scholarly journals User mobility-aware decision making for mobile computation offloading

2013 ◽  
Author(s):  
Kilho Lee ◽  
Insik Shin
Keyword(s):  
Decision Making ◽  
Computation Offloading ◽  
User Mobility

On the Efficacy of Computation Offloading Decision-Making Strategies

2008 ◽  
Vol 22 (4) ◽  
pp. 460-479 ◽  
Author(s):  
Selim Gurun ◽  
Rich Wolski ◽  
Chandra Krintz ◽  
Dan Nurmi
Keyword(s):  
Decision Making ◽  
Computation Offloading

ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model

2019 ◽  
Vol 16 (4) ◽  
pp. 53-73
Author(s):  
Abdulrahman Elhosuieny ◽  
Mofreh Salem ◽  
Amr Thabet ◽  
Abdelhameed Ibrahim
Keyword(s):  
Decision Making ◽  
Adaptive Learning ◽  
Execution Time ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Matrix Multiplication ◽  
Computation Offloading ◽  
Phase Curve ◽  
Predicting Model ◽  
Major Interest

Nowadays, mobile computation applications attract major interest of researchers. Limited processing power and short battery lifetime is an obstacle in executing computationally-intensive applications. This article presents a mobile computation automatic decision-making offloading framework. The proposed framework consists of two phases: adaptive learning, and modeling and runtime computation offloading. In the adaptive phase, curve-fitting (CF) technique based on non-linear polynomial regression (NPR) methodology is used to build an approximate time-predicting model that can estimate the execution time for spending the processing of the detected-intensive applications. The runtime computation phase uses the time predicting model for computing the predicted execution time to decide whether to run the application remotely and perform the offloading process or to run the application locally. Eventually, the RESTful web service is applied to carry out the offloading task in the case of a positive offloading decision. The proposed framework experimentally outperforms a competitive state-of-the-art technique by 73% concerning the time factor. The proposed time-predicting model records minimal deviation of the originally obtained values as it is applied 0.4997, 8.9636, 0.0020, and 0.6797 on the mean squared error metric for matrix-determinant, image-sharpening, matrix-multiplication, and n-queens problems, respectively.

The Effect of Neighboring Cells on Handover Decision Making Based on Fuzzy in the WCDMA Network

2017 ◽  
Vol 5 (2) ◽  
pp. 52-58
Author(s):  
Akbar Nur
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Base Station ◽  
Test Method ◽  
User Mobility ◽  
Handover Decision ◽  
Final Project ◽  
Soft Handover ◽  
Hard Handover ◽  
The Impact

In channel transfer (handover) from one Base Station to another Base Station. The purpose of this final project is to analyze the effect of neighboring cells on handover decisions on WCDMA networks based on fuzzy, in this handover process, handover decisions use several parameters related to handovers and supported by fuzzy logic. Relatively high user mobility demands a guarantee until the use of the service ends, the impact of user mobility results in the output being analyzed for this handover decision to help give consideration to the optimal handover decision. The method used is Tsukamoto fuzzy logic, for decision making, while the measurement method In the field, the drive test method is carried out by measuring the signal level around the base station area, and comparing the results of the two methods. Comparison of handover decisions between the results of fuzzy logic and measurements, for example for the results of no proper in fuzzy logic, yields a rate value of 0% for soft handovers and 100% for hard handovers, and for proper results in fuzzy logic, yields a rate value for measurement. 95.22% for soft / soft handover and 4.72% for hard handover

scholarly journals Computation Offloading Strategy in Mobile Edge Computing

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 191 ◽  
Author(s):  
Jinfang Sheng ◽  
Jie Hu ◽  
Xiaoyu Teng ◽  
Bin Wang ◽  
Xiaoxia Pan
Keyword(s):  
Decision Making ◽  
User Experience ◽  
Task Scheduling ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Smart Devices ◽  
Mobile Edge Computing ◽  
Network Bandwidth ◽  
Intelligent Devices

Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem. In this paper, we propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision-making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi-objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

LPCD framework: Analytical tool or psychological model?

2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks
Keyword(s):  
Decision Making ◽  
Analytical Tool ◽  
Mathematical Framework ◽  
Bayesian Decision ◽  
Perceptual Decision Making ◽  
Psychological Model ◽  
Psychological Reality ◽  
Psychological Theories ◽  
Target Article ◽  
Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

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