Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience ...Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.展开更多
Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is ...Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.展开更多
基金supported by the Shandong Provincial Key Research and Development Program(No.2021CXGC010107)the National Natural Science Foundation of China(Nos.U21A20466,62325209)+3 种基金the New 20 Project of Higher Education of Jinan(No.202228017)the Special Project on Science and Technology Program of Hubei Province(No.2021BAA025)the Fundamental Research Funds for the Central Universities(Nos.2042023kf0203,20420241013)the Researchers Supporting Project Number(RSP2024R509),King Saud University,Riyadh,Saudi Arabia。
文摘Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.
文摘Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.
