Sustainable data integrity in real-time environmental monitoring through probabilistic learning

Joseph Brian M. Kasozi, Robert Bakyayita, Musoke David, Sarah Hangujah

Abstract


As the need for real-time data pipelines grows in environmental monitoring, smart farming, and sustainability, data integrity amidst errors, missing values, and system failures remains a serious challenge. Low-quality data weakens decisions, creates inefficiencies and computational demand, contrary to the aim of green computing and digital infrastructure. This paper introduces an energy-conscious framework that blends probability-based methods with machine learning to increase data quality in real-time. Monte Carlo Markov Chain (MCMC) sampling handles uncertainty in fast-moving streams; combining with adaptive machine learning (ML) tools for error detection and data repair, the approach reduces costly reprocessing and wasted effort towards the ultimate aim of eco-friendly digital practice. The study follows a system-oriented design, tested through simulation and with environmentally sensed data in real-time scenarios. The main contributions of this monologue include outlining data quality challenges in sustainable real-time networks; presentation of a combinatorial MCMC-ML model tuned for speed and energy saving; subsequent proof of increased accuracy, completeness and data reliability with greater resource efficiency. Results show that this method works better than traditional rule-based approaches under shifting conditions, while also supporting environmental technologies by making smarter use of resources. This research adds to the field of sustainable data systems by showing how reliable insights can be maintained with greater efficiency, advancing eco-conscious real-time data engineering.

Received 14 May 2026

Accepted 09 August 2026

Published 09 September 2026


Keywords


Markov Chain Monte Carlo (MCMC), Machine Learning, Real-Time Data Streams, Data Quality, Sustainable Computing, Anomaly Detection, Green Computing, Time-Series Data, Environmental Monitoring

Full Text:

PDF

References


A. Karkouch, H. Mousannif, H. Al Moatassime, and T. Noel, “Data Quality in Internet of Things: A State-of-the-Art Survey,” Journal of Network and Computer Applications, vol. 73, pp. 57–81, Sep. 2016, doi: 10.1016/j.jnca.2016.08.002.

H. Gomes , J. Silva, and L. Pereira, “An efficient framework for real-time data quality management,” Journal of Data Science and Engineering, vol. 15, no. 2, pp. 123–134, 2021.

C. Batini and M. Scannapieca, Data Quality. Springer Berlin Heidelberg, 2006.

W. Fan and F. Geerts, “Foundations of Data Quality Management,” Synthesis Lectures on Data Management, vol. 4, no. 5, pp. 1–217, Jul. 2012, doi: 10.2200/s00439ed1v01y201207dtm030.

E. Strubell, A. Ganesh, and A. McCallum, “Energy and Policy Considerations for Deep Learning in NLP,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3645–3650, 2019, doi: 10.18653/v1/p19-1355.

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly Detection: A Survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1–58, Jul. 2009, doi: 10.1145/1541880.1541882.

C. Andrieu, N. de Freitas, A. Doucet, and M. I. Jordan, “An Introduction to MCMC for Machine Learning,” Machine Learning, vol. 50, no. 1/2, pp. 5–43, 2003, doi: 10.1023/a:1020281327116.

W. R. Gilks, S. Richardson, and D. Spiegelhalter, Eds., Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC, 1995.

P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long Short Term Memory Networks for Anomaly Detection in Time Series,” in Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2015. Accessed: Sep. 01, 2026. [Online]. Available: https://www.esann.org/sites/default/files/proceedings/legacy/es2015-56.pdf

K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding,” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18), pp. 387–395, Jul. 2018, doi: 10.1145/3219819.3219845.

G. Ras, N. Xie, M. Van Gerven, and D. Doran, “Explainable Deep Learning: A Field Guide for the Uninitiated,” Journal of Artificial Intelligence Research, vol. 73, pp. 329–397, Jan. 2022, doi: 10.1613/jair.1.13200.

F. Wang, Y. Jiang, R. Zhang, A. Wei, J. Xie, and X. Pang, “A Survey of Deep Anomaly Detection in Multivariate Time Series: Taxonomy, Applications, and Directions,” Sensors, vol. 25, no. 1, p. 190, Jan. 2025, doi: 10.3390/s25010190.

B. Deforce, M.-C. Lee, B. Baesens , E. S. Asensio, J. Yoo, and L. Akoglu, “Proceedings of the 2025 SIAM International Conference on Data Mining (SDM) | End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection,” Proceedings. [Online]. Available: https://doi.org/10.1137/1.9781611978520.61

Q. Liu, P. Boniol, T. Palpanas, and J. Paparrizos, “Time-Series Anomaly Detection: Overview and New Trends,” Proceedings of the VLDB Endowment, vol. 17, no. 12, pp. 4229–4232, Aug. 2024, doi: 10.14778/3685800.3685842.

J. N. Furze and S. M. Raafat, “Integrating nodes and optimization across social and scientific areas in sus-tainability loops,” International Journal of Simulation and Process Modelling, vol. 14, no. 5, pp. 397–398, 2019.

J. Zhang, H. Zhang, Y. Li, and X. Wu, “Morphological Component Analysis Based on Mixed Dictionary for Signal Denoising of Ground Penetrating Radar,” International Journal of Simulation and Process Modelling, vol. 14, no. 5, pp. 431–441, 2019, doi: 10.1504/ijspm.2019.104118.

A. A. Kraidi, R. H. Ahmed, A. S. Hadi, and M. S. Shaker, “Sustainable Wind Turbine Systems Based on on-Line Fault Estimation and Fault Tolerant Control,” Earth Systems Protection and Sustainability, vol. 2, pp. 265–291, 2022, doi: 10.1007/978-3-030-98584-4_11.

J. Hammond and V. A. Smith, “Bayesian Networks for Network Inference in Biology,” Journal of The Royal Society Interface, vol. 22, no. 226, May 2025, doi: 10.1098/rsif.2024.0893.

A. R. Nasser and A. M. Mahmood, “Deep Learning and Its Environmental Applications,” in Earth Systems Protection and Sustainability, Springer, 2022, pp. 293–318.

M. Ahmed, A. Naser Mahmood, and J. Hu, “A Survey of Network Anomaly Detection Techniques,” Journal of Network and Computer Applications, vol. 60, pp. 19–31, Jan. 2016, doi: 10.1016/j.jnca.2015.11.016.

Q. Meng and J. Yan, “A Data Cleaning Method for Water Quality Based on Improved Hierarchical Clustering Algorithm,” International Journal of Simulation and Process Modelling, vol. 14, no. 5, p. 442, 2019, doi: 10.1504/ijspm.2019.104120.

Y. Zhou and L. T. Kubota, “Trends in Electrochemical Sensing,” ChemElectroChem, vol. 7, no. 18, pp. 3684–3685, Aug. 2020, doi: 10.1002/celc.202001025.

H. Qin, M. Yu, Y. Lai, Z. Liu, and J. Liu, “Cloud Resource Orchestration Optimisation Based on ARIMA,” International Journal of Simulation and Process Modelling, vol. 14, no. 5, p. 420, 2019, doi: 10.1504/ijspm.2019.104116.

J. N. Furze and E. H. Mayad, “Generators, Harmonics and Evolutionary Emergence,” in Earth Systems Protection and Sustainability, Springer, 2022, pp. 17–34.

D. Dayal, P. K. Gupta, and A. Pandey, “Streamflow Estimation Using Satellite-Retrieved Water Fluxes and Machine Learning Technique Over Monsoon-Dominated Catchments of India,” Hydrological Sciences Journal, vol. 66, no. 4, pp. 656–671, Mar. 2021, doi: 10.1080/02626667.2021.1889557.

R. Ande, D. Mehta, V. Maurya, P. Birbal, J. N. Furze, and S. Verma, “Flood Risk Assessment and Hydrological Simulation in the Upper Krishna River Basin, India,” International Journal of Water, vol. 17, no. 2, pp. 153–184, 2025, doi: 10.1504/ijw.2025.151230.

D. B. Rubin, Multiple Imputation for Nonresponse in Surveys. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1987.

S. Aljbali and K. Roy, “Anomaly Detection Using Bidirectional LSTM,” in ntelligent Systems and Applications, Advances in Intelligent Systems and Computing, Springer, 2020, pp. 612–619.

J. N. Furze, Q. Zhu, F. Qiao, and J. Hill, “Mathematical Methods to Quantify and Characterise the Primary Elements of Trophic Systems,” International Journal of Computer Applications in Technology, vol. 47, no. 4, p. 314, 2013, doi: 10.1504/ijcat.2013.055324.

F. Barros, “Semantics of Multisampling Systems,” International Journal of Simulation and Process Modelling, vol. 11, no. 5, p. 374, 2016, doi: 10.1504/ijspm.2016.079201.

J. N. Furze, Q. Zhu, J. Hill, and F. Qiao, “Biological Modelling for Sustainable Ecosystems,” in Mathematical Advances Towards Sustainable Environmental Systems, Springer, 2016, pp. 9–42.

K. Basaid, B. Chebli, J. Furze, E. H. Mayad, and R. Bouharroud, “The Value of Simulations Characterizing Classes of Symbiosis: ABCs of Formulation Design,” in Distributed Sensing and Intelligent Systems, ser. Studies in Distributed Intelligence, Springer, 2022, pp. 589–605.

C. P. Robert and G. Casella, “The Metropolis—Hastings Algorithm,” in Monte Carlo Statistical Methods, ser. Springer Texts in Statistics. Springer, 2004, pp. 267–320.

S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6, no. 6, pp. 721–741, Nov. 1984, doi: 10.1109/tpami.1984.4767596.

D. V. Mestry and A. R. Bhowmick, “Demystifying Monte Carlo Methods in R: A Guide From Metropolis–Hastings to Hamiltonian Monte Carlo With Biological Growth Equation Examples,” Ecological Modelling, vol. 501, p. 110922, Feb. 2025, doi: 10.1016/j.ecolmodel.2024.110922.

M. A. Belay, S. S. Blakseth, A. Rasheed, and P. Salvo Rossi, “Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions,” Sensors, vol. 23, no. 5, p. 2844, Mar. 2023, doi: 10.3390/s23052844.

N. Mejri, L. Lopez-Fuentes, K. Roy, P. Chernakov, E. Ghorbel, and D. Aouada, “Unsupervised Anomaly Detection in Time-Series: An Extensive Evaluation and Analysis of State-of-the-Art Methods,” Expert Systems with Applications, vol. 256, pp. 124922–124922, Jul. 2024, doi: 10.1016/j.eswa.2024.124922.




DOI: https://dx.doi.org/10.21622/ACE.2026.06.2.2152

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Jospeh Brian M. Kasozi, Robert Bakyayita, Musoke David, Sarah Hangujah

Advances in Computing and Engineering

E-ISSN: 2735-5985

P-ISSN: 2735-5977

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

ace@aast.edu