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Wireless Personal Communications

Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches

  • 10-07-2024
Published in:
Authors
Pooja Chaturvedi
A. K. Daniel
Vipul Narayan
Published in
Wireless Personal Communications | Issue 2/2024

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Abstract

The article explores the application of machine learning approaches to address the target coverage problem in wireless sensor networks (WSN). It delves into the challenges of managing large-scale sensor networks with dynamic topologies and discusses the advantages of machine learning techniques in predicting node status for optimal coverage. The paper reviews various machine learning models, including supervised, unsupervised, and reinforcement learning, and evaluates their performance in enhancing network lifetime and reducing data transmissions. The proposed approach, which utilizes machine learning to predict node status based on historical data, is compared with existing methods, demonstrating its superior accuracy and efficiency. The article concludes with potential future research directions, emphasizing the integration of deep learning concepts to further enhance network performance.
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previous article Cluster-Based Hybrid Routing Technique for Wireless Sensor Networks
next article Towards Detection of DDoS Attacks in IoT with Optimal Features Selection
Title
Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches
Authors
Pooja Chaturvedi
A. K. Daniel
Vipul Narayan
Publication date
10-07-2024
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 2/2024
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-024-11410-x
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