Controlling interferences in smart building IoT networks using machine learning

Abstract

The coexistence of many internet of things (IoT) networks in smart buildings poses a major challenge because they interfere mutually. In most settings this results in a greedy approach where each IoT node optimises its own performance parameters like increasing transmit-power, etc. However, this means that interference levels are increased, battery powers are wasted, and spectrum resources are exhausted in high dense settings. To control interference levels, share spectrum resources, and lower the overall powerconsumptions this paper proposes a centralised control scheme which is based on a nonlinear cost function. This cost function is optimised by using machine learning in the form of a binary particle swarm optimisation (BPSO) algorithm. It has been found that this approach shares the spectrum in a fair way, it saves power and lowers the interference levels, and it dynamically adapts to network changes.