In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users.It encompasses several heterogeneous resource and c...In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users.It encompasses several heterogeneous resource and communication standard in ensuring incessant availability of service.At the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,etc.In UAV networks,energy efficiency and data collection are considered the major process for high quality network communication.But these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted UAVs.These issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G environment.With this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G environment.The proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets minimized.The presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct clusters.The QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of UAVs.The performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.展开更多
Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized ...Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.展开更多
Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,etc.Medical image security and privacy become a ...Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,etc.Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks.This paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT environments.The WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data transmission.Initially,the medical images were captured from the patient using IoT devices.Then,the captured images are encrypted using signcryption technique.In addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA algorithm.The goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain securely.The performance of the presented method was analyzed utilizing the benchmark medical image dataset.The security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.展开更多
Normally all real world process in a process industry will have time delay.For those processes with time delays,obtaining satisfactory closed loop performances becomes very difficult.In this work,three interacting cyl...Normally all real world process in a process industry will have time delay.For those processes with time delays,obtaining satisfactory closed loop performances becomes very difficult.In this work,three interacting cylindrical tank process is considered for study and the objective of the work is to compensate for time delays using smith predictor structure and to maintain the level in the third tank.Input/Output data is generated for the three interacting tank process.It is approximated as Integer First Order Plus Dead Time system(IFOPDT)and Fractional First Order Plus Dead Time system(FFOPDT).Smith predictor based fractional order Proportional Integral controller and Integer order Proportional Integral controller is designed for the IFOPDT and FFOPDT model using frequency response technique and their closed loop performance indices are compared and tabulated.The servo and regulatory responses are simulated using Matlab/Simulink.展开更多
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1F1A1063319).
文摘In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile users.It encompasses several heterogeneous resource and communication standard in ensuring incessant availability of service.At the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,etc.In UAV networks,energy efficiency and data collection are considered the major process for high quality network communication.But these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted UAVs.These issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G environment.With this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G environment.The proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets minimized.The presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct clusters.The QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of UAVs.The performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.
文摘Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.
文摘Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,etc.Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks.This paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT environments.The WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data transmission.Initially,the medical images were captured from the patient using IoT devices.Then,the captured images are encrypted using signcryption technique.In addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA algorithm.The goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain securely.The performance of the presented method was analyzed utilizing the benchmark medical image dataset.The security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.
文摘Normally all real world process in a process industry will have time delay.For those processes with time delays,obtaining satisfactory closed loop performances becomes very difficult.In this work,three interacting cylindrical tank process is considered for study and the objective of the work is to compensate for time delays using smith predictor structure and to maintain the level in the third tank.Input/Output data is generated for the three interacting tank process.It is approximated as Integer First Order Plus Dead Time system(IFOPDT)and Fractional First Order Plus Dead Time system(FFOPDT).Smith predictor based fractional order Proportional Integral controller and Integer order Proportional Integral controller is designed for the IFOPDT and FFOPDT model using frequency response technique and their closed loop performance indices are compared and tabulated.The servo and regulatory responses are simulated using Matlab/Simulink.
