Internet of Things (IoT) technologies are being used in smart towns because people want more clever, efficient, and environmentally friendly systems. But the fact that these methods use a lot of energy is still a big ...
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ISBN:
(数字)9798331515683
ISBN:
(纸本)9798331515690
Internet of Things (IoT) technologies are being used in smart towns because people want more clever, efficient, and environmentally friendly systems. But the fact that these methods use a lot of energy is still a big worry. This article talks about creating an IoT design that uses less energy by using Edge and Fog computing to make the best use of resources and boost speed in smart city apps. The suggested design uses both Edge computing and Fog computing. Edge devices process data locally, which means they don't need to use cloud services as much. Fog nodes bring processing even closer to the edge of the network, which ensures low delay and low energy use. The design includes energy-aware programs that make the best use of real-time energy supply and network conditions to send and process data. Our results show that smart city IoT systems actually use a lot less energy and work much more efficiently. We use a lot of models to show that moving processing from the cloud to the edge and fog layers can cut the total amount of energy needed by up to 30%. The suggested structure also guarantees very little delay in data processing, which is very important for real-time smart city applications like tracking the environment, managing traffic, and collecting trash. The results show that combining Edge and Fog computing can make IoT systems in smart towns more energy-efficient.
Provides information on a study which presented a trust region approach for solving nonlinear constrained optimization. Algorithm of the trust region approach; Information on the global convergence of the algorithm; N...
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Provides information on a study which presented a trust region approach for solving nonlinear constrained optimization. Algorithm of the trust region approach; Information on the global convergence of the algorithm; Numerical results of the study.
An extended semi-definite programming, the SDP with an additional quadratic term in the objective function, is studied. Our generalization is similar to the generalization from linear programming to quadratic programm...
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An extended semi-definite programming, the SDP with an additional quadratic term in the objective function, is studied. Our generalization is similar to the generalization from linear programming to quadratic programming. Optimal conditions for this new class of problems are discussed and a potential reduction algorithm for solving QSDP problems is presented. The convergence properties of this algorithm are also given.
This paper deals with boundary value problems for linear uniformly elliptic systems. First the general linear uniformly elliptic system of the first order equations is reduced to complex form, and then the compound bo...
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This paper deals with boundary value problems for linear uniformly elliptic systems. First the general linear uniformly elliptic system of the first order equations is reduced to complex form, and then the compound boundary value problem for the complex equations of the first order is discussed. The approximate solutions of the boundary value problem are found by the variation-difference method, and the error estimates for the approximate solutions are *** the approximate method of the oblique derivative problem for linear uniformly elliptic equations of the second or der is introduced.
The current research explores the influence of bio-convection, thermophoresis and Brownian motion (TBM) on the Casson liquid flow across a stretchable surface subjected to porous media. Additionally, the consequence o...
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作者：
Peng, JMAssistant Professor
State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academia Sinica Beijing China
The implicit Lagrangian has attracted much attention recently because of its utility in reformulating complementarity and variational inequality problems as unconstrained minimization problems, II was first proposed b...
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The implicit Lagrangian has attracted much attention recently because of its utility in reformulating complementarity and variational inequality problems as unconstrained minimization problems, II was first proposed by Mangasarian and Solodov as a merit function for the nonlinear complementarity problem (Ref. 1). Three open problems were also raised in the same paper, This paper addresses, among other issues, one of these problems by giving the properties of the implicit Lagrangian and establishing its convexity under appropriate assumptions.
As sustainable development gains attention, integrating carbon-intelligent computing into fault diagnosis systems has emerged as a critical strategy to reduce energy consumption and carbon footprints. This approach us...
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In the present paper,we study the restricted inexact Newton-type method for solving the generalized equation 0∈f(x)+F(x),where X and Y are Banach spaces,f:X→Y is a Frechet differentiable function and F:X■Y is a set...
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In the present paper,we study the restricted inexact Newton-type method for solving the generalized equation 0∈f(x)+F(x),where X and Y are Banach spaces,f:X→Y is a Frechet differentiable function and F:X■Y is a set-valued mapping with closed *** establish the convergence criteria of the restricted inexact Newton-type method,which guarantees the existence of any sequence generated by this method and show this generated sequence is convergent linearly and quadratically according to the particular assumptions on the Frechet derivative of ***,we obtain semilocal and local convergence results of restricted inexact Newton-type method for solving the above generalized equation when the Frechet derivative of f is continuous and Lipschitz continuous as well as f+F is metrically *** application of this method to variational inequality is *** addition,a numerical experiment is given which illustrates the theoretical result.
This study examines how imbalanced datasets affect the accuracy of machine learning models, especially in predictive analytics applications such as churn prediction. When datasets are skewed towards the majority class...
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