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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63845
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Application
Bapuji institute of engineering and technology, Davanagere
Master of Computer Application
Bapuji institute of engineering and technology, Davanagere
The rapid decline of global biodiversity constitutes one of the most critical environmental challenges of the twenty-first century, driven by habitat loss, climate change, and the relentless pressure of illegal poaching. Nevertheless, there is one thing that for sure should not be omitted—the old school techniques of monitoring animals based on the human eye without the help of radio collars are no longer up to date because of the mentioned above reasons, and the quantity of collected data was rather small. Moreover, at the moment when we live in an era of new technologies like the Internet of Things (IoT), satellites, and biologging, we are collecting an enormous amount of information about our surrounding world, and consequently, the problem to be solved is the analysis of Big Data due to its size, velocity, and precision. The analysis of Big Data is the technique which is used in the process of data mining. The utilization of big data technology for storage, analysis, and processing of such information will help researchers analyze the migratory trends as well as any change in behavior. Moreover, visualization technology will be incorporated into this system to help the conservationists make their decisions based on the information available to them.
KEYWORDS
The project, Next-Generation Big Data Ecosystems for Wildlife Stewardship, marks a definitive departure from the era of anecdotal and fragmented field research. By successfully integrating distributed computing substrates with high-fidelity bio-telemetry, this initiative has demonstrated that Real-Time Biome Visibility is not merely a technical objective, but an operational necessity in the face of escalating anthropogenic pressures. The primary outcome of this research is the transition from a reactive to a Proactive Defense Posture. The successful deployment of Kinematic Anomaly Heuristics and Predictive Conflict Modeling proves that we can anticipate threats—be they poaching incursions or human-wildlife encounters—before they manifest into crises. This shift is predicated on the project's ability to collapse traditional data silos, creating a Unified Informational Singularity that informs everyone from the forest ranger to the national policymaker. Beyond its immediate tactical utility, the architecture provides a Sustainable Scientific Foundation. By utilizing open-source frameworks and cloud-native elasticity, the system is designed for Decadal Persistence.
1. Ayoola, Victoria & Idoko, Idoko & Eromonsei, Samson & Afolabi, Olusogun & APAMPA, kinkunmi & Oyebanji, Oluwatosin. (2024). The role of big data and AI in enhancing biodiversity conservation and resource management in the USA. World Journal of Advanced Research and Reviews. 23. 1851-1873. 10.30574/wjarr.2024.23.2.2350. https://www.researchgate.net/publication/383561717_The_role_of_big_data_and_AI_in_enhancing_biodiversity_conservation_and_resource_management_in_the_USA 2. A. Walia, A. Ghosh, V. Kumar, A. Prakash and G. P, "Wildlife Conservation and Habitat Monitoring," 2024 4th International Conference on Innovative Sustainable Computational Technologies (CISCT), Dehradun, India, 2024, pp. 1-5, doi: 10.1109/CISCT62494.2024.11134181. https://ieeexplore.ieee.org/abstract/document/11134181 3. Mohammed, Dr. (2026). Artificial Intelligence in Wildlife Tracking and Conservation. International Journal of Integrated Research and Practice. 66-76. 10.65579/31075037.0115. https://doi.org/10.65579/31075037.0115 4. Marvin, D. C., Koh, L. P., Lemoine, A. J., & Wich, S. A. (2016). Drone ecology: cleaner mechanisms for monitoring ecosystems. Nature Ecology & Evolution, 1(2). https://doi.org/10.1038/s41559-016-0038 5. Gonzalez, A. T., & Pielke, S. (2018). The Internet of Nature: IoT applications for Wildlife Conservation. IEEE Internet of Things Journal, 5(6), 450-461. https://ieeexplore.ieee.org/document/10395145
Publication Date
5/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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