Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information

2017 ◽  
Vol 35 (11) ◽  
pp. 2606-2615 ◽  
Author(s):  
Xinchen Lyu ◽  
Wei Ni ◽  
Hui Tian ◽  
Ren Ping Liu ◽  
Xin Wang ◽  
...  
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 101539-101549 ◽  
Author(s):  
Hao Wu ◽  
Hui Tian ◽  
Gaofeng Nie ◽  
Pengtao Zhao

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71859-71871
Author(s):  
Jianwei Liu ◽  
Xianglin Wei ◽  
Jianhua Fan

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Chuanxiu Chi ◽  
Yingjie Wang ◽  
Yingshu Li ◽  
Xiangrong Tong

With the advent of the Internet of Things (IoT) era, various application requirements have put forward higher requirements for data transmission bandwidth and real-time data processing. Mobile edge computing (MEC) can greatly alleviate the pressure on network bandwidth and improve the response speed by effectively using the device resources of mobile edge. Research on mobile crowdsourcing in edge computing has become a hot spot. Hence, we studied resource utilization issues between edge mobile devices, namely, crowdsourcing scenarios in mobile edge computing. We aimed to design an incentive mechanism to ensure the long-term participation of users and high quality of tasks. This paper designs a long-term incentive mechanism based on game theory. The long-term incentive mechanism is to encourage participants to provide long-term and continuous quality data for mobile crowdsourcing systems. The multistrategy repeated game-based incentive mechanism (MSRG incentive mechanism) is proposed to guide participants to provide long-term participation and high-quality data. The proposed mechanism regards the interaction between the worker and the requester as a repeated game and obtains a long-term incentive based on the historical information and discount factor. In addition, the evolutionary game theory and the Wright-Fisher model in biology are used to analyze the evolution of participants’ strategies. The optimal discount factor is found within the range of discount factors based on repeated games. Finally, simulation experiments verify the existing crowdsourcing dilemma and the effectiveness of the incentive mechanism. The results show that the proposed MSRG incentive mechanism has a long-term incentive effect for participants in mobile crowdsourcing systems.


Author(s):  
Atiqur Rahman ◽  
Guangfu Wu ◽  
Ali Md Liton

Nowadays, the masonry for environment-friendly and protected network structure designs, for example, the Internet of Things and gigantic data analytics are increasing at a faster pace compared to an earlier state. Mobile edge computing for an Internet of Things widget is information processing that is achieved at or close to the collectors of information in an Internet of Things system. Herein, we are proposing to temporarily evaluation the concepts, features, protection, and privacy applications of Internet of Things authorized mobile edge computing with its data protection view in our data-driven globe. We focus on illuminating one of kind components that need to be taken into consideration whilst creating a scalable, consistent, impenetrable and disseminated mobile edge computing structure. We also sum up the fundamental ideas regarding security threat alleviation strategies. After that, we walk around the existing challenges and opportunities in the area of mobile edge computing. In conclusion, we analyze a case study, in which a security protection mechanism can be hardened to lift out everyday jobs.


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