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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (2): 28-47.doi: 10.12133/j.smartag.2020.2.2.201909-SA005

• Topic--Agricultural Sensor and Internet of Things • Previous Articles     Next Articles

Cognitive Radio Sensor Networks Clustering Routing Algorithm for Crop Phenotypic Information Edge Computing Collection

WANG Jinhong1,2, HAN Yuxing1,2()   

  1. 1.College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
    2.Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou 510642, China
  • Received:2019-09-26 Revised:2020-02-06 Online:2020-06-30


With the rapid growth of wireless nodes numbers and the increase in demanding for high-bandwidth transmission services such as multimedia images, the related fields of the agricultural Internet of Things(IoT) can foresee a trend of shortage of wireless spectrum resources. For the crop phenotypic information collection system based on the traditional IoT, there are many problems such as spectrum competition, data congestion during the data transmission process due to the dense deployment of nodes, and the reduction of the monitoring cycle due to uneven energy consumption in the fixed battery network. Based on previous studies, a crop phenotypic information collection model for cognitive radio sensor networks was established, and based on the model, an event-driven clustering routing algorithm that introduced dynamic spectrum and energy balance (DSEB) of edge computer system was proposed. The algorithm includes dynamic spectrum sensing clustering. The hierarchical clustering algorithm was used to combine the available channels, distances between nodes, residual energy, and neighbor node degrees obtained by spectrum sensing as similarities to cluster and cluster nodes in the monitored area and select cluster heads. The process of clustering and selecting cluster heads and constructing a clustering topology introduceed rewards and punishment factors to the equilibrium of the clustering sizes to improve the average spectrum utilization of each clustering network. The events triggered by edge computing trigger data routing, and based on the clustered topology structure, the events triggered by abnormal changes in farm conditions in the areas to be detected on the farm were forwarded to the convergent nodes by means of alternate cluster iterations and inter-cluster relays. Convergence includes direct transmission and intra-cluster relay, and inter-cluster relay includes two cases: ①primary gateway node and secondary gateway node-primary gateway node; ②adaptive re-clustering based on spectrum changes and communication quality of service (QoS)-changes in available channels caused by changes in the PU behavior of the primary user, or interference with poor quality of clustering effects on communication service quality, triggering cognitive radio sensor networks to perform adaptive re-clustering. In addition, a new energy balancing strategy was proposed to decentralize energy consumption (assuming sink is the center), that is, introducing a weight coefficient proportional to the distance from the node to the sink in the gateway or cluster head node selection calculation formula. The simulation results of the algorithm showed that, compared with the event-driven clustering ERP routing scheme using K-medoid clustering and energy sensing, under the premise that the number of CRSN nodes is a fixed value, the clustering routing algorithm based on DSEB in the network lifetime and there are certain improvements in utilization and energy efficiency; when the number of primary user nodes is a fixed value, the proposed algorithm has higher spectrum utilization than the other two algorithms.

Key words: cognitive radio sensor network (CRSN), crop phenotype information collection, energy balance, cluster routing

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