AI-Augmented DevOps Strategies for Scalable WSN Data Processing and Compression |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-4 |
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| Year of Publication : 2025 | ||
| Authors : Chhaya Gunawat, Rohit Kumar Gupta | ||
| DOI : 10.14445/22312803/IJCTT-V73I4P103 | ||
How to Cite?
Chhaya Gunawat, Rohit Kumar Gupta, "AI-Augmented DevOps Strategies for Scalable WSN Data Processing and Compression," International Journal of Computer Trends and Technology, vol. 73, no. 4, pp. 19-27, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I4P103
Abstract
The rapid proliferation of Wireless Sensor Networks (WSNs) has led to an exponential increase in sensor-generated data, necessitating efficient data processing and compression techniques for scalability and real-time decision-making. Traditional DevOps methodologies for managing WSNs often face challenges in handling large-scale data streams, optimizing resource utilization, and ensuring low-latency processing. This paper proposes an AI-augmented DevOps strategy that integrates machine learning and automation to enhance data processing and compression in WSNs. Our approach leverages intelligent pipeline automation, adaptive compression algorithms, and predictive analytics to optimize data flow, minimize energy consumption, and improve system reliability. We evaluate the framework’s performance regarding compression ratio, latency, processing overhead, and reconstruction accuracy, demonstrating its efficiency in handling large-scale WSN deployments.
Keywords
AI-augmented DevOps, Wireless Sensor Networks, Data Processing, Data Compression, Scalable Computing, Automation.
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