Optimized node classification and channel pairing scheme for RF energy harvesting based cognitive radio sensor networks

Spectral-efficiency and energy-efficiency are the key concerns for next-generation wireless networks. RF energy-harvesting is emerged as a prominent technology which self-empowers wireless nodes to achieve energy-efficiency. For spectral-efficiency, the opportunistic spectrum-access technology (by employing cognitive radios) is an optimal solution. Therefore, in this paper, we merge both technologies (cognitive radio & RF energy harvesting) together to achieve network-wide spectral and energy efficiency. A novel two-level residual-energy and channel-quality (capacity and idle-time) aware node-classification scheme is introduced for cluster-based cognitive radio sensor networks to select the best sensor nodes for reporting process. At first level, the nodes are classified as harvesting or transmitting nodes based on their residual energy. Later on, the best node-channel pairs are formed for transmitting nodes using Hungarian algorithm. In the second level of classification, only those nodes are selected for reporting, which can transmit reporting packet in the given duration on the allocated channel. Otherwise, the node is directed to perform energy harvesting task to achieve energy-balancing and avoid unsuccessful reporting. Simulation results demonstrate that the proposed scheme shows better performance gain in terms of successful reporting rate compared to existing node-classification schemes. Furthermore, we compare the proposed node-channel pairing scheme with greedy-pairing and random-pairing schemes and illustrate the performance gain in terms of successful reporting.