  Edge intelligence refers to a set of connected systems and devices for data
collection, caching, processing, and analysis in locations close to where data
is captured based on artificial intelligence. The aim of edge intelligence is
to enhance the quality and speed of data processing and protect the privacy and
security of the data. Although recently emerged, spanning the period from 2011
to now, this field of research has shown explosive growth over the past five
years. In this paper, we present a thorough and comprehensive survey on the
literature surrounding edge intelligence. We first identify four fundamental
components of edge intelligence, namely edge caching, edge training, edge
inference, and edge offloading, based on theoretical and practical results
pertaining to proposed and deployed systems. We then aim for a systematic
classification of the state of the solutions by examining research results and
observations for each of the four components and present a taxonomy that
includes practical problems, adopted techniques, and application goals. For
each category, we elaborate, compare and analyse the literature from the
perspectives of adopted techniques, objectives, performance, advantages and
drawbacks, etc. This survey article provides a comprehensive introduction to
edge intelligence and its application areas. In addition, we summarise the
development of the emerging research field and the current state-of-the-art and
discuss the important open issues and possible theoretical and technical
solutions.
