A trust evaluation system based on reputation data in Mobile edge computing network

2020 ◽  
Vol 13 (5) ◽  
pp. 1744-1755
Author(s):  
Xiaoheng Deng ◽  
Jin Liu ◽  
Leilei Wang ◽  
Zhihui Zhao
Keyword(s):  
Evaluation System ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Trust Evaluation

scholarly journals A trust evaluation scheme of service providers in mobile edge computing

2022 ◽  
Vol 12 (2) ◽  
pp. 2121
Author(s):  
Merrihan Badr Monir Mansour ◽  
Tamer Abdelkader ◽  
Mohammed Hashem AbdelAziz ◽  
El-Sayed Mohamed EI-Horbaty
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Edge Computing ◽  
Cloud Services ◽  
Service Users ◽  
Mobile Edge Computing ◽  
Trust Evaluation ◽  
Computing Paradigm ◽  
Performance Quality ◽  
Evaluation Scheme

Mobile edge computing (MEC) is a new computing paradigm that brings cloud services to the network edge. Despite its great need in terms of computational services in daily life, service users may have several concerns while selecting a suitable service provider to fulfil their computational requirements. Such concerns are: with whom they are dealing with, where will their private data migrate to, service provider processing performance quality. Therefore, this paper presents a trust evaluation scheme that evaluates the processing performance of a service provider in the MEC environment. Processing performance of service providers is evaluated in terms of average processing success rate and processing throughput, thus allocating a service provider in a relevant trust status. Service provider processing incompliance and user termination ratio are also computed during provider’s interactions with users. This is in an attempt to help future service users to be acknowledged of service provider’s past interactions prior dealing with it. Thus, eliminating the probability of existing compromised service providers and raising the security and success of future interactions between service providers and users. Simulations results show service providers processing performance degree, processing incompliance and user termination ratio. A service provider is allocated to a trust status according to the evaluated processing performance trust degree.

Load Balancing for Energy-Harvesting Mobile Edge Computing

2021 ◽  
Vol E104.A (1) ◽  
pp. 336-342
Author(s):  
Ping ZHAO ◽  
Jiawei TAO ◽  
Abdul RAUF ◽  
Fengde JIA ◽  
Longting XU
Keyword(s):  
Energy Harvesting ◽  
Load Balancing ◽  
Edge Computing ◽  
Mobile Edge Computing

Mobile Edge Computing Review and Analysis

2019 ◽  
Vol 7 (1) ◽  
pp. 525-532
Author(s):  
Komal Kharbanda ◽  
Kiranbir Kaur
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing

Computation Offloading Game in Multiple UAV-Enabled Mobile Edge Computing Networks

2020 ◽  
Author(s):  
Yanling Ren ◽  
Zhibin Xie ◽  
Zhenfeng Ding ◽  
xiyuan sun ◽  
Jie Xia ◽  
...  
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing

Communication-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

2020 ◽  
pp. 1-1
Author(s):  
Ping Zhou ◽  
Ke Shen ◽  
Neeraj Kumar ◽  
Yin Zhang ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Heterogeneous Networks ◽  
Edge Computing ◽  
Mobile Edge Computing

Context-Aware Multi-QoS Prediction for Services in Mobile Edge Computing

2019 ◽  
Author(s):  
Zhizhong Liu ◽  
Quan Z. Sheng ◽  
Wei Emma Zhang ◽  
Dianhui Chu ◽  
Xiaofei Xu
Keyword(s):  
Edge Computing ◽  
Context Aware ◽  
Mobile Edge Computing ◽  
Qos Prediction

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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