Edge Computing for Environmental Monitoring: Real-Time Intelligence for a Sustainable Planet
Abstract
Edge computing brings processing power closer to data sources, enabling faster and more efficient environmental monitoring. This paper explores edge-based architectures for tracking air quality, water levels, forest health, and climate conditions using low-power sensors and AI-enabled analytics. Case studies demonstrate how localized processing reduces latency, energy consumption, and reliance on centralized infrastructure, providing an effective approach to real-time, scalable sustainability solutions in remote and urban environments.
References
Chittineni, S. (2024). AI-Driven Code Refactoring: Improving Java Backend Code Quality with Machine Learning Models. American Journal of AI & Innovation, 6(6).
Brahmandam, B. A. (2024). Using artificial intelligence and AIOps, automated fault prediction and prevention in Cloud Native settings. International Journal of Computer Techniques, 11(6), 1-7.
Brahmandam, B. A. (2025). Cloud Migration and Hybrid Infrastructure in Financial Institutions. International Journal of Computer Science Engineering Techniques, 9(1), 42-46.
Mohammed, C. S. A. (2025). Integrated Financial Ecosystems: Leveraging FRDP to Bridge Risk, Compliance, and Product Innovation. Indonasian Journal of Advanced Research & Technology, 7(7).
Chittineni, S. (2025). AI-Powered Predictive Analytics for E-commerce: Enhancing User Experience and Business Decision Making. Australian Journal of Cross-Disciplinary Innovation, 7(7).
Chittineni, S. (2019). Optimizing Microservices Performance with Reinforcement Learning: A Case Study in Spring Boot Applications. American Journal of AI & Innovation, 1(1).
Chittineni, S. (2019). AI-Driven Optimization of Backend Systems: Enhancing Performance and Reducing Latency in Large-Scale Applications. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Chittineni, S. (2020). Leveraging Machine Learning for Automated Thread Dump Analysis and Performance Tuning in Enterprise Java Applications. Australian Journal of Cross-Disciplinary Innovation, 2(2).
Mohammed, C. (2021). Revolutionizing Financial Operations: A Comprehensive Study on the Impact of SAP and Kyriba Integration. International Journal of Sustainable Development in Computing Science, 3(2), 1-19. Retrieved from https://ijsdcs.com/index.php/ijsdcs/article/view/696/260
The Critical Role of Accurate Balance Carry Forward in Preventing Financial Irregularities. (2022). International Journal of Interdisciplinary Finance Insights, 1(1), 1-13. https://injmr.com/index.php/ijifi/article/view/143
Brahmandam, B. A. (2025 )MLOps in Finance: Automating Compliance & Fraud Detection.
Brahmandam, B. A. (2025). Beyond DevOps: The Evolution Toward Intelligent IT Operations with AIOps and MLOps.
Chittineni, S. (2022). Automated API Performance Testing and Anomaly Detection Using Machine Learning in RESTful Architectures. American Journal of AI & Innovation, 4(4).
Chittineni, S. (2023). Enhancing Messaging Systems with AI: Predictive Load Balancing in JMS and IBM MQ. American Journal of AI & Innovation, 5(5).
Chittineni, S. (2023). Shadow Comparator with AI: A Machine Learning Approach for Anomaly Detection in Production Systems. Australian Journal of Cross-Disciplinary Innovation, 5(5).
Mitigating Risks and Ensuring Compliance: The Necessity of Regular Upgrades to SAP Financial Products Subledger (FPSL) (C. S. A. Mohammed , Trans.). (2023). International Journal of Creative Research In Computer Technology and Design, 5(5), 1-11. https://jrctd.in/index.php/IJRCTD/article/view/75
Chittineni, S. (2024). Real-Time Failure Prediction in Large-Scale Enterprise Applications Using Deep Learning Techniques. Australian Journal of Modern Research & Applications, 7(7).
Chittineni, S. (2024). Intelligent Payment Fraud Detection: Applying Deep Learning Models to Secure Financial Transactions. Australian Journal of Cross-Disciplinary Innovation, 6(6).
