Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Edge Computing and Artificial Intelligence Powered Dynamic Environmental Conservation Strategies for Sustainable Ecosystem Management and Predictive Climate Analysis

Authors
Sathya Kannan1, C. Raghavendra2, *, J. Senthil Kumar3, S. Narayanasamy4, Allam Balaram5, S. Aravindh6
1AI Engineer, John Deere, Moline, USA
2Associate Professor, Department of Cyber Security, CVR College of Engineering, Hyderabad, Telangana, India
3Professor, Department of IT, Sona College of Technology, Salem, 636005, Tamil Nadu, India
4Assistant Professor, Department of Computer Science and Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
5Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, 500043, Telangana, India
6Assistant Professor, Department of Mechanical Engineering, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India
*Corresponding author. Email: crg.svch@gmail.com
Corresponding Author
C. Raghavendra
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_41How to use a DOI?
Keywords
Edge computing; artificial intelligence; real-time environmental monitoring; predictive climate analysis; machine learning; IoT-enabled ecosystem management
Abstract

The multidimensional environmental issues arising from climate change, deforestation, and ecosystem degradation necessitate the deployment of sophisticated technology-based solutions for sustainable management of these resources. Here, we propose an AI-based, edge computing-enabled active environmental conservation framework for real-time ecosystem control, predicting climate for adaptive conservation measures. The proposed solution differs from conventional centralized AI approaches by utilizing edge computing to process environmental data at the source, thereby minimizing latency and improving responsiveness. Leveraging machine learning and deep learning along with IoT-enabled telemetry, this framework provides real-time insights, early detection of climate anomalies, and data-driven policy recommendation. Through our real-world case studies and scalable model of research, we connect the dots between theory and practice in AI. It also tackles regulatory hurdles and encourages sustainable, AI-powered ecosystem resilience approaches. These finishes emphasize, the intelligent conservation strategy has proven very successful for increasing biodiversity conservation, decreasing ecological risks and improving climate sustainability. We aim to further the field of next-generation environmental conservation strategies by providing a route to activate static AI models into dynamic, real-time, and expandable ecosystem management tools.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_41How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sathya Kannan
AU  - C. Raghavendra
AU  - J. Senthil Kumar
AU  - S. Narayanasamy
AU  - Allam Balaram
AU  - S. Aravindh
PY  - 2025
DA  - 2025/05/23
TI  - Edge Computing and Artificial Intelligence Powered Dynamic Environmental Conservation Strategies for Sustainable Ecosystem Management and Predictive Climate Analysis
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 477
EP  - 490
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_41
DO  - 10.2991/978-94-6463-718-2_41
ID  - Kannan2025
ER  -