  A novel approach is presented in this work for context-aware connectivity and
processing optimization of Internet of things (IoT) networks. Different from
the state-of-the-art approaches, the proposed approach simultaneously selects
the best connectivity and processing unit (e.g., device, fog, and cloud) along
with the percentage of data to be offloaded by jointly optimizing energy
consumption, response-time, security, and monetary cost. The proposed scheme
employs a reinforcement learning algorithm, and manages to achieve significant
gains compared to deterministic solutions. In particular, the requirements of
IoT devices in terms of response-time and security are taken as inputs along
with the remaining battery level of the devices, and the developed algorithm
returns an optimized policy. The results obtained show that only our method is
able to meet the holistic multi-objective optimisation criteria, albeit, the
benchmark approaches may achieve better results on a particular metric at the
cost of failing to reach the other targets. Thus, the proposed approach is a
device-centric and context-aware solution that accounts for the monetary and
battery constraints.
