A Machine Learning Assisted Method for Coverage Optimization in a Network of Mobile Sensors

In this work, efficient algorithms are developed to increase the area covered by a network of mobile sensors. The sensors are divided into k sets, and then the proposed algorithms perform iteratively to increase the area covered by at least k sensors as much as possible. Since the performance of the...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-06, Vol.19 (6), p.7301-7311
Main Authors: Mahboubi, Hamid, Blouin, Stephane, Aghdam, Amir G.
Format: Article
Language:English
Subjects:
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Algorithms
Clustering
Clustering algorithms
Coverage
Energy consumption
Informatics
Machine learning
mobile sensor networks (MSNs)
Optimization
Partitioning algorithms
Performance enhancement
Sensor phenomena and characterization
Sensors
Surveillance
Wireless sensor networks
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Summary:In this work, efficient algorithms are developed to increase the area covered by a network of mobile sensors. The sensors are divided into k sets, and then the proposed algorithms perform iteratively to increase the area covered by at least k sensors as much as possible. Since the performance of the algorithms highly depends on the initial positions of sensors, we use the K-means clustering technique for partitioning the sensors into k sets. Simulation results confirm the effectiveness of the proposed algorithms. They also show that using the K-means clustering technique improves the performance of the algorithms in terms of energy consumption, covered area, and convergence time.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3205368