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Research paper | Open Access

An intelligence optimization method based on crowd intelligence for IoT devices

Ke Wang1, Zheming Yang1, Bing Liang1, Wen Ji2( )
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Abstract

Purpose

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

Design/methodology/approach

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Findings

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

Originality/value

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

Keywords

Genetic algorithmIoTCollaborative optimizationCrowd intelligence

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International Journal of Crowd Science
Volume 5 Issue 3,
November 2021
Pages 218-227
DOI: 10.1108/IJCS-03-2021-0007
Cite this article:
Wang K, Yang Z, Liang B, et al. An intelligence optimization method based on crowd intelligence for IoT devices. International Journal of Crowd Science, 2021, 5(3): 218-227. https://doi.org/10.1108/IJCS-03-2021-0007

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Received: 02 March 2021
Revised: 01 April 2021
Accepted: 04 April 2021
Published: 03 June 2021
© The author(s)

Ke Wang, Zheming Yang, Bing Liang and Wen Ji. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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