Bee Swarm Intelligence Inspired Sustainable Swarm Air Purification Agent System with K-means Clustering

Most of the world’s population is living in hazardous air quality. In this paper, we have proposed an air purification peer-to-peer networked multi-agent-based intelligent system in open areas for a society complex in urban cities. In this paper, an artificial bee swarm optimization algorithm is use...

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Bibliographic Details
Published in:SN computer science Vol. 6; no. 5; p. 502
Main Authors: Sahai, Priya, Kumar, Rakesh, Mehrotra, Monica
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.06.2025
Springer Nature B.V
Subjects:
Advances in Computational Approaches for Image Processing
Air pollution
Air purification
Air quality
Cities
Cloud Applications and Network Security
Cluster analysis
Clustering
Collaboration
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Decision making
Food supply
Foraging behavior
Heart
Information Systems and Communication Service
Multiagent systems
Nitrogen dioxide
Optimization
Optimization algorithms
Original Research
Outdoor air quality
Pattern Recognition and Graphics
Pollutants
Search algorithms
Smart cities
Software Engineering/Programming and Operating Systems
Supply chains
Swarm intelligence
Urban areas
Vector quantization
Vision
Wireless Networks
Germany
India
Fitness value
Swarm intelligence
Bee swarm optimization algorithm
Clustering
AQI index
ISSN:2661-8907, 2662-995X, 2661-8907
Online Access:Get full text
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Summary:Most of the world’s population is living in hazardous air quality. In this paper, we have proposed an air purification peer-to-peer networked multi-agent-based intelligent system in open areas for a society complex in urban cities. In this paper, an artificial bee swarm optimization algorithm is used to provide cooperative, intelligent, and novel solutions for cleaner air in urban societies. With this system, we are able to optimize power consumption and make the system sustainable for future-centric smart city layouts. Artificial bee colony algorithm finds the best solution after evaluating the fitness value of the source. We have also used the k-means clustering algorithm to determine the physical locations of such agent units and provided a scalable solution for the problem. Implementation is done using Spyder (Anaconda 3) tool and results have shown that our proposed algorithm provides a scalable efficient solution of the identified problem.
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-025-04033-x