Title: Construction of building fire information monitoring model based on adaptive clustering scheduling

Authors: Ying Li; Lian Xue

Addresses: Architecture and Planning College, Hunan City University, Yiyang, 413000, China ' College of Information Engineering, Wuhan Technology and Business University, Wuhan, Hubei, 430065, China

Abstract: In order to solve the problem of unsatisfactory monitoring information transmission and large time overhead during conventional building fire monitoring, an optimisation method of building fire information monitoring based on adaptive clustering scheduling is proposed. In this method, a channel model for building fire information monitoring is constructed through the bi-directional link transmission control method, and then node deployment for building fire information monitoring is optimised through the shortest path optimisation method. The deployment of the largest coverage of fire information monitoring sensor nodes is designed through the self-adaptive rotation scheduling, and balance control of the output link layer of internet of things is performed through the adaptive clustering scheduling method to improve the accurate forwarding and real-time transmission capabilities of internet of things for fire detection information, and then a building fire information monitoring model is constructed. Experimental results show that the proposed method can effectively improve the success rate of fire information monitoring packet forwarding with an average increase of 24.7%, which greatly improves the monitoring information transmission efficiency, and it reduces the time overhead of fire information monitoring by 160s. The proposed method meets the actual needs and ensures the effectiveness of fire monitoring.

Keywords: building; fire information; monitoring model; path optimisation; node deployment.

DOI: 10.1504/IJIPT.2019.101358

International Journal of Internet Protocol Technology, 2019 Vol.12 No.3, pp.121 - 127

Received: 27 Jun 2018
Accepted: 19 Aug 2018
Published online: 05 Aug 2019 *

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