Path Segmentation and Optimization for Routing in Heterogeneous Full-/Half- Duplex CR-IoT Networks

The tremendous increase in the number of Internet of Things (IoT) devices is leading to severe scarcity in the available frequency spectrum. Therefore, smart techniques are needed to efficiently utilize the available frequency bands. One main technique for achieving spectral efficiency is cognitive radio (CR). By allowing unlicensed users to use the unused portions of the licensed frequency spectrum, CR significantly improves network throughput. Another technique is full-duplex (FD) communication, which achieves spectral efficiency by allowing concurrent transmission and reception over the same frequency channel. Interestingly, most of the existing routing schemes for multihop CR-IoT networks were designed assuming homogeneous setups, where all nodes are endowed with either half-duplex (HD) or FD radio capabilities. However, in reality, multihop CR-IoT networks are more likely to be heterogeneous, i.e., comprised of both HD and FD devices. Routing schemes designed for FD-homogeneous networks will not attain their intended optimal performance when deployed over heterogeneous networks, due to the existence of HD nodes. Similarly, routing schemes designed for HD-homogeneous networks will not take advantage of the FD capabilities available in heterogeneous networks, resulting in suboptimal performance. In this article, we propose the heterogeneity-aware path segmentation and optimization (PSO) routing scheme for heterogeneous multihop CR-IoT networks. For a given source–destination pair, PSO aims at selecting the path and determining the channel assignment that results in maximizing the overall end-to-end throughput, while taking into account the HD/FD heterogeneity. Simulation experiments reveal that the proposed PSO algorithm leads to improved system performance compared to existing algorithms that overlook heterogeneity.