scholarly journals Randomization-Based Dynamic Programming Offloading Algorithm for Mobile Fog Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenle Bai ◽  
Zhongjun Yang ◽  
Jianhong Zhang ◽  
Rajiv Kumar
Keyword(s):  
Dynamic Programming ◽  
Approximate Solution ◽  
Fog Computing ◽  
Optimization Theory ◽  
Genetic Optimization ◽  
Computational Load ◽  
Generation Problem

Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.

scholarly journals SWIPT-Aware Fog Information Processing: Local Computing vs. Fog Offloading

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3291 ◽  
Author(s):  
Haina Zheng ◽  
Ke Xiong ◽  
Pingyi Fan ◽  
Li Zhou ◽  
Zhangdui Zhong
Keyword(s):  
Information Processing ◽  
Closed Form ◽  
Optimization Problems ◽  
Fog Computing ◽  
Mode Selection ◽  
Optimization Theory ◽  
Access Point ◽  
Power Splitting ◽  
Closed Form Solutions ◽  
Processing Modes

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed.

scholarly journals An example to illustrate several aspects of optimization theory in Managerial Economics

2021 ◽  
Vol 22 (1) ◽  
pp. 23-34
Author(s):  
Somdeb Lahiri
Keyword(s):  
Dynamic Programming ◽  
Imperfect Information ◽  
Optimization Theory ◽  
Managerial Economics

We provide a single example that illustrates all aspects of linear, integer and dynamic programming, including such concepts such as value of perfect and imperfect information. Such problems, though extremely plausible and realistic are hardly ever discussed in managerial economics.

scholarly journals A simultaneous trajectory generation method for quadcopter intercepting ground mobile vehicle

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141771770 ◽  
Author(s):  
Jiangcheng Zhu ◽  
Jun Zhu ◽  
Chao Xu
Keyword(s):  
Dynamic Programming ◽  
Trajectory Generation ◽  
Closed Form Solution ◽  
Form Solution ◽  
Ground Vehicle ◽  
Mobile Vehicle ◽  
Commercial Flight ◽  
Interaction Problem ◽  
Trajectory Generator ◽  
Generation Problem

This article proposes a trajectory generator for quadcopter to intercept moving ground vehicle. For this air–ground interaction problem, we formulate the trajectory generation problem as quadratic dynamic programming in a moving-horizon scheme based on the quadcopter kinematics and observation to ground vehicle. The closed-form solution of quadratic dynamic programming in each iteration enables this algorithm a real-time replanning performance. Thereafter, segmented trajectory rule, inspired from commercial flight landing regular, is implemented to guarantee smoothness in approaching and interception to moving ground target from comparably far origin. Our established algorithm is verified through both simulations and experiments.

scholarly journals Introducing Dynamic Programming and Persistently Exciting into Data-Driven Model Predictive Control

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hong Jianwang ◽  
Ricardo A. Ramirez-Mendoza ◽  
Ruben Morales-Menendez
Keyword(s):  
Dynamic Programming ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Minimum Principle ◽  
Short Note ◽  
Optimization Theory ◽  
Inequality Constraint ◽  
Data Driven ◽  
Control Input ◽  
Control Scheme

In this paper, one new data-driven model predictive control scheme is proposed to adjust the varying coupling conditions between different parts of the system; it means that each group of linked subsystems is grouped as data-driven scheme, and this group is independently controlled through a decentralized model predictive control scheme. After combing coalitional scheme and model predictive control, coalitional model predictive control is used to design each controller, respectively. As the dynamic programming is only used in optimization theory, to extend its advantage in control theory, the idea of dynamic programming is applied to analyze the minimum principle and stability for the data-driven model predictive control. Further, the goal of this short note is to bridge the dynamic programming with model predictive control. Through adding the inequality constraint to the constructed model predictive control, one persistently exciting data-driven model predictive control is obtained. The inequality constraint corresponds to the condition of persistent excitation, coming from the theory of system identification. According to the numerical optimization theory, the necessary optimality condition is applied to acquire the optimal control input. Finally, one simulation example is used to prove the efficiency of our proposed theory.

scholarly journals The Solution of Fully Fuzzy Quadratic Equation Based on Optimization Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
T. Allahviranloo ◽  
L. Gerami Moazam
Keyword(s):  
Approximate Solution ◽  
Fuzzy Number ◽  
Real Root ◽  
Optimization Theory ◽  
Quadratic Equation ◽  
Core Point ◽  
The Core

Firstly in this paper we introduce a new concept of the 2nd power of a fuzzy number. It is exponent to production (EP) method that provides an analytical and approximate solution for fully fuzzy quadratic equation (FFQE) :F(X̃)=D̃, whereF(X̃)=ÃX̃2+B̃X̃+C̃. To use the mentioned EP method, at first the 1-cut solution of FFQE as a real root is obtained and then unknown manipulated unsymmetrical spreads are allocated to the core point. To this purpose we findλandμas optimum values which construct the best spreads. Finally to illustrate easy application and rich behavior of EP method, several examples are given.

scholarly journals Application of dynamic programming methods for solving a problem on recruiting with considering specific content of military technical education

2021 ◽  
Vol 10 (2) ◽  
pp. 309-314
Author(s):  
Tatiana Evgenjevna Tarasova ◽  
Anatoly Vladimirovich Tarasov ◽  
Tatiana Sergeevna Smirnova
Keyword(s):  
Dynamic Programming ◽  
Computer Science ◽  
Military Service ◽  
Optimization Problems ◽  
Practical Knowledge ◽  
Optimization Theory ◽  
Task Assignment ◽  
Specific Content ◽  
Training Approach ◽  
High Level

This paper discusses the use of dynamic programming technologies in teaching cadets of military universities to solve optimization problems in the course of computer science. One of the key topics in such courses as higher mathematics and computer science in all civil and military technical universities is the optimization theory, familiarization with which is based on learning methods for solving a transport task, assignment problem, traveling salesman problem and others. An effective solution to this type of tasks is possible through automated computing tools, tabular processors, and programming systems. The specifics of training cadets at military universities dictates the need to formulate tasks with a focus on military-technical research. Optimization issues are considered as applied to possible real situations in the military service of future officers. The staffing task is solved through high-level programming. Some results of the comparative analysis of educational material assimilation in the control and experimental groups are given. A deeper understanding of the theoretical material by the cadets and confident practical knowledge of programming technologies and solving problems in general with the specified training approach are noted, and its confirmed by the results of the tests conducted by the authors of the paper.

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