Sensor-Based Intelligent Systems for Advanced Environmental Monitoring and Control

Ratul Talukder; Anna Khan; Jhon Kabir

doi:10.61424/ijans.v2i2.858

Authors

Ratul Talukder Department of Pharmacy, Bangladesh University, Dhaka, Bangladesh
Anna Khan Department of Pharmacy, Bangladesh University, Dhaka, Bangladesh
Jhon Kabir Department of Pharmacy, Bangladesh University, Dhaka, Bangladesh

DOI:

https://doi.org/10.61424/ijans.v2i2.858

Keywords:

Sensor-Based Monitoring Systems, Environmental Monitoring and Control, Intelligent Sensor Networks, Real-Time Data Analytics, AI in Environmental Systems

Abstract

The increasing demand for accurate and real-time environmental monitoring has led to the development of sensor-based intelligent systems that integrate advanced sensing technologies with data analytics. This study presents a comprehensive framework for environmental monitoring and control using interconnected sensor networks and intelligent processing systems. Air quality sensors contribute the highest share (30%) to environmental monitoring, followed by temperature sensors (25%), humidity sensors (20%), soil moisture sensors (15%), and noise sensors (10%). This distribution highlights the critical importance of air quality and climate-related parameters in modern environmental monitoring systems. The performance improvements achieved through intelligent systems showing that pollution detection improves by 35%, data accuracy by 32%, response time by 30%, energy efficiency by 28%, and resource optimization by 25%. These results demonstrate the effectiveness of integrating sensor networks with advanced analytics, particularly in enhancing detection capabilities and enabling faster decision-making processes. The study proposes a multi-layered system architecture that combines sensor-based data acquisition, real-time processing, and intelligent analytics to improve monitoring efficiency. By leveraging artificial intelligence and machine learning techniques, the system can analyze complex environmental data, detect anomalies, and provide predictive insights. The integration of these technologies enables proactive environmental management, reducing risks and improving sustainability. In conclusion, the findings highlight the significant role of sensor-based intelligent systems in advancing environmental monitoring and control. The proposed framework offers a scalable and efficient solution for addressing environmental challenges, supporting sustainable development, and improving overall system performance in diverse applications.

Downloads

Download data is not yet available.

References

Ahmad, A., Imran, M., & Ahsan, H. (2023). Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases. Pharmaceutics, 15(6), 1630. https://doi.org/10.3390/pharmaceutics15061630

Ahsan, H. (2019). Biomolecules and biomarkers in oral cavity: bioassays and immunopathology. Journal of Immunoassay and Immunochemistry, 40(1), 52-69.

Alam, M. I., Hemal, M. A. K. P., Sami, M. A., & Rahman, M. L. (2024). Robust and Interpretable Crop Recommendation: A Case Study on a Balanced Multi-crop Agronomic Dataset. European Journal of Ecology, Biology and Agriculture, 1(5), 168-184. https://doi.org/10.59324/ejeba.2024.1(5).14

Alam, M. I., Sami, M. A., Hemal, M. A. K. P., & Rahman, M. L. (2023). Predictive Analytics and Decision Intelligence for Climate-Resilient Agritech Systems. Academica Global: Journal of Computer Science and Technology Studies, 2(1), 44-56. https://doi.org/10.32996/agjcsts.2023.2.1.4

Aryutova, K., Stoyanov, D. S., Kandilarova, S., Todeva-Radneva, A., & Kostianev, S. S. (2021). Clinical use of neurophysiological biomarkers and self-assessment scales to predict and monitor treatment response for psychotic and affective disorders. Current Pharmaceutical Design, 27(39), 4039-4048.

Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805.

Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the Internet of Things. Proceedings of the MCC Workshop on Mobile Cloud Computing, 13–16.

Dennis, J. K., Sealock, J. M., Straub, P., Lee, Y. H., Hucks, D., Actkins, K. E., ... & Davis, L. K. (2021). Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome medicine, 13(1), 6.

Dhama, K., Latheef, S. K., Dadar, M., Samad, H. A., Munjal, A., Khandia, R., ... & Joshi, S. K. (2019). Biomarkers in stress related diseases/disorders: diagnostic, prognostic, and therapeutic values. Frontiers in molecular biosciences, 6, 465402.

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.

Juie, B. J. A., Kabir, J. U. Z., Ahmed, R. A., & Rahman, M. M. (2021). Evaluating the impact of telemedicine through analytics: Lessons learned from the COVID-19 era. Journal of Medical and Health Studies, 2(2), 161–174. https://doi.org/10.32996/jmhs.2021.2.2.19.

Nusrat, S., Hossain, F., & Sikder, T. R. (2024). Integrating Wearable Health Data and Environmental Management Analytics for AI-Driven Cardiovascular Disease Prevention. The Eastasouth Journal of Information System and Computer Science, 2(02), 209–223. https://doi.org/10.58812/esiscs.v2i02.868

Sami, M. A., Hemal, M. A. K. P., Alam, M. I., & Rahman, M. L. (2024). Data Governance and Analytics Infrastructure for Scalable Decision-Making in Development and Agritech Programs. European Journal of Applied Science, Engineering and Technology, 2(2), 388-403. https://doi.org/10.59324/ejaset.2024.2(2).28

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks, 76, 146–164.

Sikder, T. R., Dash, S., Uddin, B., & Hossain, F. (2023a). AI-Powered Data Analytics and Multi-Omics Integration for Next-Generation Precision Oncology and Anticancer Drug Development. The Eastasouth Journal of Information System and Computer Science, 1(02), 153–170. https://doi.org/10.58812/esiscs.v1i02.838

Sikder, T. R., Siam, M. A., Melon, M. M. H., Uddin, S. M. M., Mohonta, S. C., & Karim, F. (2023b). A Multimodal Data Analytics Framework for Early Cancer Detection Using Genomic, Radiomic, and Clinical Big Data Fusion. Journal of Computer Science and Technology Studies, 5(3), 183-188. https://doi.org/10.32996/jcsts.2023.5.3.13

Silberring, J., & Ciborowski, P. (2010). Biomarker discovery and clinical proteomics. TrAC Trends in Analytical Chemistry, 29(2), 128-140.

Vanu, N., Hasan, M. R., Sikder, T. R., & Tamanna, Z. S. (2021). AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling. Journal of Computer Science and Technology Studies, 3(2), 124-141. https://doi.org/10.32996/jcsts.2021.3.2.11

Zhang, C., Patras, P., & Haddadi, H. (2018). Deep learning in mobile and wireless networking. IEEE Communications Surveys & Tutorials, 21(3), 2224–2287.