towards designing ai-aided lightweight solutions for key challenges in sensing, communication and computing layers of iot: smart health use-cases
abstract
the advent of the 5g and beyond 5g (b5g) communication system, along with the
proliferation of the internet of things (iot) and artificial intelligence (ai), have started to
evolve the vision of the smart world into a reality. similarly, the internet of medical things
(iomt) and ai have introduced numerous new dimensions towards attaining intelligent and
connected mobile health (mhealth). the demands of continuous remote health monitoring
with automated, lightweight, and secure systems have massively escalated. the ai-driven
iot/iomt can play an essential role in sufficing this demand, but there are several challenges in attaining it. we can look into these emerging hurdles in iot from three directions:
the sensing layer, the communication layer, and the computing layer. existing centralized
remote cloud-based ai analytics is not adequate for solving these challenges, and we need
to emphasize bringing the analytics into the ultra-edge iot. furthermore, from the communication perspective, the conventional techniques are not viable for the practical delivery of
health data in dynamic network conditions in 5g and b5g network systems. therefore, we
need to go beyond the traditional realm and press the need to incorporate lightweight ai
architecture to solve various challenges in the three mentioned iot planes, enhancing the
healthcare system in decision making and health data transmission.
in this thesis, we present different ai-enabled techniques to provide practical and lightweight
solutions to some selected challenges in the three iot planes.