用于作物表型信息边缘计算采集的认知无线传感器网络分簇路由算法

随着无线终端数量的快速增长和多媒体图像等高带宽传输业务需求的增加，农业物联网相关领域可预见地会出现无线频谱资源紧缺问题。针对基于传统物联网的作物表型信息采集系统中存在由于节点密集部署导致数据传输过程容易出现频谱竞争、数据拥堵的现象以及固定电池的网络由于能耗不均衡引起监测周期缩减等诸多问题，本研究建立了一个认知无线传感器网络（CRSN）作物表型信息采集模型，并针对模型提出一种引入边缘计算机制的动态频谱和能耗均衡（DSEB）的事件驱动分簇路由算法。算法包括：（1）动态频谱感知分簇，采用层次聚类算法结合频谱感知获取的可用信道、节点间的距离、剩余能量和邻居节点度为相似度对被监控区域内的节点进行聚类分簇并选取簇头，构建分簇拓扑的过程对各分簇大小的均衡性引入奖励和惩罚因子，提升网络各分簇平均频谱利用率；（2）融入边缘计算的事件触发数据路由，根据构建的分簇拓扑结构，将待检测各区域变化异常表型信息触发事件以簇内汇聚和簇间中继交替迭代方式转发至汇聚节点，簇内汇聚包括直传和簇内中继，簇间中继包括主网关节点和次网关节点-主网关节点两种情况；（3）基于频谱变化和通信服务质量（QoS）的自适应重新分簇：基于主用户行为变化引起的可用信道改变，或分簇效果不佳对通信服务质量产生的干扰，触发CRSN进行自适应重新分簇。此外，本研究还提出了一种新的能耗均衡策略去能量消耗中心化（假设sink为中心），即在网关或簇头节点选取计算式中引入与节点到sink的距离成正比的权重系数。算法仿真结果表明，与采用K-medoid分簇和能量感知的事件驱动分簇(ERP)路由方案相比，在CRSN节点数为定值的前提下，基于DSEB的分簇路由算法在网络生存期与能效等方面均具有一定的改进；在主用户节点数为定值时，所提算法比其它两种算法具有更高频谱利用率。

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

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.