CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit ...CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.展开更多
Cognitive Radio (CR) is an intelligent radio communication system, whose intelligence mostly comes from the Cognitive Engine (CE). Based on the techniques of software-defined radio and with the support of machine reas...Cognitive Radio (CR) is an intelligent radio communication system, whose intelligence mostly comes from the Cognitive Engine (CE). Based on the techniques of software-defined radio and with the support of machine reasoning and learning in artificial intelligence, cognitive engine implements the cognitive loop to realize the abilities of sensing, adaptation and learning in CR. Cognitive engine consists of the modeling system, knowledge base, reasoning engine, learning engine and interfaces. The key techniques are knowledge representation, machine reasoning and machine learning.展开更多
The paper presents the conceptual and operational basis of the creation of IDSS based on our recent research experiences. In this paper, an intelligent decision support system, IDSS is defined as: "any interactiv...The paper presents the conceptual and operational basis of the creation of IDSS based on our recent research experiences. In this paper, an intelligent decision support system, IDSS is defined as: "any interactive system that is specially desinged to improve the decision making of its user by extending the user’s cognitive decision making abilities." As a result, this view of man-machine joint cognitive system stresses the need to use computational technology to aid the user in the decision making process. And the human’s role is to achieve total system’s objectives. The paper outlines the designing procedure in successive steps. The first identifies the decision maker’s cognitive needs for decision support. The second defines the computationally realizable support functions that could be provided by IDSS. The third, the specific techniques that would best fill the decision needs are discussed. And finally, for system implementation the modern computational technology infrastructure is emphasized.展开更多
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r...Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.展开更多
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r...Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.展开更多
基金The authors would like to thank for the support from Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia。
文摘CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.
基金The work was supported by the National Basic Research Program of China("973" Program) under Grant No. 2009CB320403the National Natural Science Foundation of China under Grant No. 60832008.
文摘Cognitive Radio (CR) is an intelligent radio communication system, whose intelligence mostly comes from the Cognitive Engine (CE). Based on the techniques of software-defined radio and with the support of machine reasoning and learning in artificial intelligence, cognitive engine implements the cognitive loop to realize the abilities of sensing, adaptation and learning in CR. Cognitive engine consists of the modeling system, knowledge base, reasoning engine, learning engine and interfaces. The key techniques are knowledge representation, machine reasoning and machine learning.
文摘The paper presents the conceptual and operational basis of the creation of IDSS based on our recent research experiences. In this paper, an intelligent decision support system, IDSS is defined as: "any interactive system that is specially desinged to improve the decision making of its user by extending the user’s cognitive decision making abilities." As a result, this view of man-machine joint cognitive system stresses the need to use computational technology to aid the user in the decision making process. And the human’s role is to achieve total system’s objectives. The paper outlines the designing procedure in successive steps. The first identifies the decision maker’s cognitive needs for decision support. The second defines the computationally realizable support functions that could be provided by IDSS. The third, the specific techniques that would best fill the decision needs are discussed. And finally, for system implementation the modern computational technology infrastructure is emphasized.
基金This work was funded by the UK EPSRC[grant number:EP/S035362/1,EP/N023013/1,EP/N02334X/1]and by the Cisco Research Centre[grant number 1525381].
文摘Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.
基金funded by the UK EPSRC[grant number:EP/S035362/1,EP/N023013/1,EP/N02334X/1]by the Cisco Research Centre[grant number 1525381].
文摘Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.