A Prediction-Based Fault-Tolerant Aggregation Algorithm in Wireless Sensor Networks

Data aggregation is a key technology in wireless sensor networks (WSNs), enabling WSNs to be widely used in many fields such as environment monitoring. It can not only reduce redundant data transmission during data gathering, but also effectively improve data accuracy. However, poor anti-interference ability, limited energy and other reasons existed in sensor nodes make data error-prone, thus affecting quality of the aggregation results. In this paper we propose a prediction-based fault-tolerant aggregation algorithm, which is implemented on a constructed aggregation tree. Using temporal-spatial correlation between data, the algorithm can distinguish different data types. And then we make a specific analysis of faulty data that may occur during data gathering and pointedly put forward different fault-tolerant solutions using the PSO-BPNN-based prediction model. Experimental results show that the proposed algorithm can effectively predict data and exclude the impact of faulty data, ensuring real-time and fault-tolerant requirements.