Abstract:
This thesis deals with improving the spectral and energy efficiency of Internet of Things (IoTs). It is expected that the physical devices connected to internet would grow immensely roughly 50 billion in 2020. Therefore, the network would become highly dense. For densely deployed IoTs, management of available spectrum would be a key challenge. Further, energy provisioning to these densely deployed networks will become a critical task. Therefore, in this thesis, we step forward to merge the cognitive radio for spectral efficiency and RF energy harvesting for energy efficiency to address both the spectral and energy challenges of IoT networks. We have focused to optimize the overall network capacity of IoTs using cognitive radio technology. Cognitive radio network has two types of users. i.e., primary (the licensed users) and secondary (the unlicensed users). In our case, we consider cellular devices as primary users and CR-based IoTs as secondary devices. We have used binary knapsack dynamic algorithm to pick up the nodes with highest energy levels to transmit data on an optimal channel. There are two operational modes (i.e. harvesting or transmission) for IoTs. Each IoT device can operate in one of the operational modes at a time. For validity of our proposed scheme, we have compared our proposed scheme with Greedy Algorithm (GA) and Random Selection Method (RSS) in terms of throughput, residual energy and network energy.
