Optimizing Energy Resources in WSNs: ARIMA Feature Selection Meets Adaptive Reinforcement Learning

ABSTRACT

Efficient energy management in WSNs is pivotal for prolonged network lifetime and sustained performance. This research introduces a novel approach to energy optimization through the integration of an ARIMA-driven feature selection process and an Actor-Critic Reinforcement Learning model. The Intel Lab dataset, encompassing data from 54 nodes distributed over a month, serves as the basis for experimentation. In the proposed methodology, the experimental setup ensures the validity of the study, leveraging the realism of the Intel Lab dataset. The data collection process captures the intricacies of WSN dynamics, with temperature records collected from strategically positioned nodes. The ARIMA-driven feature selection method refines the dataset, capturing temporal dependencies critical for energy prediction. The Actor-Critic model, a hybrid of policy and value-based reinforcement learning, dynamically adapts energy allocation strategies based on learned policies and offers a dynamic solution to the challenges posed by WSNs’ ever-changing environments. Comparative analysis with methods reveals lower energy consumption of 0.32 mJ, an extended network lifetime of 1501 rounds, and higher prediction accuracy of 98%. The study’s implications extend beyond the specific algorithms, suggesting a shift toward adaptive learning models in WSNs. The findings open avenues for future research in the integration of machine learning models for sustainable and efficient WSN deployments, emphasizing the growing importance of dynamic adaptation in sensor networks. Moreover, limitations, such as simulation realism and computational complexity, are acknowledged, prompting avenues for future research.

KEYWORDS:

• Wireless Sensor Networks (WSNs)
• machine learning
• energy management
• Auto Regressive Integrated Moving Average (ARIMA)
• Actor-Critic Reinforcement Learning
• energy efficiency

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval

Compliance with Ethical Standards

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Author contribution

ASS agreed on the content of the study. ASS and SPAL collected all the data for analysis. ASS agreed on the methodology. ASS and SPAL completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

S Anslam Sibi

S Anslam Sibi received the BTech degree in Information Technology from St.Xavier’s Catholic College of Engineering, Nagercoil and ME degrees in Computer Science and Engineering from Misrimal Navajee Munoth Jain Engineering College, Chennai. She is working in the department of Information Technology in St.Joseph’s Institute of Technology. She is currently pursuing the part-time PhD degree in the Faculty of Information Communication and Engineering, Anna University, Chennai. She is having 10 years of teaching experience and active member of Computer Society of India. Her research interests include wireless sensor networks, IoT, Artificial Intelligence and Machine Learning.

L Sherly Puspha Annabel

L. SHERLY PUSPHA ANNABEL received her BE degree in computer 725 science and engineering from Karunya Institute of Technology, Coimbatore, India, in 2000 and her ME degree in computer science and engineering from Jaya Engineering College, Chennai, India, in 2006. She has completed her Doctoral Degree in Anna University, Chennai. Currently, she is working in St. Joseph’s College of Engineering, 730 Chennai. She has 22 years of teaching experience and also a life member of ISTE. Her areas of interest include Machine Learning, Deep Learning, Wireless Sensor Networks, IoT. She has served as reviewer for reputed International journals and session chair for International Conferences.