Jin, M.H., Wu, W.J., Chen, C.K., Chen, Y.F., Wen, C.M., Kao, C.Y., Yu, S.A., Lin, Y.H., Huang, J.G., Rao, H. and Hsu, C.H. (2004) Hierarchical Sensor Network Architecture for Stationary Smart Node Supervision. Proceedings of SPIE, Smart Structures and Materials 2004: Smart Electronics, MEMS, BioMEMS, and Nanotechnology, 5389, 11 p.

has been cited by the following article:

• TITLE: Data Prediction in Distributed Sensor Networks Using Adam Bashforth Moulton Method

  AUTHORS: Md Monirul Islam, Zabir Al Nazi, A. B. M. Aowlad Hossain, Md Masud Rana

  KEYWORDS: Adams-Bashforth-Moulton Method, Energy Saving, Milne-Simpson Method, Wireless Sensor Networks

  JOURNAL NAME: Journal of Sensor Technology, Vol.8 No.2, June 21, 2018

  ABSTRACT: Information collection from remote location is very important for several tasks such as temperate monitoring, air quality investigation, and wartime surveillance. Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction threshold reduces to a small value. Our proposed Adams-Bashforth-Moulton algorithm to overcome this drawback was compared with the Milne Simpson scheme. The proposed algorithm is simulated on distributed sensor nodes where information is gathered from the Intel Berkeley Research Laboratory. To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.