threats each time attacker activity is observed, acting as a barrier to further attacker activities. We demonstrate feasibility with both anomaly- and misuse-based detection alerts, for a historical dataset (playback), and a real-time network simulation where asset-to-mission mappings are known. Our findings suggest that unpredictability yields promise as a new approach to deception in laboratory settings. Further research will be necessary to explore unpredictability in production environments.

Active Load Management of Hydrogen Refuelling Stations for Increasing the Grid Integration of Renewable Generation

Hydrogen Fuel Cell Electric Vehicles (FCEV) can help reduce carbon emissions, air pollution and dependency on fossil fuels in the transport sector. Clean hydrogen fuel can be generated by a power-to-gas process at refuelling stations equipped with water electrolysers, especially in renewable rich areas. Coupled with onsite hydrogen tanks, the fast response capability of electrolysis, could potentially turn the station demand into a flexible electricity load since the hydrogen can be stored and used when needed. This paper presents a novel real-time load management scheme that actively operates a hydrogen refuelling station to relieve thermal network constraints, handles the fluctuations from renewables, and releases network headroom for connecting renewable generation. The key components involved in the refuelling station and their operational characteristics are explicitly modelled in the analysis. The economic impact of the different operational strategies is also examined. In the case study, the effectiveness of the proposed control strategy to avoid overloading and save curtailment in the local distribution network is verified by running the real-time network simulation at 1 minute steps over a 1 hour window and 5 day window respectively. Moreover, a whole year simulation of the station operation shows that the proposed active control strategy enables wind farms in the local network to avoid 9.5 times more curtailment than under passive control strategy. The station's net cost of electricity consumption thus can be reduced by 7.5%., by making use of excess electricity that would otherwise be curtailed. A further 5% reduction on the cost would be possible if the incentive rewards for offering network constraint management services are in place.

Application of truetime for wireless process control

The fast development of the wireless technologies has opened new potential for wireless networked process control system. With wireless network, the scalability of the systems will be extended. In order to analyse and evaluate the efficiency of a wireless networked process control system, a real-time network simulator is required. In this paper, we introduce True Time, an open source Simulink package for simulation of networked control systems. By using True Time, we present an analysis and evaluation of the effects of communication network to the performance of a wireless process control system. Wireless environment is simulated using True Time toolbox. Other parts of the system such as actuator, sensor, controller are real devices.

Co-simulation of transfer alignment and CAN busbased on CANoe-MATLAB

The research on rapid transfer alignment for INS on moving base is very important in the field of the inertia technology. In order to build the real-time CAN bus network simulation system with the velocity matching model of the transfer alignmentalgorithm, CANoe(CAN open environment)-MATLAB interface is applied. The CANoe-MATLAB interface is connected with MATLAB/Simulink which has powerful modeling functions and CANoe which has perfect function of bus simulation. Modeling the transfer alignmentalgorithm by MATLAB/Simulink and providing the real-time CAN bus network communications in HIL(hardware-in-loop) simulation by CANoe. The simulations tests show that co-simulation system built by the CANoe and the MATLAB/Simulink performs favorably with rapid convergence and validity.

GPU-based Real-time Porous Medium Dynamic Network Simulation

Porous medium network simulation is to accurately describe the seepage law of multiphase fluids in porous media. Researchers have studied the relationship between various macroscopic physical properties of multiphase fluids (e.g. relative permeability, capillary pressure and fluid saturation) from theoretical, experimental, and computer simulation. This research introduces the construction method of stochastic pore network model and describes the calculation steps of pore network pressure field. In this research, the computational efficiency of the GPU-based pore network was compared with CPU. The calculation speed and simulation model scale of the pressure domain of the random pore network were improved.

GPU-based Real-time Porous Medium Dynamic Network Simulation

GPU-based Real-time Porous Medium Dynamic Network Simulation

Planning and real-time management of smart grids with high PV penetration in Italy

For planning and development and in real-time operation of smart grids, it is important to evaluate the impacts of photovoltaic (PV) distributed generation. This paper presents an integrated platform, constituted of two main components: a PV simulator and a real-time distribution network simulator. The first, designed and developed following the microservice approach and providing Representational State Transfer web services, simulates real-sky solar radiation on rooftops and estimates the PV energy production. The second, based on a digital real-time power systems simulator, simulates the behaviour of the electricity network under the simulated generation scenarios. The platform is tested on a case study based on real data for a district of the city of Turin, Italy. In the results, the authors show possible applications of the platform for power flow forecasting during real-time operation and for detecting possible voltage and transformers’ capacity problems during planning due to high penetration of renewable energy sources. In particular, the results show that the case study distribution network, in the actual configuration, is not ready to accommodate all the generation capacity that can be installed, as, in certain hours of the day and on certain days of the year, the capacity of some transformers is exceeded.

Viability of using energy storage for frequency regulation on power grid

This project is about the development and integration of a real-time network simulator in the laboratory using hardware in the loop (HIL) for the purpose of frequency regulation. Frequency regulation is done using the energy storage system (ESS) and a real-time network test bed developed in the smart energy laboratory in Newcastle University. An IEEE Test System was built in the OPAL-RT network simulator to mimic the power grid with renewable energy sources. The study demonstrates the viability of using an ESS to regulate the frequency under an increased penetration of renewable energy sources.

SNAVA—A real-time multi-FPGA multi-model spiking neural network simulation architecture

Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN’s connectivity, to compile the neuron-synapse model and to monitor SNN’s activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities.

Frequency response services designed for energy storage

Energy Storage Systems (ESS) are expected to play a significant role in regulating the frequency of future electric power systems. Increased penetration of renewable generation, and reduction in the inertia provided by large synchronous generators, are likely to increase the severity and regularity of frequency events in synchronous AC power systems. By supplying or absorbing power in response to deviations from the nominal frequency and imbalances between supply and demand, the rapid response of ESS will provide a form of stability which cannot be matched by conventional network assets. However, the increased complexity of ESS operational requirements and design specifications introduces challenges when it comes to the realisation of their full potential through existing frequency response service markets: new service markets will need to be designed to take advantage of the capabilities of ESS. This paper provides new methods to analyse and assessing the performance of ESS within existing service frameworks, using real-time network simulation and power hardware in the loop. These methods can be used to introduce improvements in existing services and potentially create new ones. Novel statistical techniques have been devised to quantify the design and operational requirements of ESS providing frequency regulation services. These new techniques are demonstrated via an illustrative service design and high-resolution frequency data from the Great Britain transmission system.

A Novel Integrated Real-time Simulation Platform for Assessing Photovoltaic Penetration Impacts in Smart Grids

For future planning and development of smart grids, it is important to evaluate the impacts of PV distributed generation, especially in densely populated urban areas. In this paper we present an integrated platform, constituted by two main components: a PV simulator and a real-time distribution network simulator. The first simulates real-sky solar radiation of rooftops and estimates the PV energy production; the second simulates the behaviour of the network when generation and consumption are provided at the different buses. The platform is tested on a case study based on real data for a district of the city of Turin, Italy.

A multi-agent learning approach for online calibration and consistency checking of real-time traffic network management systems

ABSTRACTConsiderable effort has been devoted to developing real-time traffic network management systems for congestion mitigation in large metropolitan areas. These systems usually adopt high-resolution simulation models to provide real-time traffic network state estimation and short-term prediction capabilities. If the simulation results deviate from their corresponding real-world observations, online adjustment to the parameters of the simulation model is recommended to maintain its consistency. For that purpose, several consistency checking and online adjustment modules could be integrated with the simulation model and activated periodically to maintain the model consistency. This paper presents a multi-agent learning methodology for consistency checking and online calibration of real-time traffic network simulation models. The methodology allows multiple online adjustment modules to learn based on their historical performance. Consequently, an adjustment module is activated only if its activation is expected to reduce any detected model inconsistency. The methodology eliminates computation burden associated with the unnecessary activation of these adjustment modules, which might affect the system’s real-time execution requirement. The performance of the methodology is examined using real-world data. The results show that the methodology is promising as an efficient mechanism for maintaining model estimation consistency.

Development and Application of a Real-Time Test Bed for Cyber–Physical System

Enhanced integration of information and communication technologies in the smart grid has led to an increase in the number of cyber assets and has also opened up the possibility of a cyberattack. It is necessary to understand the complex relationship between the cyber and physical domains, and its potential impact on the power grid because of a successful cyber–physical attack. A cyber–physical test bed that can model and simulate the smart grid is necessary to test and validate algorithms and devices. This paper presents the development of an end-to-end, real-time cyber–physical test bed using Real-Time Digital Simulator and Network Simulator 3 (ns-3). A methodology for integrating the hardware phasor measurement unit and the phasor data concentrator in the test bed is presented along with the detailed modeling of the communication network for the power system. The developed test bed is validated and used to demonstrate the impact of different cyberattacks on the power system and tested algorithms.

PrimoGENI for hybrid network simulation and emulation experiments in GENI

The Global Environment for Network Innovations (GENI) is a community-driven research and development effort to build a collaborative and exploratory network experimentation platform—a ‘virtual laboratory’ for the design, implementation, and evaluation of future networks. The PrimoGENI project enables real-time network simulation by extending an existing network simulator to become part of the GENI federation to support large-scale experiments involving physical, simulated, and emulated network entities. In this paper, we describe a novel design of PrimoGENI, which aims at supporting realistic, scalable, and flexible network experiments with real-time simulation and emulation capabilities. We present a flexible emulation infrastructure that allows both remote client machines, local cluster nodes running virtual machines, and external networks to seamlessly interoperate with the simulated network running within a designated ‘slice’ of resources. We present the results of our preliminary validation and performance studies to demonstrate the capabilities as well as limitations of our approach.

A biologically plausible real-time spiking neuron simulation environment based on a multiple-FPGA platform

Neurological research has revealed that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of such neuronal systems. The spiking neuron models which are used in simulations can be described mathematically, but the continuous time involved in mathematical models needs to be replaced by discrete time steps. An alternative approach, hardware implementation, provides the possibility of generating independent spikes precisely and simultaneously output spike waves in real biological time, under the premise that the spiking neural network implemented in hardware can take full advantage of hardware-timed speed and reliability. In this work we propose a multi-layered biologically plausible real-time spiking neural network simulation platform that can be used to emulate the operation of biological neural systems (such as elements of the visual cortex) and computational models of such systems. The implementation of a layered spiking neural network using the Xilinx Virtex-4 family of Field Programmable Gate Array (FPGA) is presented.

Parallel Hybrid Network Traffic Models

Fluid-based network traffic models are attractive due to their execution efficiency. They run much faster than the corresponding discrete-event packet-oriented simulation, especially when we study the aggregate traffic behavior of large-scale network scenarios. The efficiency, however, comes at a cost: fluid modeling does not include packet-level details. The ability to accurately capture the interaction between the packets and the network routers and hosts visited by the packets is essential for real-time network simulations, where the simulator must be able to interact with real applications in real time. In particular, the virtual network must be able to carry real packets subject to proper delays and losses and be able to react to these real packets (such as traceroute). Previously, we presented a hybrid network traffic model that combines a continuous-time fluid model and the discrete-event packet-oriented simulation. In this article, we examine a parallel processing method for simulations of large-scale networks using the hybrid model. Our method benefits from the observation that the time it takes to propagate fluid characteristics along the path taken by the traffic flows has a lower bound equal to the minimum link delay as manifested by the governing ordinary differential equations (ODEs). A better lookahead can thus be used to allow parallel simulation of the hybrid model to run without more synchronization overhead than the corresponding discrete-event packet-oriented model. We derive an analytical model comparing the fluid model and the packet-oriented model both for sequential and parallel simulations. We demonstrate the benefit of the parallel hybrid model through a series of simulation experiments of a large-scale network consisting of over 170 000 hosts and 1.6 million traffic flows on a small parallel cluster.

Real-time network simulation support for scalable routing experiments

This paper describes a new software infrastructure that combines the scalability and flexibility benefits of real-time network simulation with the realism of open-source routing protocol implementations. The infrastructure seamlessly integrates the open-source XORP router implementation with a real-time large-scale network simulation engine. The design uses a novel forwarding plane offloading approach that decouples routing from forwarding and confines the more resource consuming forwarding operations inside the simulation engine to reduce I/O overhead. Experiments demonstrate superior performance of the software routing infrastructure without impairing accuracy. The infrastructure is shown to be able to support large-scale routing experiments on light-weight virtual machines.