SFTDA: Scalable Task Scheduling for Data Aggregation in Fault-tolerant Wireless Sensor Networks

摘要

This paper sheds light on the reliability assessment with scalable data aggregation in wireless sensor networks. In terms of the sensor nodes' Bayesian lifetime model, the fault ratio of sensing data among a pair of nodes in the worst case is derived for efficient data gathering. Leveraging the property of exponential aggregation latency distribution, the optimizing strategy, namely Scalable Fault-tolerant Data Aggregation (SFTDA), is proposed. Different from existing schemes, SFTDA not only optimizes data transmission cost, but also incorporates the function of data fusion, which is crucial for emerging sensor networks with data management and high availability requirements. Performance evaluations of the proposed approach with both sensor node faults and data aggregation policy are carried out. The results show that the adaptive timestamp estimation scheme can reflect the node status in saving the network throughput promptly, and this approach outperforms the earlier algorithms in light of latency designed without regard to sensor faults.