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Resource allocation scheme based on gray wolf optimization algorithm in mobile edge computingAiming at the problem of server resource allocation in Mobile Edge Computing (MEC)
task migration
Energy efficient computation offloading mechanism in multi-server mobile edge computing—an integer linear optimization approach) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling
Energy - Aware Offloading Algorithm for Multi-level Cloud Based 5G SystemMobile edge computing (MEC) is a recent communication paradigm developed mainly for cellular
Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study
About data processing scenarios for Mobile Edge Computing-driving vehicles and many other areas. One of the latest implementations of Edge Computing is Mobile Edge Computing
Multi-level Architecture for P2P Services in Mobile Networks it was proposed to replace the standard centralized architecture of mobile applications by the mobile edge
Personalized Health Tracking with Edge Computing Technologies, such as Internet of Things (IoT), Cloud, and Edge computing, could address. The solution proposed in this paper
Mobile Edge Computing for Video Application Migration with the appropriate quality of experience. MEC or Mobile edge computing offers significant advantages for example
Dd-fog: Intelligent distributed dynamic fog computing framework and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More
Multi-Agent Systems for Collaborative Inference Based on Deep Policy Q-Inference Network it results in faster and more energy-efficient inference, as computation can be offloaded to edge servers
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