Title :
Prediction revision strategies for data transmission in wireless sensor networks
Author :
Yang, Jun ; Wang, Yi ; Zhang, Deyun
Author_Institution :
Sch. of Electron. & Inf. Eng., Xian Jiaotong Univ., Xian
Abstract :
In this paper, a novel strategy for data transmission that is based on prediction revision dynamic adjustment data gathering algorithm (PRDA) is proposed in WSNs. The key idea of the PRDA is to separate the data prediction and model computing, and the autoregressive process model is employed for prediction revision algorithm. The model computing of PRDA is conducted by sink node firstly according to sampling data sequence from sensor nodes, then, sink node sends the parameters of model to sensor node. Each sensor node predicts the values of the data with parameters, and then, determines whether the current sampling data are sent out or not according to the comparison results of predicting data and sampling data. The dynamic adjustment mechanism of model computing is used to fit the variety of sampling data. Simulation results show that PRDA is able to reduce the amount of data transmission and lead to more significantly energy saving than the traditional approach.
Keywords :
autoregressive processes; data communication; wireless sensor networks; autoregressive process model; current sampling data; data gathering algorithm; data prediction; data transmission; energy saving; model computing; prediction revision dynamic adjustment; sampling data sequence; wireless sensor networks; Algorithm design and analysis; Data communication; Data engineering; Energy consumption; Mathematics; Military computing; Prediction algorithms; Predictive models; Sampling methods; Wireless sensor networks;
Conference_Titel :
Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-2357-6
Electronic_ISBN :
978-1-4244-2358-3
DOI :
10.1109/CIT.2008.4594781
