All one needs to know about fog computing and related edge computing paradigms: A complete survey

TL;DR: A description of the MEC reference architecture and proposed handover algorithms and techniques found in the literature is provided and challenges and gaps in the body of knowledge are discussed to provide guidance for future work.

TL;DR: Experimental results prove that the suggested adaptive resource allocation scheme enhances the performance of the emergency services in terms of end-to-end delay while keeping acceptable throughput for non-safety traffic in stressed situations.

TL;DR: In this paper, the cross-correlation problem is decomposed from a multilevel point of view, and an efficient GPU-parallel algorithm for multiple settings, including the edge computing one, is proposed.

TL;DR: In this paper , the authors present a model-based approach for the specification and execution of self-adaptive multi-layered IoT systems, which includes the design of a domain-specific language (DSL) for specification of such architectures and a runtime framework to support the system behaviuor and its selfadaptation at runtime.

TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.

TL;DR: The definition of edge computing is introduced, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge Computing.

TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).

TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.