Title: A new IoT resource addressing method based on rough set neural network

Authors: Liang Jin; Wei Li; Qinghui Meng

Addresses: Department of Mechanical and Electrical Automation, Henan Polytechnic Institute, Nanyang, Henan Province, 473000, China ' School of Electronic Information Engineering, Henan Polytechnic Institute, Nanyang, Henan Province, 473000, China ' Department of Mechanical and Electrical Automation, Henan Polytechnic Institute, Nanyang, Henan Province, 473000, China

Abstract: In order to overcome the problems of low precision and long time in traditional resource addressing methods of Internet of Things (IoT), this paper proposes a new resource addressing method based on rough set neural network, which enhances the information processing ability of rough set neural network and establishes a new decision system modelling method. The resource names are refined into original resource names and transformed resource names through the model. At the same time, the resource addresses are expanded into resource address information with standard hierarchical structure information and expanded hierarchical structure information, which provides important information for the transformation of resource names and realises the IoT resources addressing. The experimental results show that the proposed method can effectively improve the addressing accuracy and reduce the average search time, and the node failure problem is significantly improved.

Keywords: rough set neural network; IoT; Internet of Things; resource addressing; simulation; standard hierarchical structure information; expanded hierarchical structure information.

DOI: 10.1504/IJAACS.2023.129643

International Journal of Autonomous and Adaptive Communications Systems, 2023 Vol.16 No.1, pp.112 - 125

Received: 11 May 2020
Accepted: 01 Sep 2020
Published online: 17 Mar 2023 *