Cognitive Internet of Vehicles(CIoV)can improve spectrum utilization by accessing the spectrum licensed to primary user(PU)under the premise of not disturbing the PU’s transmissions.However,the traditional static spe...Cognitive Internet of Vehicles(CIoV)can improve spectrum utilization by accessing the spectrum licensed to primary user(PU)under the premise of not disturbing the PU’s transmissions.However,the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments.In this paper,a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum,and avoid causing harmful interference to the PU.The frame structure of the CIoV is separated into sensing period and access period,whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period.Considering both detection probability and false alarm probability,a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel,bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance.The simulations have shown that compared with the traditional non-learning spectrum access algorithm,the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.展开更多
Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper...Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming(MRC/ZFB)scheme to guarantee the secrecy perfor-mance of dual-hop short-packet communications in cognitive IoT.This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ra-tio(SNR)regime and the infinite blocklength.In ad-dition,the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput.The nu-merical results verify the impact of the system pa-rameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a se-crecy constraint.It is shown that the proposed scheme achieves better secrecy performance than other bench-mark schemes.展开更多
Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the...Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried out.In addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.展开更多
基金This work was supported by the Joint Foundations of the National Natural Science Foundations of China and the Civil Aviation of China under Grant U1833102the Natural Science Foundation of Liaoning Province under Grants 2020-HYLH-13 and 2019-ZD-0014+1 种基金the fundamental research funds for the central universities under Grant DUT21JC20the Engineering Research Center of Mobile Communications,Ministry of Education.
文摘Cognitive Internet of Vehicles(CIoV)can improve spectrum utilization by accessing the spectrum licensed to primary user(PU)under the premise of not disturbing the PU’s transmissions.However,the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments.In this paper,a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum,and avoid causing harmful interference to the PU.The frame structure of the CIoV is separated into sensing period and access period,whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period.Considering both detection probability and false alarm probability,a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel,bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance.The simulations have shown that compared with the traditional non-learning spectrum access algorithm,the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.
基金Natural Science Foun-dation of China(No.62171464,61801496 and 61771487)This paper was presented in part at the 2021 IEEE International Conference on Communica-tions Workshops(ICC Workshops),2021.
文摘Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming(MRC/ZFB)scheme to guarantee the secrecy perfor-mance of dual-hop short-packet communications in cognitive IoT.This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ra-tio(SNR)regime and the infinite blocklength.In ad-dition,the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput.The nu-merical results verify the impact of the system pa-rameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a se-crecy constraint.It is shown that the proposed scheme achieves better secrecy performance than other bench-mark schemes.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/23/42),Received by Fahd N.Al-Wesabi.www.kku.edu.sa.
文摘Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried out.In addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.