任务类型感知的无线传感器网络数据融合调度算法

针对数据融合调度能量与时延优化问题，提出一种任务类型感知的无线传感网数据融合调度算法。通过传感器节点多功率、多信道的方式，利用最大独立集思想，构建基于数据融合主干树的网络拓扑结构，从而根据调度优先级，通过近似贪婪算法实现簇内数据融合调度，同时结合稀疏系数感知任务类型，减少传输数据量，进而利用簇头节点在网络中的等级，实现簇间数据融合调度。结果表明，所提算法在减少簇头节点数据传输量，降低节点能耗的同时，缩短了数据融合时延，提高了网络寿命。

Task Classification-aware Data Aggregation Scheduling Algorithm in Wireless Sensor Networks

In order to optimize the energy and delay of data aggregation scheduling algorithm, a task classification aware data aggregation scheduling algorithm is proposed. Through the multi-power and multi-channel approach of sensor nodes, we use the maximum independent sets to construct network topology structure based on data aggregation backbone tree. According to the scheduling priority, the data aggregation scheduling within clusters is achieved by utilizing the approximate greedy algorithm. Besides, combined with sparse coefficient, the sensing task types can be recognized to reduce the amount of data transmission, and then the level of cluster head nodes in the network is used to achieve data aggregation scheduling between clusters. The results show that the proposed algorithm can reduce the data transmission amount of the cluster head node and lower the node energy consumption, while decreasing the data aggregation delay and enhancing the network lifetime.