No abstract

As the number of devices connected to the internet has grown larger, so too has the intensity of the tasks that these devices need to perform. Modern networks are more frequently working to perform computationally intensive tasks on low-power devices and low-end hardware. Current architectures and platforms tend towards centralized and resource rich cloud computing approaches to address these deficits. However, edge computing presents a much more viable and flexible alternative. Edge computing refers to a distributed and decentralized network architecture in which demanding tasks such as image recognition, smart city services, and high-intensity data processing tasks can be distributed over a number of integrated network devices. In this paper, we provide a comprehensive survey for emerging edge intelligence applications, lightweight machine learning algorithms, and their support for future applications and services. We started by analyzing the rise of cloud computing discuss its weak points, and identify situations in which edge computing provides advantages over traditional cloud computing architectures. We then divulge into the survey - the first section identifying opportunities and domains for edge computing growth, the second identifying algorithms and approaches that can be used to enhance edge intelligence implementations, and the third specifically analyzing situations in which edge intelligence can be enhanced using any of the aforementioned algorithms or approaches. In this third section, lightweight machine learning approaches are detailed. A more in-depth analysis and discussion of future developments follows. The primary discourse of this piece is in an effort to ensure that appropriate approaches are applied adequately to artificial intelligence implementations in edge systems and mainly the lightweight machine learning approaches.