In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research ha...In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy consumption.However,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the network.The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high availability.We propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS attacks.Given a number of input parameters,our model has two distinct capabilities.First,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions.Second,it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks.The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters.Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.展开更多
Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a devic...Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a device is usually referred to as contacts that the device had in the past. Whatever may be the metric of history, most of these routing protocols work on the realistic premise that node mobility is not truly random. In contrast, there are several oracles based methods where such oracles assist these methods to gain access to information that is unrealistic in the real world. Although, such oracles are unrealistic, they can help to understand the nature and behavior of underlying networks. In this paper, we have analyzed the gap between these two extremes. We have performed max-flow computations on three different opportunistic networks and then compared the results by performing max-flow computations on history generated by the respective networks. We have found that the correctness of the history based prediction of history is dependent on the dense nature of the underlying network. Moreover, the history based prediction can deliver correct paths but cannot guarantee their absolute reliability.展开更多
Federated learning(FL)is essential to energy transition as it leverages decentralized energy data and machine learning to collaborative train global energy predictive models across distributed energy resources while p...Federated learning(FL)is essential to energy transition as it leverages decentralized energy data and machine learning to collaborative train global energy predictive models across distributed energy resources while preserving data privacy.This paper introduces one of the first FL frameworks that efficiently integrates swarm intelligence-based aggregation methods to large-scale energy consumption forecasting,by extending the TensorFlow Federated Core framework with specialized functional enhancements.The primary objective is to enhance forecasting accuracy in decentralized learning settings.We investigated the effectiveness of various nature-inspired metaheuristics for optimizing the aggregation of local model updates from distributed energy resource nodes into a global model for load forecasting tasks,including Grey Wolf Optimization(GWO),Particle Swarm Optimization(PSO),and Firefly Algorithm(FFA)against the standard Federated Averaging(FedAvg)algorithm.Using a real-world dataset comprising of 4,438 distinct energy consumers,we demonstrate that metaheuristic aggregators consistently outperform the most well-known method,Federated Averaging in predictive accuracy.Among these approaches,GWO emerges as the superior performer achieving up to 23.6%error reduction.Our findings underscore the significant potential of metaheuristic-based aggregation mechanisms in improving FL outcomes,particularly in energy forecasting applications involving large-scale distributed data scenarios.展开更多
Accurate forecasting of buildings'energy demand is essential for building operators to manage loads and resources efficiently,and for grid operators to balance local production with demand.However,nowadays models ...Accurate forecasting of buildings'energy demand is essential for building operators to manage loads and resources efficiently,and for grid operators to balance local production with demand.However,nowadays models still struggle to capture nonlinear relationships influenced by external factors like weather and consumer behavior,assume constant variance in energy data over time,and often fail to model sequential data.To address these limitations,we propose a hybrid Transformer-based model with Liquid Neural Networks and learnable encodings for building energy forecasting.The model leverages Dense Layers to learn non-linear mappings to create embeddings that capture underlying patterns in time series energy data.Additionally,a Convolutional Neural Network encoder is integrated to enhance the model's ability to understand temporal dynamics through spatial mappings.To address the limitations of classic attention mechanisms,we implement a reservoir processing module using Liquid Neural Networks which introduces a controlled non-linearity through dynamic reservoir computing,enabling the model to capture complex patterns in the data.For model evaluation,we utilized both pilot data and state-of-the-art datasets to determine the model's performance across various building contexts,including large apartment and commercial buildings and small households,with and without on-site energy production.The proposed transformer model demonstrates good predictive accuracy and training time efficiency across various types of buildings and testing configurations.Specifically,SMAPE scores indicate a reduction in prediction error,with improvements ranging from 1.5%to 50%over basic transformer,LSTM and ANN models while the higher R²values further confirm the model's reliability in capturing energy time series variance.The 8%improvement in training time over the basic transformer model,highlights the hybrid model computational efficiency without compromising accuracy.展开更多
In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue problems.Although PINN has recently been applied to pure fatigue lifetime prediction,it has not y...In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue problems.Although PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting fatigue.We propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation lifetime.Unlike traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy.The DA-PINN method,employed in this study,consists of two main steps.First,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other cases.Second,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue lifetime.The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed.Additionally,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.展开更多
基金This research work was funded by TMR&D Sdn Bhd under project code RDTC160902.
文摘In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy consumption.However,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the network.The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high availability.We propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS attacks.Given a number of input parameters,our model has two distinct capabilities.First,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions.Second,it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks.The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters.Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.
文摘Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a device is usually referred to as contacts that the device had in the past. Whatever may be the metric of history, most of these routing protocols work on the realistic premise that node mobility is not truly random. In contrast, there are several oracles based methods where such oracles assist these methods to gain access to information that is unrealistic in the real world. Although, such oracles are unrealistic, they can help to understand the nature and behavior of underlying networks. In this paper, we have analyzed the gap between these two extremes. We have performed max-flow computations on three different opportunistic networks and then compared the results by performing max-flow computations on history generated by the respective networks. We have found that the correctness of the history based prediction of history is dependent on the dense nature of the underlying network. Moreover, the history based prediction can deliver correct paths but cannot guarantee their absolute reliability.
基金the framework of the Horizon Europe European Commission project DEDALUS(Grant Agreement No.101103998).
文摘Federated learning(FL)is essential to energy transition as it leverages decentralized energy data and machine learning to collaborative train global energy predictive models across distributed energy resources while preserving data privacy.This paper introduces one of the first FL frameworks that efficiently integrates swarm intelligence-based aggregation methods to large-scale energy consumption forecasting,by extending the TensorFlow Federated Core framework with specialized functional enhancements.The primary objective is to enhance forecasting accuracy in decentralized learning settings.We investigated the effectiveness of various nature-inspired metaheuristics for optimizing the aggregation of local model updates from distributed energy resource nodes into a global model for load forecasting tasks,including Grey Wolf Optimization(GWO),Particle Swarm Optimization(PSO),and Firefly Algorithm(FFA)against the standard Federated Averaging(FedAvg)algorithm.Using a real-world dataset comprising of 4,438 distinct energy consumers,we demonstrate that metaheuristic aggregators consistently outperform the most well-known method,Federated Averaging in predictive accuracy.Among these approaches,GWO emerges as the superior performer achieving up to 23.6%error reduction.Our findings underscore the significant potential of metaheuristic-based aggregation mechanisms in improving FL outcomes,particularly in energy forecasting applications involving large-scale distributed data scenarios.
基金the DEDALUS project grant number 101103998 funded by the European Commission as part of the Horizon Europe Framework Programme and within Ministry of Research,Innovation and Digitization,CNCS/CCCDI-UEFISCDI,project number PN-IV-P8-8.1-PRE-HE-ORG-2023-0111,within PNCDI IV.
文摘Accurate forecasting of buildings'energy demand is essential for building operators to manage loads and resources efficiently,and for grid operators to balance local production with demand.However,nowadays models still struggle to capture nonlinear relationships influenced by external factors like weather and consumer behavior,assume constant variance in energy data over time,and often fail to model sequential data.To address these limitations,we propose a hybrid Transformer-based model with Liquid Neural Networks and learnable encodings for building energy forecasting.The model leverages Dense Layers to learn non-linear mappings to create embeddings that capture underlying patterns in time series energy data.Additionally,a Convolutional Neural Network encoder is integrated to enhance the model's ability to understand temporal dynamics through spatial mappings.To address the limitations of classic attention mechanisms,we implement a reservoir processing module using Liquid Neural Networks which introduces a controlled non-linearity through dynamic reservoir computing,enabling the model to capture complex patterns in the data.For model evaluation,we utilized both pilot data and state-of-the-art datasets to determine the model's performance across various building contexts,including large apartment and commercial buildings and small households,with and without on-site energy production.The proposed transformer model demonstrates good predictive accuracy and training time efficiency across various types of buildings and testing configurations.Specifically,SMAPE scores indicate a reduction in prediction error,with improvements ranging from 1.5%to 50%over basic transformer,LSTM and ANN models while the higher R²values further confirm the model's reliability in capturing energy time series variance.The 8%improvement in training time over the basic transformer model,highlights the hybrid model computational efficiency without compromising accuracy.
基金China Scholarship Council,Grant/Award Number:202008130124National Natural Science Foundation of China,Grant/Award Number:12272270+1 种基金Shanghai Pilot Program for Basic ResearchSlovenian Research Agency research core funding,Grant/Award Number:P2-0095。
文摘In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue problems.Although PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting fatigue.We propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation lifetime.Unlike traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy.The DA-PINN method,employed in this study,consists of two main steps.First,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other cases.Second,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue lifetime.The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed.Additionally,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.
