A Comprehensive Survey on Architecture for Big Data Processing in Mobile Edge Computing Environments
Maninder Kaur · EAI/Springer Innovations in Communication and Computing · 2018
With the exponential growth of smartphones, the growth of mobile traffic has also increased dramatically. With this, there has been also increase in the data involved – which is big data. A large part of big data is most valuable when it is analyzed quickly as it is generated. There is a need for processing continuous data streams under very short delays. Recently, frameworks and architectures have been proposed for carrying out data stream processing at the edge of the network using constrained resources. This chapter aims to present a comprehensive survey of the framework, architecture, and applications areas in the area of mobile edge computing. It also discusses some of the challenges and related existing solutions as well. It also provides a survey of the state-of-the-art mobile edge computing research with the focus on deep learning as a technique used for reliable and secure deployment of MEC.