Internet of Things(IoT)network used for industrial management is vulnerable to different security threats due to its unstructured deployment,and dynamic communication behavior.In literature various mechanisms addresse...Internet of Things(IoT)network used for industrial management is vulnerable to different security threats due to its unstructured deployment,and dynamic communication behavior.In literature various mechanisms addressed the security issue of Industrial IoT networks,but proper maintenance of the performance reliability is among the common challenges.In this paper,we proposed an intelligent mutual authentication scheme leveraging authentication aware node(AAN)and base station(BS)to identify routing attacks in Industrial IoT networks.The AAN and BS uses the communication parameter such as a route request(RREQ),node-ID,received signal strength(RSS),and round-trip time(RTT)information to identify malicious devices and routes in the deployed network.The feasibility of the proposed model is validated in the simulation environment,where OMNeT++was used as a simulation tool.We compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection,communication cost,latency,computational cost,and throughput.The results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7%.展开更多
Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best asp...Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.展开更多
A cognitive radio network(CRN)intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization.In CRNs,the secondary users(SUs)opportunistical...A cognitive radio network(CRN)intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization.In CRNs,the secondary users(SUs)opportunistically access the primary users(PUs)spectrum.Therefore,unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs.Cooperative spectrum sensing(CSS)is rated as the best choice for making reliable sensing decisions.This paper employs machinelearning tools to sense the PU channels reliably in CSS.The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput.The fine-k-nearest neighbor algorithm(FKNN),employed in this paper,estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands.The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules,validating the effectiveness of the proposed scheme.Furthermore,the global decision made at the fusion center(FC)based on the modified sensing samples,results low energy consumption,higher throughput,and improved detection with low error probabilities.展开更多
Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including...Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.展开更多
Multichip on Ahnnintnn Metal Plate（MOAMP） technology with simple structure and low thermal resistance is developed for effective heat reratrval of Light Emitting Diode（LED） p-n junction and LED lighting module to ...Multichip on Ahnnintnn Metal Plate（MOAMP） technology with simple structure and low thermal resistance is developed for effective heat reratrval of Light Emitting Diode（LED） p-n junction and LED lighting module to have high reliability. The thermal resistance of LED modules was numerical and experimental. Thermal resistance from the jtnction to aluminten metal plate, considering input power of IFD module using MOAMP technology, is 3.02 K/W, 3.23 K/W for the measured and calculated, respectively. We expect that the reported MOAMP technology with low thermal resistance will be a promising solution for high power LED fighting modules.展开更多
Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addi...Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addition,the performance of a model decreases as the subject’s distance from the camera increases.This study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition accuracy.The 20BN-Jester dataset was used to train the model for six gesture classes.After achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the camera.Despite being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition results.In the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera.Additionally,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,respectively.We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.展开更多
This paper proposes a new FIR （finite impulse response） filter under a least squares criterion using a forgetting factor. The proposed FIR filter does not require information of the noise covariances as well as the ...This paper proposes a new FIR （finite impulse response） filter under a least squares criterion using a forgetting factor. The proposed FIR filter does not require information of the noise covariances as well as the initial state, and has some inherent properties such as time-invariance, unbiasedness and deadbeat. The proposed FIR filter is represented in a batch form and then a recursive form as an alternative form. Prom discussions about the choice of a forgetting factor and a window length, it is shown that they can be considered as useful parameters to make the estimation performance of the proposed FIR filter as good as possible. It is shown that the proposed FIR filter can outperform the existing FIR filter with incorrect noise covariances via computer simulations. Finally, as a useful application, an image sequence stabilization problem is considered. Through this application, the FIR filtering based approach is shown to be superior to the Kalman filtering based approach.展开更多
基金supported by the MSIT(Ministry of Science and ICT),Korea under the ITRC(Information Technology Research Center)support program(IITP-2020-2018-0-01426)supervised by IITP(Institute for Information and Communication Technology Planning&Evaluation)+1 种基金in part by the National Research Foundation(NRF)funded by the Korea government(MSIT)(No.2019R1F1A1059125).
文摘Internet of Things(IoT)network used for industrial management is vulnerable to different security threats due to its unstructured deployment,and dynamic communication behavior.In literature various mechanisms addressed the security issue of Industrial IoT networks,but proper maintenance of the performance reliability is among the common challenges.In this paper,we proposed an intelligent mutual authentication scheme leveraging authentication aware node(AAN)and base station(BS)to identify routing attacks in Industrial IoT networks.The AAN and BS uses the communication parameter such as a route request(RREQ),node-ID,received signal strength(RSS),and round-trip time(RTT)information to identify malicious devices and routes in the deployed network.The feasibility of the proposed model is validated in the simulation environment,where OMNeT++was used as a simulation tool.We compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection,communication cost,latency,computational cost,and throughput.The results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7%.
基金This work was supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2016R1C1B1014069)in part by the National Research Foundation of Korea(NRF)funded by the Korea government(MIST)(No.2021R1A2C1013150).
文摘Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.
基金This work was supported in part by the Ministry of Science and ICT(MSIT),Korea,under the Information and Technology Research Center(ITRC)support program(IITP-2022-2018-0-01426)in part by the National Research Foundation of Korea(NRF)funded by theKorea government(MSIT)(No.2021R1A2C1013150).
文摘A cognitive radio network(CRN)intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization.In CRNs,the secondary users(SUs)opportunistically access the primary users(PUs)spectrum.Therefore,unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs.Cooperative spectrum sensing(CSS)is rated as the best choice for making reliable sensing decisions.This paper employs machinelearning tools to sense the PU channels reliably in CSS.The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput.The fine-k-nearest neighbor algorithm(FKNN),employed in this paper,estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands.The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules,validating the effectiveness of the proposed scheme.Furthermore,the global decision made at the fusion center(FC)based on the modified sensing samples,results low energy consumption,higher throughput,and improved detection with low error probabilities.
基金This work was supported in part by the Ministry of Science and ICT(MSIT)Korea,under the Information and Technology Research Center(ITRC)support program(IITP-2021-2018-0-01426)in part by the National Research Foundation of Korea(NRF)funded by the Korea government(MSIT)(No.2019R1F1A1059125).
文摘Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.
文摘Multichip on Ahnnintnn Metal Plate（MOAMP） technology with simple structure and low thermal resistance is developed for effective heat reratrval of Light Emitting Diode（LED） p-n junction and LED lighting module to have high reliability. The thermal resistance of LED modules was numerical and experimental. Thermal resistance from the jtnction to aluminten metal plate, considering input power of IFD module using MOAMP technology, is 3.02 K/W, 3.23 K/W for the measured and calculated, respectively. We expect that the reported MOAMP technology with low thermal resistance will be a promising solution for high power LED fighting modules.
文摘Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addition,the performance of a model decreases as the subject’s distance from the camera increases.This study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition accuracy.The 20BN-Jester dataset was used to train the model for six gesture classes.After achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the camera.Despite being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition results.In the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera.Additionally,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,respectively.We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.
文摘This paper proposes a new FIR （finite impulse response） filter under a least squares criterion using a forgetting factor. The proposed FIR filter does not require information of the noise covariances as well as the initial state, and has some inherent properties such as time-invariance, unbiasedness and deadbeat. The proposed FIR filter is represented in a batch form and then a recursive form as an alternative form. Prom discussions about the choice of a forgetting factor and a window length, it is shown that they can be considered as useful parameters to make the estimation performance of the proposed FIR filter as good as possible. It is shown that the proposed FIR filter can outperform the existing FIR filter with incorrect noise covariances via computer simulations. Finally, as a useful application, an image sequence stabilization problem is considered. Through this application, the FIR filtering based approach is shown to be superior to the Kalman filtering based approach.
