A New Data Processing Algorithm based on Model Fitting for Wireless Sensor Networks
Guimin Huang, Yuanyuan Chen, Ya Zhou, Pingshan Liu, Siyun Liu · Procedia Computer Science · 2013
Wireless sensor networks (WSNs) are data-centric networks. In recent years, with the continuous development of WSNs technology, the characteristics of node storage capacity-constrained and node energy resource-constrained are prominent in WSNs technology. How to effectively compress data is an important issue in WSNs. Especially, the issue becomes a more challenging problem when the data monitored by nodes have no spatial and temporal correlation or stable correlation. To settle the above problem, this paper proposes a new data processing algorithm based on B-spline fitting model, called BFM algorithm. The BFM algorithm can compress data effectively, and thus reduce the amount of data transmitted. This can save the energy consumption of WSNs and prolong the lifetime of WSNs. Through experiments, the BFM algorithm is compared with the Tiny DB model. The results of the experiments show the BFM algorithm outperforms the Tiny DB model in terms of data transfer ratio.