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

TL;DR: This survey presents the up-to-date state-of-the-art of FC-BC integration with a detailed literature review and classification, and discusses and categorizes the related papers according to the year of publication, domain, used algorithms, BC roles, and the placement of the BC in the FC architecture.

TL;DR: This survey discusses IoT middleware requirements and challenges, and presents the current state of research in this domain, and a technical taxonomy is presented according to the abstract and processing approach of data.

TL;DR: In this article , the authors proposed a study for the state-of-the-art fog-based approaches in smart cities, which falls into three classes: service-based, resource-based and application-based.

TL;DR: A multi-agent reinforcement learning framework is developed to solve the game with the proposed Independent Learners based Multi-Agent Q-learning (IL-based MA-Q) algorithm, which has better system cost performance and achieves distributed computation offloading.

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.