Task classification-aware data aggregation scheduling algorithm in wireless sensor networks

In order to minimise the delay of data aggregation scheduling, a task classification aware data aggregation scheduling algorithm is proposed. Through the multi-power and multi-channel approach of sensor nodes, maximum independent sets are used 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 approximating the greedy algorithm. Besides, combined with sparse coefficient, sensing task type reduces 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. Numerical results show that the proposed algorithm can reduce cluster heads data traffic and energy consumption, while shortening the data aggregation delay and enhancing the network survivability.