Title: A radio link reliability prediction model for wireless sensor networks

Authors: Wei Sun; Qiyue Li; Jianping Wang; Liangfeng Chen; Daoming Mu; Xiaojing Yuan

Addresses: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China ' School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China ' School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China ' Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China ' School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China ' College of Technology, University of Houston, Houston, TX 77004, USA

Abstract: The wireless sensor network (WSN) has great prospects in monitoring and control of industrial plants and devices. One of the main challenges of developing WSNs for industrial applications is to satisfy their requirements for reliability with strict bounds. Accurate prediction of a radio link's reliability is helpful for upper layer protocols to optimise network level quality of service (QoS) performance. In this paper, to predict the bounds of the confidence interval of the packet reception ratio (PRR), a radio link reliability prediction (RLRP) model is proposed for describing the relation between the reliability metric's bounds and factors that affect it. Based on the RLRP model, the adaptive extended Kalman filter algorithm is adopted to predict the reliability metric. Real-world experiments were performed to demonstrate the proposed model. The results indicate that our RLRP model is accurate in predicting the bounds of link reliability, can better reflect the random characteristic of the radio link, and is more sensitive to dynamic changes of the radio link.

Keywords: link reliability model; reliability prediction; wireless sensor network; WSN; Kalman filter.

DOI: 10.1504/IJSNET.2018.093960

International Journal of Sensor Networks, 2018 Vol.27 No.4, pp.215 - 226

Received: 12 Jul 2016
Accepted: 05 Apr 2017
Published online: 10 Aug 2018 *

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