Cognitive radio sensor network (CRSN) is an intelligent and reasonable combination of cognitive radio and wireless sensor networks. Clustering is an effective method to manage the network topology of CRSN. In order to solve the optimal cluster head selection problem in clustering which is proved to be NP-hard, inspired by ions motion optimization (IMO) algorithm, a novel centralized clustering routing protocol, i.e., IMO-based clustering routing protocol (IMOCRP) which can adapt to the dynamic characteristics of CRSN is presented in this paper. Optimal number of clusters and identity of cluster heads are automatically determined by combining the excellent characteristics of IMO algorithm and the rich resource, comprehensive information at the sink together. Thanks to its centralized structure, IMOCRP proposed in this paper avoids excessive overhead in cluster head selection and cluster formation, which in turn helps conserve energy. Available channel list at each living node is taken into consideration to perform reasonable channel allocation for clusters, and the probability of collisions with primary users can be reduced, which promotes more successful information delivery. Simulation results have shown that IMOCRP can balance network lifetime and effective information collection capability, and it is superior to other competing protocols. INDEX TERMS Cognitive radio sensor network, clustering algorithms, ions motion optimization, computational complexity JIHONG WANG received the B.S. degree in communication engineering from Jilin University, Changchun, Jilin in 2011 and the PhD. degree in communication and information systems from School of Communication Engineering, Jilin University in 2016. She is currently an associate professor and master supervisor in Northeast Electric Power University. Her research interests include cognitive radio sensor networks, routing and resource allocation in wireless networks, energy harvesting and so on SHUO LI was born in Changchun, Jilin, China, in 1995. He obtained a bachelor's degree in communication engineering from Northeast Electric Power University, China, in 2018. In the same year, he studied for a master's degree at Northeast Electric Power University, China, and his research direction is clustering and routing in cognitive radio sensor network. YIYANG GE was born in Changchun, Jilin, China, in 1996. She received B.S. degrees in electronic information science and technology from Northeast Electric Power University, China, in 2019. Her research interests include cluster routing protocol design and resource allocation in cognitive radio wireless sensor networks with energy harvesting.

No abstract

No abstract

In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. In addition, for event-driven traffic, the long delay incurred by clustering and searching for available routes after events results in poor timeliness of information transmission. In order to solve above problems, a traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP) is proposed in this paper. For the first time, time-triggered and event-driven traffic can be served by a single clustering protocol. To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. In this case, time-triggered traffic can be periodically served. Priority-based schedule and corresponding frame structure are designed to ensure priority delivery of event-driven information. The clustering architecture built for time-triggered traffic is leveraged, and there is no cluster construction and route selection after emergent events. Only the CRSNs nodes which discover emergent events and corresponding CHs participate in data transmission, which means that TD-IMOCRP covers fewer nodes, especially when the sink is located at the corner. Therefore, it can help reduce node energy consumption and delay. Simulation results demonstrate that compared with representative event-driven clustering protocols, TD-IMOCRP can decrease the average number of covered nodes and the total energy consumption by more than 66.3% and 25%, respectively. In addition, when serving time-triggered traffic, TD-IMOCRP can achieve almost the same performance as its basic version IMOCRP which is better than majority of current time-triggered clustering protocols. In a word, TD-IMOCRP can guarantee in-time delivery of event-driven information while guaranteeing its performance of serving time-triggered traffic.