Edge intelligence secure frameworks: Current state and future challenges

Abstract

At the confluence of two great paradigms such as Edge Computing and Artificial Intelligence, Edge Intelligence arises. This new concept is about the smart exploitation of Edge Computing by bringing together reasoning and learning by Artificial Intelligence algorithms and the sensors/actuators computing capabilities. Security is the third paradigm that must join the team in order to have resilient and reliable systems to be used in real-world applications and use cases. Hence, smartness is, in this context, a puzzle of several independent pieces which, once fitted, can derived unprecedented benefits: a) security, b) low communication latency and network load, c) cost and energy saving and d) scalability by means of resource virtualization close to the IoT data generators (IoT devices). In fact, by paying exclusive attention to some of those main pillars and, therefore, disregarding others, edge computation once in operation often suffers from bad performance, unforeseen events or does not exploit the enormous potential that should be unlocked if a proper and complete specification had been laid down. With all this in mind, this work provides a technical review of the available and up-to-date frameworks to implement secure Edge Intelligence, pinpoints the most relevant unfilled gaps (strengths and weaknesses) and, last but nos least, includes challenges and future research lines as a result of our exploration.