Energy-Efficient k-Hop Clustering in Cognitive Radio Sensor Network for Internet of Things

The design and development of energy and spectrum-efficient solutions are important in the success of the Internet of Things (IoT). Due to the presence of an enormous number of smart devices, such as sensors, actuators, and different household devices achieving such scalable and efficient solutions are challenging. A wireless sensor network (WSN) with dynamic spectrum access (DSA) capability, known as the cognitive radio sensor network (CRSN) is recently introduced to deal with spectrum scarcity problem. Although the spectrum scarcity is reduced with DSA paradigm, the energy-efficient solutions are still required to be addressed due to the involvement of energy constrained devices in CSRN. Clustering is one of the efficient ways to optimize the energy consumption in the networks. Due to combination of both WSN and cognitive radio network (CRN), existing solutions of WSN and of CRNs are not applicable to CRSN. In this article, we propose a neighbor discovery algorithm and two greedy k-hop clustering schemes (k-SACB-WEC and k-SACB-EC) for CRSN with the aim focusing on IoT applications, which require constant intracluster and intercluster communications. We focus on achieving bichannel connectivity while maximizing network life. In our clustering different parameters, such as nodes' residual energy, spectrum awareness, appearance probability of primary users (PUs) of channels, channel quality, robustness on PUs' arrival, and the Euclidean distance between nodes are taken into consideration to select the hop count and common channels for clusters. Through simulation, we have highlighted the performance improvements of our proposed schemes in terms of the lifetime of the network, number of clusters, stability of networks, and frequency of reclustering over recently reported clustering algorithm in CRSN. The simulation results show that k-SACB-WEC generates at least 40% less number of clusters as compared to k-SACB-EC, network stability-aware clustering (NSAC), Prolong-SEP (PSEP), SAC-WCM, and Cognitive LEACH (CogLEACH). Also, in terms of network stability, the k-SACB-WEC achieves at least approximately 100% higher number of rounds before the first node dead than the compared competitive approaches.