Review

. 2023 Jan 28;23(3):1466.

doi: 10.3390/s23031466.

Swarm Intelligence in Internet of Medical Things: A Review

Affiliations

¹ Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia.
² Department of Computer Engineering, Faculty of Engineering, Fasa University, Vali asr Blvd, Fasa 74617-81189, Iran.
³ Department of Electrical and Computer Engineering, Isfahan University of Technology, Daneshgah e Sanati Hwy, Isfahan 84156-83111, Iran.
⁴ Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy.
⁵ BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia.
⁶ UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia.
⁷ Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, NSW 2109, Australia.
⁸ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
⁹ Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore.
¹⁰ Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.
¹¹ Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA.

PMID: 36772503
PMCID: PMC9920579
DOI: 10.3390/s23031466

Review

Swarm Intelligence in Internet of Medical Things: A Review

Roohallah Alizadehsani et al. Sensors (Basel). 2023.

. 2023 Jan 28;23(3):1466.

doi: 10.3390/s23031466.

Authors

Affiliations

¹ Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia.
² Department of Computer Engineering, Faculty of Engineering, Fasa University, Vali asr Blvd, Fasa 74617-81189, Iran.
³ Department of Electrical and Computer Engineering, Isfahan University of Technology, Daneshgah e Sanati Hwy, Isfahan 84156-83111, Iran.
⁴ Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy.
⁵ BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia.
⁶ UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia.
⁷ Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, NSW 2109, Australia.
⁸ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
⁹ Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore.
¹⁰ Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.
¹¹ Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA.

PMID: 36772503
PMCID: PMC9920579
DOI: 10.3390/s23031466

Abstract

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

Keywords: internet of medical things; internet of things; internet of things in health; swarm intelligence algorithm; wearable devices; wireless sensor network.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

**Figure 1**
The number of reviewed papers.

**Figure 2**
Search statistics extracted from Scopus related to the application of SI in IoT: (a) per subject area, (b) per publication type, and (c) per year.

**Figure 3**
Search statistics extracted from Scopus related to the application of SI in IoMT: (a) per publication type, (b) per year.

**Figure 4**
Components of IoT technology.

**Figure 5**
Stages of IoT in Healthcare (IoMT).

**Figure 6**
Nature-inspired meta-heuristic algorithms and the position of SI algorithms in them.

**Figure 7**
Statistics of PSO, ACO, ABC, and other SI algorithms in the field of IoT/IoMT.

See this image and copyright information in PMC

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Swarm Intelligence in Internet of Medical Things: A Review

Affiliations

Swarm Intelligence in Internet of Medical Things: A Review

Authors

Affiliations

Abstract

Conflict of interest statement

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