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Research paper | Open Access

An intelligence optimization method based on crowd intelligence for IoT devices

Ke Wang1Zheming Yang1Bing Liang1Wen 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.

References

 

Cai, C. (2017), “Analysis about NB-IoT low-rate narrowband IoT communication technology status and development trend”, Information Communications, No. 3, pp. 237-238.

 

Chen, Y., Li, M., Chen, P. and Xia, S. (2019), “Survey of cross-technology communication for IoT heterogeneous devices”,IET Communications, Vol. 13 No. 12, pp. 60-69.

 

Fortino, G., Messina, F., Rosaci, D. and Sarné, G. (2020), “Using blockchain in a reputation-based model for grouping agents in the internet of things”, IEEE Transactions on Engineering Management, Vol. 67 No. 4, pp. 1231-1243.

 
Gachet, D., Buenaga, M., Aparicio, F. and Padrón, V. (2012), “Integrating internet of things and cloud computing for health services provisioning. The virtual cloud carer project”, International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 918-921.https://doi.org/10.1109/IMIS.2012.25
 

Gai, K. and Qiu, M. (2018), “Optimal resource allocation using reinforcement learning for IoT content-centric services”,Applied Soft Computing, Vol. 70, pp. 12-21.

 

Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. (2013), “Internet of things (IoT): a vision, architectural elements, and future directions”, Future Generation Computer Systems, Vol. 29 No. 7, pp. 1645-1660.

 

He, D., Wang, F. and Jia, M. (2005), “Uniform design of initial population and operational parameters of genetic algorithm”, Journal of Northeastern University (NATURAL SCIENCE), Vol. 26 No. 9, pp. 828-831.

 

Ji, W., Liang, B., Wang, Y., Qiu, R. and Yang, Z. (2020), “Crowd V-IoE: visual internet of everything architecture in AI-driven fog computing”, IEEE Wireless Communications, Vol. 27 No. 2, pp. 51-57.

 
Khan, M., Alam, M., Moullec, Y. and Yaacoub, E. (2018), “Cooperative reinforcement learning for adaptive power allocation in device-to-device communication”, 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), pp. 476-481.https://doi.org/10.1109/WF-IoT.2018.8355169
 

Kotb, Y., Ridhawi, I.A., Aloqaily, M., Baker, T. and Tawfik, H. (2019), “Cloud-based multi-agent cooperation for IoT devices using workflow-nets”, Journal of Grid Computing, Vol. 17, pp. 625-650.

 

Li, H., Ota, K. and Dong, M. (2018), “Learning IoT in edge: deep learning for the internet of things with edge computing”, IEEE Network, Vol. 32 No. 1, pp. 96-101.

 

Li, Z., Pan, Z., Wang, X., Ji, W. and Yang, F. (2019), “Intelligence level analysis for crowd networks based on business entropy”, International Journal of Crowd Science, Vol. 3 No. 3, pp. 249-266.

 

Liu, J., Pan, Z., Xu, J., Liang, B., Chen, Y. and Ji, W. (2018), “Quality-time-complexity universal intelligence measurement”, International Journal of Crowd Science, Vol. 2 No. 2, pp. 99-107.

 
Liang, B., Pan, Z., Xu, J., Ji, W. and Chen, Y. (2018), “Quality-complexity-task universal intelligence measurement”, the 3rd International Conference, pp. 1-6.https://doi.org/10.1145/3265689.3265709
 

Pilloni, V., Atzori, L. and Mallus, M. (2017), “Dynamic involvement of real-world objects in the IoT: a consensus-based cooperation approach”, Sensors, Vol. 17 No. 3, p. 484.

 

Sheng, J., Hu, J., Teng, X., Wang, B. and Pan, X. (2019), “Computation offloading strategy in mobile edge computing”, Information (Switzerland), Vol. 10 No. 6, pp. 1-20.

 
Wang, J., Pan, J. and Flavio, E. (2017), “Elastic urban video surveillance system using edge computing”, The Workshop, pp. 1-6.https://doi.org/10.1145/3132479.3132490
 

Wang, W., He, S., Sun, L., Jiang, T. and Zhang, Q. (2018), “Cross-technology communications for heterogeneous IoT devices through artificial doppler shifts”, IEEE Transactions on Wireless Communications, Vol. 28 No. 8, pp. 34-43.

 

Wang, X., Pan, Z., Li, Z., Ji, W. and Yang, F. (2019), “Adaptive information sharing approach for crowd networks based on two stage optimization”, International Journal of Crowd Science, Vol. 3 No. 3, pp. 284-302.

 

Xu, H. (2018), “Characteristics of massive MIMO technology and applications in 5G scenario”, China New Telecommunications, Vol. 20 No. 16, pp. 75-76.

 

Yang, Z. and Ji, W. (2020), “Meta measurement of intelligence with crowd network”, International Journal of Crowd Science, Vol. 4 No. 3, pp. 295-307.

 

Zhang, H., Wang, Z. and He, X. (2020), “V2X offloading and resource allocation under SDN and MEC architecture”, Journal on Communications, Vol. 41 No. 1, pp. 114-124.

International Journal of Crowd Science
Pages 218-227
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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