Abstract:
With the prediction of thousands of IoT devices within a single cell in the future, it is the need of
the hour to come up with solutions to mitigate the expected problems among these devices. The
competition to acquire up and downlink channels for operations would become intense for these
large numbers of devices as 3GPP approved NB-IoT bandwidth for long-range communication is
only 200khz with a subcarrier spacing of 15khz. In addition, the competition would also contribute
to the problems of lower data rate, decrease devices battery lives and disrupt continuity for devices
that require continuous communication. The thesis study focuses on introducing device to device
connection among IoT devices which would help save the battery life of the devices. The new
connections made among the devices will utilize lower power as the algorithm designed focuses
on ideal potential pairing devices with least bit error rate, least distance, and lowest transmission
power. The results show the supremacy of the proposed solution which can be used to reduce the
interference among device to device IoT network in addition, to the power saving capability.
Deployment of the proposed cognitive power-based device to device pairing solution can help
reduce the power consumed by the IoT network which can be put in the network security
algorithms to make the IoT network communication more secure. Moreover, machine learning is
deployed to further speed up the device to device link formation between IoT devices after the
initial set of training data is produced and processed at the operator end.
