Channel Switching Cost-Aware Energy Efficient Routing in Cognitive Radio-Enabled Internet of Things

Abstract

Routing is the process of determining the data transmission path from the source to the destination considering restrictions. Optimizing the transmission performance of acquired data, in particular, emphasizes the importance of routing in today's Internet of Things (IoT) network technology. The major topic of the research is that typical IoT network devices, which can only communicate in existing frequency bands, may communicate in various frequency bands by utilizing the features of Cognitive Radio Networks (CRN). In the present scenario, existing IoT devices containing Cognitive Radio (CR) capabilities such as spectrum sensing, spectrum management, spectrum sharing, and mobility, were utilized and identified as Cognitive Radio-Enabled Internet of Things (CR-IoT) devices. This research investigated the routing path selection problem in CR-IoT networks under the assumption that there are customized CR-IoT devices with a single transceiver in the network. As a routing scenario, the aim was to send data through CR-IoT devices while considering frequency switching, energy consumption, and energy efficiency. In this context, a frequency switching model was designed and modelled energy usage over time intervals based on switching. A greedy algorithm was proposed called the Cognitive Radio Enabled Greedy Routing Protocol for Low-power and Lossy Networks (CR-GreedyRPL), which is a customized form of the IPv6 routing protocol of Routing Protocol for Low-power and Lossy Networks (RPL). We formulate the routing problem as energy efficiency maximization problem. Link capacity maximization and energy consumption minimization were ensured considering channel switching latency. This proposed algorithm performs using Objective Functions (OFs) during parent selection and finding optimal path. The effects of signal-to-noise ratio (SNR), number of frequencies, time slot frame and switching delay of a unit frequency were investigated. Additionally, we conduct simulations to evaluate the performance of our proposed CR-GreedyRPL algorithm via comparison with the optimal solutions obtained by implementing our ILP formulation using optimization software CPLEX as well as via comparison with the Widest Path Routing (WPR), RPL, and Switching Aware RPL (RPL-Sw) in terms of energy efficiency. Simulation results demonstrate that our proposed CR-GreedyRPL algorithm yields close results to the CPLEX solutions which can be regarded as an upper bound.