Purpose-The rapid proliferation of Internet of Things(IoT)devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet.Fog computing,wh...Purpose-The rapid proliferation of Internet of Things(IoT)devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet.Fog computing,which brings computation resources closer to IoT devices,has emerged as a promising solution.An automatic service placement framework is needed to use fog computing resources efficiently.Design/methodology/approach-In this study,first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers,facilitating automatic application deployment.Next,an enhanced version of the equilibrium optimizer(EO)algorithm,inspired by physics,is designed for service placement in fog computing environments.Findings-Simulations reveal that the proposed approach surpasses existing methods,achieving a 99%success rate compared to the closest alternative’s 93%.The algorithm also significantly reduces waiting and planning times for service placement,proving its efficiency and effectiveness in optimizing IoTservice deployment in fog computing.Research limitations/implications-One of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads.Although improved EO offers improvements in placement efficiency,it may not be fully optimized for highly fluctuating environments.Another important limitation is the uncertainty in node resources.Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes.This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.Practical implications-From a practical perspective,the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems.Organizations that rely on IoT networks,particularly those with critical real-time requirements,can benefit from reduced service placement times and lower failure rates.This can lead to better resource utilization,reduced operational costs and improved overall performance of IoT systems.The commercial impact is evident in industries such as smart cities,healthcare,where fast data processing is crucial.Social implications-Our proposed framework has important implications for real-world IoT applications,particularly in areas requiring low latency processing,such as healthcare,smart cities.By reducing service delays and optimizing resource allocation,the framework can significantly improve the quality and reliability of services.Additionally,improved resource management leads to cost savings and better system efficiency,making the technology accessible to a wider range of applications.Originality/value-Existing resource placement strategies have shown inadequate performance,highlighting the need for more advanced algorithms.This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.展开更多
For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in ...For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.展开更多
Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical opti...Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical optimisation techniques to identify the best course of action for controlling a given objective function over time.Modeling the supply chain’s dynamics,which include elements like production rates,inventory levels,demand trends,and transportation constraints,is the best control strategy when applied to a supply chain.In this study,we have considered that production rate is an unknown function of time,which is a controlling function.The demand for the product is taken as a function of price and time.The emission of carbon is taken as a linear function of the production rate of the system.To solve the suggested supply chain system,we have used an optimal control approach for determining the unknown production rate.To find the optimal values of the objective function as well as the decision variables,we have used different meta-heuristic algorithms and compared their results.It is observed that the equilibrium optimizer algorithm performed better than other algorithms used.Finally,a sensitivity analysis is performed,which is presented graphically in order to choose the best course of action.展开更多
文摘Purpose-The rapid proliferation of Internet of Things(IoT)devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet.Fog computing,which brings computation resources closer to IoT devices,has emerged as a promising solution.An automatic service placement framework is needed to use fog computing resources efficiently.Design/methodology/approach-In this study,first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers,facilitating automatic application deployment.Next,an enhanced version of the equilibrium optimizer(EO)algorithm,inspired by physics,is designed for service placement in fog computing environments.Findings-Simulations reveal that the proposed approach surpasses existing methods,achieving a 99%success rate compared to the closest alternative’s 93%.The algorithm also significantly reduces waiting and planning times for service placement,proving its efficiency and effectiveness in optimizing IoTservice deployment in fog computing.Research limitations/implications-One of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads.Although improved EO offers improvements in placement efficiency,it may not be fully optimized for highly fluctuating environments.Another important limitation is the uncertainty in node resources.Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes.This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.Practical implications-From a practical perspective,the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems.Organizations that rely on IoT networks,particularly those with critical real-time requirements,can benefit from reduced service placement times and lower failure rates.This can lead to better resource utilization,reduced operational costs and improved overall performance of IoT systems.The commercial impact is evident in industries such as smart cities,healthcare,where fast data processing is crucial.Social implications-Our proposed framework has important implications for real-world IoT applications,particularly in areas requiring low latency processing,such as healthcare,smart cities.By reducing service delays and optimizing resource allocation,the framework can significantly improve the quality and reliability of services.Additionally,improved resource management leads to cost savings and better system efficiency,making the technology accessible to a wider range of applications.Originality/value-Existing resource placement strategies have shown inadequate performance,highlighting the need for more advanced algorithms.This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.
基金This work was supported by the National Natural Science Foundation of China(62073093)the Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province(LBH-Q19098)+1 种基金the Heilongjiang Provincial Natural Science Foundation of China(LH2020F017)the Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Information Technology.
文摘For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.
基金supported by UGC SRF Fellowship(NTA Ref.Nos.211610092425 and 201610165233).
文摘Supply chains and other complex systems can be effectively managed and optimised with the help of optimal control techniques.Optimal control,as used in supply chain management,is the process of using mathematical optimisation techniques to identify the best course of action for controlling a given objective function over time.Modeling the supply chain’s dynamics,which include elements like production rates,inventory levels,demand trends,and transportation constraints,is the best control strategy when applied to a supply chain.In this study,we have considered that production rate is an unknown function of time,which is a controlling function.The demand for the product is taken as a function of price and time.The emission of carbon is taken as a linear function of the production rate of the system.To solve the suggested supply chain system,we have used an optimal control approach for determining the unknown production rate.To find the optimal values of the objective function as well as the decision variables,we have used different meta-heuristic algorithms and compared their results.It is observed that the equilibrium optimizer algorithm performed better than other algorithms used.Finally,a sensitivity analysis is performed,which is presented graphically in order to choose the best course of action.
