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At the same time, we keep coefficients for a certain time period at the mobile edge sites in order to realize real time object detection, taking the advantage of low latency of the mobile edge computing ends.
Considering the unbalanced computation capacity between a mobile center cloud and mobile edge sites, we shift large storage burden into the cloud, while performing the dictionary learning by using compressive measurements in the mobile edge sites.
The convergence analysis shows that ACMES has the ability to solve complicated mobile edge cloud storage problems in reality.
This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing.
Our platform solves the Mobile Edge (ME) management issues with respect to Application (App) provisioning and traffic management.
Unfortunately, the commonly employed algorithms fail to be adaptive to the new storage pattern on mobile edge cloud.
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Distributed Mobility Management (DMM) helps going into this direction, by flattening the network, hence improving its scalability, and enabling local access to the Internet and other communication services, like mobile-edge clouds.
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