基于G-Means的中医药可穿戴设备的数据汇聚方法研究

The Research of the G-Means Based Data Transmission Method for the Wearable Devices for Traditional Chinese Medicine

目的主要研究高效的数据汇聚方法,以减少中医药可穿戴设备节点的能量消耗,并延长中医药可穿戴设备网络的生存周期。方法通过G-Means算法,按照中医可穿戴设备节点的地理位置,将节点划分为多个服从高斯分布的簇集。根据节点的剩余能量与地理位置为所有簇集内可穿戴设备节点分配权重,所计算的权重值为每个簇集选取合适的簇头节点。网络内所有节点数据经簇头节点收集并汇聚至中医医生观测平台。结果文中的方法在场景1与场景2中所有节点在网络前100轮的平均能耗分别为0.0262 J与0.0555 J,比同类型方法均降低10%以上。文中所提方法在场景1与场景2下首个节点的失效时间为910轮与849轮,比同类型方法均延长10%以上。结论高效中医药可穿戴设备的数据汇聚方法优化中医药可穿戴设备网络的拓扑结构,提高设备节点的能效性,并延长网络的生存周期。

Objective Research an efficient data gathering method to reduce the energy consumption of the Chinese medicine wearable devices and prolong the life-cycle of the Chinese medicine wearable device networks.Methods This study intends to adopt G-Means method to divide the devices into multiple clusters through the positions of the devices while the devices obey a Gaussian distribution.Then the study intends to assign the weights for all the devices of the clusters based on the residual energy and the positions,and use the weights to select appropriate cluster heads for each cluster.The selected cluster heads collect the data from the devices of the corresponding cluster and transmit the data to the sink node.Results The average energy consumption for all nodes of the first 100 rounds in scenario 1 and scenario 2 is 0.0262 J and 0.0555 J,respectively,which is more than 10%lower than the same type of methods.The failure time of the first node in scenario 1 and scenario 2 of the proposed method is the 910 thround and the 849 thround,which is more than 10%longer than the same type of methods.Conclusion The proposed efficient data gathering method for the Chinese medicine wearable devices can optimize the topology of the Chinese medicine wearable device networks,enhance the energy efficiency of the devices and extend the network life-cycle.