Title: Optimisation of delay tolerance in wireless sensor networks based on unscented Kalman filter estimation

Authors: Zhongli Shen; Guozhu Yao; Qiyue Xie; Fei Jiang

Addresses: School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410000, China ' School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan, 410000, China ' School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410000, China ' School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan, 410000, China

Abstract: Delay tolerance in wireless sensor network can be applied to complex and harsh communication environment. In order to improve the communication performance of delay tolerance in wireless sensor network, the optimisation of delay tolerance in wireless sensor network based on Unscented Kalman filter estimation is studied. Firstly, the model of delay tolerance in wireless sensor network is established according to the theory of dynamics and position constraints, and then an unscented Kalman filter estimation algorithm is used. Through analysing the basic state and measurement equation of delay tolerance in wireless sensor network model, the measurement equation of round-trip delay model is obtained. The pre handover time estimated by unscented Kalman filter is set, to effectively reduce the pre handover time and energy consumption, and optimise the delay wireless sensor network. The experimental results show that the delay tolerance of wireless sensor network optimised by the proposed method is within 30 ms under different observation noises, and the packet loss rate and energy consumption are low.

Keywords: unscented Kalman filter; estimation; wireless sensor; network; delay tolerance; optimisation research.

DOI: 10.1504/IJSNET.2020.107871

International Journal of Sensor Networks, 2020 Vol.33 No.2, pp.63 - 73

Received: 14 Dec 2019
Accepted: 18 Dec 2019

Published online: 20 Jun 2020 *

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