Implementation of Collaborative RF Localization Using a Software-Defined Radio Network

This thesis investigates the use of collaboration between sensor nodes that were tasked with localizing a radio frequency emitter. Localization is a necessary component for dynamic spectrum access. Using a set of software-defined radios as our sensors and a received signal strength-based maximum likelihood localization algorithm, we successfully localized transmitting nodes based on their received signal strength. Our experiment was conducted outdoors using a flexible topology that could be shaped into 21 sub-topologies that varied in size, and orientation with respect to the transmitters. This was made possible through application of a time shift concept and a post-processing technique. We were able to compare our real world results with the simulated results of the same topologies. Although our simulation results did not fully comply with our real world results, we observed some common trends regarding effective topology design

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing