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5G application offloading system based on edge computing of service requirements migrates to edge computing servers, access to the rest of the services is provided

Pushing intelligence to the edge with a stream processing architecture
© 2017 IEEE. The cloud computing paradigm underpins the Internet of Things (IoT) by offering a

A Machine Learning-Based Intelligent Vehicular System (IVS) for Driver’s Diabetes Monitoring in Vehicular Ad-Hoc Networks (VANETs)) sensors, in combination with machine learning approaches at cloud servers, can be beneficial

System model for multi-level cloud based tactile internet system such latency is to build the system based on the multilevel cloud architecture and the 5G network structure

Session-Level Reliability Analysis for Multi-Service Communication in Autonomous Vehicular Fleets-)allocation across multiple competing data streams in autonomous vehicular fleets equipped with 5G cellular

Energy - Aware Offloading Algorithm for Multi-level Cloud Based 5G System clouds. Cellular networks are able to offer the cloud computing capabilities at the edge of the radio

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms
the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a

Distributed edge computing to assist ultra-low-latency VANET applications edge computing (MEC) to solve the problem of high traffic density and provides and offloading path

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

About data processing scenarios for Mobile Edge Computing bandwidth, low latency and delay lacks during data transfer process. Edge Computing is the underlying

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