Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Article


Akram, Junaid, Munawar, Hafiz Suliman, Kouzani, Abbas Z. and Mahmud, M. A. Parvez. 2022. "Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks." Sensors. 22 (3), pp. 1-23. https://doi.org/10.3390/s22031083
Article Title

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

ERA Journal ID34304
Article CategoryArticle
AuthorsAkram, Junaid, Munawar, Hafiz Suliman, Kouzani, Abbas Z. and Mahmud, M. A. Parvez
Journal TitleSensors
Journal Citation22 (3), pp. 1-23
Article Number1083
Number of Pages23
Year2022
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s22031083
Web Address (URL)https://www.mdpi.com/1424-8220/22/3/1083
Abstract

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

KeywordsAdaptive learning; Coverage area; Energy efficiency; Learning automata; Sensors; Wireless sensor network
Byline AffiliationsSuperior University, Pakistan
University of New South Wales
Deakin University
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