The rapid advancements in distributed generation technologies,the widespread adoption of distributed energy resources,and the integration of 5G technology have spurred sharing economy businesses within the electricity...The rapid advancements in distributed generation technologies,the widespread adoption of distributed energy resources,and the integration of 5G technology have spurred sharing economy businesses within the electricity sector.Revolutionary technologies such as blockchain,5G connectivity,and Internet of Things(IoT)devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and demand.Nevertheless,sharing electricity within a smart community presents numerous challenges,including intricate design considerations,equitable allocation,and accurate forecasting due to the lack of well-organized temporal parameters.To address these challenges,this proposed system is focused on sharing extra electricity within the smart community.The working of the proposed system is composed of five main phases.In phase 1,we develop a model to forecast the energy consumption of the appliances using the Long Short-Term Memory(LSTM)integrated with the attention module.In phase 2,based on the predicted energy consumption,we designed a smart scheduler with attention-induced Genetic Algorithm(GA)to schedule the appliances to reduce energy consumption.In phase 3,a dynamic Feed-in Tariff(dFIT)algorithm makes real-time tariff adjustments using LSTM for demand prediction and SHapley Additive exPlanations(SHAP)values to improve model transparency.In phase 4,the energy saved from solar systems and smart scheduling is shared with the community grid.Finally,in phase 5,SDP security ensures the integrity and confidentiality of shared energy data.To evaluate the performance of energy sharing and scheduling for houses with and without solar support,we simulated the above phases using data obtained from the energy consumption of 17 household appliances in our IoT laboratory.Finally,the simulation results show that the proposed scheme reduces energy consumption and ensures secure and efficient distribution with peers,promoting a more sustainable energy management and resilient smart community.展开更多
The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitt...The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user signals.Although its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power domain.In contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum utilization.However,it will increase the complexity of its receiver.Different power allocation techniques will have a direct impact on the system’s throughput.As a result,in order to boost the system capacity,an efficient power allocation mechanism must be investigated.This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution.The major goal is to maximize the users’maximum weighted sum rate.The suggested algorithm’s most notable feature is that it converges to the global optimal solution.When compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.展开更多
Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control ce...Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.展开更多
基金Funded by Kuwait Foundation for the Advancement of Sciences(KFAS)under project code:PN23-15EM-1901.
文摘The rapid advancements in distributed generation technologies,the widespread adoption of distributed energy resources,and the integration of 5G technology have spurred sharing economy businesses within the electricity sector.Revolutionary technologies such as blockchain,5G connectivity,and Internet of Things(IoT)devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and demand.Nevertheless,sharing electricity within a smart community presents numerous challenges,including intricate design considerations,equitable allocation,and accurate forecasting due to the lack of well-organized temporal parameters.To address these challenges,this proposed system is focused on sharing extra electricity within the smart community.The working of the proposed system is composed of five main phases.In phase 1,we develop a model to forecast the energy consumption of the appliances using the Long Short-Term Memory(LSTM)integrated with the attention module.In phase 2,based on the predicted energy consumption,we designed a smart scheduler with attention-induced Genetic Algorithm(GA)to schedule the appliances to reduce energy consumption.In phase 3,a dynamic Feed-in Tariff(dFIT)algorithm makes real-time tariff adjustments using LSTM for demand prediction and SHapley Additive exPlanations(SHAP)values to improve model transparency.In phase 4,the energy saved from solar systems and smart scheduling is shared with the community grid.Finally,in phase 5,SDP security ensures the integrity and confidentiality of shared energy data.To evaluate the performance of energy sharing and scheduling for houses with and without solar support,we simulated the above phases using data obtained from the energy consumption of 17 household appliances in our IoT laboratory.Finally,the simulation results show that the proposed scheme reduces energy consumption and ensures secure and efficient distribution with peers,promoting a more sustainable energy management and resilient smart community.
基金the support from Taif University Researchers Supporting Project Number(TURSP-2020/331)Taif University,Taif,Saudi Arabia.This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the National Research Foundation(NRF),Korea(2022R1A2C4001270).
文摘The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user signals.Although its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power domain.In contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum utilization.However,it will increase the complexity of its receiver.Different power allocation techniques will have a direct impact on the system’s throughput.As a result,in order to boost the system capacity,an efficient power allocation mechanism must be investigated.This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution.The major goal is to maximize the users’maximum weighted sum rate.The suggested algorithm’s most notable feature is that it converges to the global optimal solution.When compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.
基金This publication was supported by the Ministry of Education,Malaysia(Grant code:FRGS/1/2018/ICT02/UKM/02/6).
文摘Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.
