Integration of Real-Time Occupancy Detection Module and Security Encryption Standards for an IOT-Based Smart Distribution Metered System

Most people contemplate how energy is maintained in houses and offices. Yet, the problems persist and are difficult to disregard. Increasing electricity costs, safety concerns, weaknesses in linked devices, and the financial losses associated with powering vacant quarters are issues that require urgent attention. This study offers an IoT-based smart supply model that collectively tackles efficiency, computerization, and security, rather than treating them as separate problems. The proposed system runs on an ESP32 microcontroller. It is a commonly used option for work that involves IoT, as it does not fail, it is inexpensive, and well-supported. The PIR motion sensors are responsible for occupancy detection by checking whether a room is in use before any devices are switched on. Current sensors display actual power draw. The different parts are not very useful on their own, but working together creates wonders and solves human challenges. Security handling is achieved via AES encryption, which protects metering and control data as it moves through the system. AES is not a new skill. However, applying it consistently to the residential system is still not as common as it perhaps should be, considering how much can be exposed through home energy data. The results showed occupant detection to be 95% accuracy, and energy consumption fell to about 35% compared to a system running without automation. In summary, the study integrated a real-time occupancy detection module and security encryption standards for an IoT-based Metered System.

Smart Distribution System, IoT Integration, AES Encryption, Energy Efficiency, Occupancy Detection

1. Akazue M. I., Ajenaghughrure I. B. (2016). Internet of Vehicle Speed Detection and Reporting System Based on RFID, Journal of the Nigerian Association of Mathematical Physics, Vol 34, pp219-226. [Google Scholar] [Crossref]

2. Ajenaghughrure I. B., Akazue M.I., Ekurume E. O.. (2015). Application of Fuzzy Logic in a Hybrid Intelligent Zero Tolerance Alcohol Influenced Vehicle Driving System, Science Focus, Vol. 20 No.2, pp. 35 – 45 [Google Scholar] [Crossref]

3. Akazue M. I. (2016a) An Automated Fuzzy Logic Based Temperature Control Model for Sustaining Server Room Temperature Stability. Journal of the Nigerian Association of Mathematical Physics, Vol 34, pp233-240 [Google Scholar] [Crossref]

4. Akazue, M. I. (2017) Users’ Perception in an Intelligent Automatic Fire Detection System for Developing Countries. Journal of Computer Science and its Applications Vol 24, No.2, pp 111 -119 [Google Scholar] [Crossref]

5. Akazue, M., Pirah, L. O., Ogeh, C., & Otegbalor, S. A. (2024a). DEVELOPMENT OF ENHANCED AGRICULTURAL GREENHOUSE SYSTEMS FOR DEVELOPING COUNTRIES. Nigerian Journal of Science and Environment, 22(1), Vol 22 (1) 68 –77. https://doi.org/10.61448/njse221246. [Google Scholar] [Crossref]

6. Akazue M. I. (2016b). A fuzzy based intelligent irrigation system. Science-Tech Journal, Vol1, No1, pp12-22. [Google Scholar] [Crossref]

7. Ukadike, I. D., Akazue, M., Omede, E., Akpoyibo, T. P (2023). Development of an IoT-based Air Quality Monitoring System, FUPRE Journal of Scientific and Industrial Research (FJSIR) Vol. 7 Number 4, pp 53-62 [Google Scholar] [Crossref]

8. Akazue Maureen I., and Ajenaghughrure Ighoyota Ben (2017)..Fuzzy Based Enhanced Smart Rest Room Automated Faucet System. Intl Journal of Engineering and Manufacturing, Vol. 7, No. 3 , pp 20-30. [Google Scholar] [Crossref]

9. Akazue, M., Esiri, K. H., & Clive, A. (2024b). Application of RFM model on Customer Segmentation in Digital Marketing. Nigerian Journal of Science and Environment, 22 (1) 57 –67. https://doi.org/10.61448/njse221245 [Google Scholar] [Crossref]

10. Ako, R. E., Aghware, F. O., Okpor, M. D., Akazue, M. I., Yoro, R. E., Ojugo, A. A., Setiadi, D. R. I. M., Odiakaose, C. C., Abere, R. A., Emordi, F. U., Geteloma, V. O., & Ejeh, P. O. (2024). Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost. Journal of Computing Theories and Applications, 2(1), 86–101. https://doi.org/10.62411/jcta.10562 [Google Scholar] [Crossref]

11. Ojie D., Akazue M., Imianvan A. (2023a). A Framework for Feature Selection using Data Value Metric and Genetic Algorithm, International Journal of Computer Applications, Volume 184, Issue 43, p14-21, doi:10.5120/ijca2023922533 [Google Scholar] [Crossref]

12. A. A. Ojugo, M. I. Akazue, P. O. Ejeh, N. C. Ashioba, C. C. Odiakaose, R. E. Ako, and F. U. Emordi (2023) Forging a User-Trust Hybrid Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study, Journal of Computing Theories and Applications, vol. 1, no. 2, page 1-11. [Google Scholar] [Crossref]

13. M. D. Okpor, F. O. Aghware, M. I. Akazue, A. A. Ojugo, F. U. Emordi, C. C. Odiakaose, R. E. Ako, V. O. Geteloma, A. P. Binitie, P. O. Ejeh (2024). Comparative Data Resample to Predict Subscription Services Attrition Using Tree-based Ensembles, Journal of Fuzzy Systems and Control, Vol. 2, No 2, 117-124, DOI: 10.59247/jfsc.v2i2.213. [Google Scholar] [Crossref]

14. Akazue, M., Esiri, K. H., & Clive, A. (2024b). Application of RFM model on Customer Segmentation in Digital Marketing. Nigerian Journal of Science and Environment, 22 (1) 57 –67. https://doi.org/10.61448/njse221245 [Google Scholar] [Crossref]

15. Ajenaghughrure, I., Sujatha, P., Maureen, A. (2017). Fuzzy based multi-fever symptom classifier diagnosis model. International Journal of Information Technology and Computer Science, Vol. 9, No. 10, pp. 13–28. DOI: 10.5815/ijitcs.2017.10.02. [Google Scholar] [Crossref]

16. Akazue, M.I and Onyekweli C.O, (2015) Survey of Automated Electricity Billing and Metering Systems: Acceptability and Implementability factors. Science Focus, Vol. 20 No.2, pp. 20 – 28. [Google Scholar] [Crossref]

17. Ali, I., Patterson, M., Rahman, S., Yao, L., & Wu, L. (2021). Australia’s transition towards smart metering: Issues, statuses, policies, and impacts on future grid. Renewable and Sustainable Energy Reviews, 135, 110105 [Google Scholar] [Crossref]

18. Ojie D.V, Akazue M, Omede E.U, Oboh E.O, Imianvan A. (May 2023b) Survival Prediction of Cervical Cancer Patients using Genetic Algorithm-Based Data Value Metric and Recurrent Neural Network, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307 (Online), Volume-13 Issue-2, May 2023 [Google Scholar] [Crossref]

19. Chen, Q., Zhang, X., & Sun, H. (2021). Application of Multi-source Heterogeneous Data Fusion for Power Equipment State Visualized Management. IOP Conference Series: Earth and Environmental Science, 787(2), 022032. [Google Scholar] [Crossref]

20. Dio T., M. I. Akazue, S. N. Okofu (November 2023). Development of an Online Examination Monitoring System using Zoom, International Journal of Computer Applications (0975 – 8887) Volume 185 – No. 40, pp 1 -10. [Google Scholar] [Crossref]

21. Chou, P. C., Guttromson, R. T., Bhatnagar, D., Lysiak, M. S., & Wang, J. (2020). Simulation-assisted distributed energy resource interconnection analysis. IEEE Transactions on Smart Grid, 12(1), 79-88 [Google Scholar] [Crossref]

22. Cardenas, J. A., Gemoets, L., Ablanedo Rosas, J. H., &Sarfi, R. (2014). A literature survey on Smart Grid distribution: An analytical approach. Journal of Cleaner Production, 65, 202--216. https://doi.org/10.1016/j.jclepro.2013.09.019 [Google Scholar] [Crossref]

23. Albraheem, L., Alajlan, H., Aljenedal, N., Alkhair, L. A., &Gwead, S. B. (2022). An IoT-based smart plug energy monitoring system. Computers & Electrical Engineering, 102, 108253. https://doi.org/10.1016/j.compeleceng.2022.108253. [Google Scholar] [Crossref]

24. Asamoah, A., Zhang, J., Ramos, C., Neto, J. P., Kyriakides, E., Granados, J. A. T., ... & Shahidehpour, M. (2022). Machine learning in smart-grid operations: A comprehensive overview. Transactions of the Institute of Measurement and Control, 01423312221109579. [Google Scholar] [Crossref]

25. Andren, F., Vukovic, O., Karedal, J., & Jonsson, F. (2022). Performance Analysis of Wireless Technologies for Smart Metering Communications in a Digital Twin Testbed. Technologies, 10(1), 2. [Google Scholar] [Crossref]

26. Arias, M. V., Rey, J. M., & Vizcaino, I. (2022). Smart grid development: A worldwide policy analysis. Electric Power Systems Research, 208, 107703. [Google Scholar] [Crossref]

27. Wan, C., Li, D., Liu, X., & Qiu, J. (2018). A general test platform for smart grid applications. Energies, 11(1), 230. [Google Scholar] [Crossref]

28. Ihama E.I., Akazue M.I. , Omede E., Ojie D. (2023). A Framework for Smart City Model Enabled by Internet of Things (IoT). International Journal of Computer Applications (0975 – 8887) Volume 185 – No. 6, May 2023, pp 6-11 [Google Scholar] [Crossref]

29. M.I. Akazue and B. O. Ojeme (2016). An RFID Based Driver’s License Authentication Model for Enhancing City Transport. The Journal of the Nigerian Institution of Production engineers, Vol 20, pp 282-294. [Google Scholar] [Crossref]

30. IHAMA, E. I., AKAZUE, M.I., & AMENAGHAWON, V. A. (2025a). Vehicular Movement Prediction Via Supervised Vector Machine, FUPRE Journal of Scientific and Industrial Research, 9(1):206-215 [Google Scholar] [Crossref]

31. S. N. Okofu, T. Anning-Dorson, & H. I. Duh (2025). Consumer Adoption and Continual Use of E-Vouchers: A Study of the Nigeria Telecommunication, International Research Journal of Multidisciplinary Scope, 06(02):330-342, DOI:10.47857/irjms.2025.v06i02.03788 [Google Scholar] [Crossref]

32. Okofu, S. N., Akazue, M. I., Ajenaghughrure B. I., Efozia F. N. (2018). An Enhanced Speech Based Airline Ticket Reservation Data Confirmation Module, Journal of Social and Management Sciences 13 (1), 11-21 [Google Scholar] [Crossref]

33. IHAMA E. I., AKAZUE M.I. , OBAHIAGBON K.O., (2025b). A Survey of Smart City Development and the Role of Internet of Things, FUPRE Journal of Scientific and Industrial Research, 9(1):28-37(2025) [Google Scholar] [Crossref]

34. Akpoyibo, T. P., Akazue, M. I., & Ukadike, I. D. (2023). Development of a Floating Surface Water Robotic Oil Spillage Surveillance (Swross) System. Global Scientific Journal, 10(11), 2214 – 2230 [Google Scholar] [Crossref]

35. Asmus, P., Forsten, K., & Daniels, A. (2022). Next Wave of Grid Intelligence: Advanced Distribution Management Systems. Guidehouse Insights. [Google Scholar] [Crossref]

36. Giraldez, J., Diaz-Gonzalez, F., Gotseva, M., Gomis-Bellmunt, O., &Sumper, A. (2021). Testing Smart Distribution Grid Functionalities in a Hardware-in-the-Loop Environment. Applied Sciences, 11(3), 1280. [Google Scholar] [Crossref]

37. Jia, L., Yuchi, M., Yu, X., Gu, C., Li, R., & Dong, Z. Y. (2022). Detecting Cyber Intrusions in Distribution SCADA Systems Using Machine Learning Techniques. IEEE Transactions on Smart Grid, 13(3), 2326-2335. [Google Scholar] [Crossref]

38. Whaley, R. (2022). Progress in Smart Grid and Renewable Energy Integration Demonstrations. IEEE Power and Energy Magazine, 20(5), 19-28. [Google Scholar] [Crossref]

39. Erol-Kantarci, M., Mouftah, H. T., & Oktug, S. (2011). A survey of architectures and localization techniques for underwater acoustic sensor networks. IEEE Communications Surveys & Tutorials, 13(3), 487-502 [Google Scholar] [Crossref]

40. Mahmud, K., Hossain, M. J., & Ravishankar, J. (2019). Internet-of-things-enabled smart grid infrastructure supporting industry 4.0. IET Cyber-Physical Systems: Theory & Applications, 4(3), 260-268. [Google Scholar] [Crossref]

41. Yoro RE, Okpor MD, Akazue MI, Okpako EA, Eboka AO, Ejeh PO, et al. (2025) Adaptive DDoS detection mode in software defined SIP-VoIP using transfer learning with boosted meta-learner. PLoS One 20(6): e0326571. https://doi.org/10.1371/journal. pone.0326571 [Google Scholar] [Crossref]

42. Fortune Business Insights. (2022). Smart grid market size, share and industry analysis, By Software (Advanced Metering Infrastructure, Smart Grid Distribution Management, Smart Grid Network Management, Grid Asset Management), By Hardware (Smart Meters), By Service (Consulting, Deployment and Integration, Support and Maintenance), By End-User (Public Utility, Private Utility) and Regional Forecast, 2019 - 2026. Fortune Business Insights [Google Scholar] [Crossref]

43. Kezunovic, M., Ren, B., Hansen, M. R., Jacobson, D. A., Ehsani, M., Korunović, L. M., & Ghavami, M. (2022). Building blocks for smart and resilient distribution grid of the future: Texas A&M smart distribution grid testbed. Electric Power Systems Research, 207, 107743 [Google Scholar] [Crossref]

44. Akazue M. I., Ajenaghughrure I. B. (2016). Internet of Vehicle Speed Detection and Reporting System Based on RFID, Journal of the Nigerian Association of Mathematical Physics, Vol 34, pp219-226. [Google Scholar] [Crossref]

45. Ajenaghughrure I. B., Akazue M.I., Ekurume E. O.. (2015). Application of Fuzzy Logic in a Hybrid Intelligent Zero Tolerance Alcohol Influenced Vehicle Driving System, Science Focus, Vol. 20 No.2, pp. 35 – 45 [Google Scholar] [Crossref]

46. Akazue M. I. (2016a) An Automated Fuzzy Logic Based Temperature Control Model for Sustaining Server Room Temperature Stability. Journal of the Nigerian Association of Mathematical Physics, Vol 34, pp233-240 [Google Scholar] [Crossref]

47. Akazue, M. I. (2017) Users’ Perception in an Intelligent Automatic Fire Detection System for Developing Countries. Journal of Computer Science and its Applications Vol 24, No.2, pp 111 -119 [Google Scholar] [Crossref]

48. Akazue, M., Pirah, L. O., Ogeh, C., & Otegbalor, S. A. (2024a). DEVELOPMENT OF ENHANCED AGRICULTURAL GREENHOUSE SYSTEMS FOR DEVELOPING COUNTRIES. Nigerian Journal of Science and Environment, 22(1), Vol 22 (1) 68 –77. https://doi.org/10.61448/njse221246. [Google Scholar] [Crossref]

49. Akazue M. I. (2016b). A fuzzy based intelligent irrigation system. Science-Tech Journal, Vol1, No1, pp12-22. [Google Scholar] [Crossref]

50. Ukadike, I. D., Akazue, M., Omede, E., Akpoyibo, T. P (2023). Development of an IoT-based Air Quality Monitoring System, FUPRE Journal of Scientific and Industrial Research (FJSIR) Vol. 7 Number 4, pp 53-62 [Google Scholar] [Crossref]

51. Akazue Maureen I., and Ajenaghughrure Ighoyota Ben (2017)..Fuzzy Based Enhanced Smart Rest Room Automated Faucet System. Intl Journal of Engineering and Manufacturing, Vol. 7, No. 3 , pp 20-30. [Google Scholar] [Crossref]

52. Akazue, M., Esiri, K. H., & Clive, A. (2024b). Application of RFM model on Customer Segmentation in Digital Marketing. Nigerian Journal of Science and Environment, 22 (1) 57 –67. https://doi.org/10.61448/njse221245 [Google Scholar] [Crossref]

53. Ako, R. E., Aghware, F. O., Okpor, M. D., Akazue, M. I., Yoro, R. E., Ojugo, A. A., Setiadi, D. R. I. M., Odiakaose, C. C., Abere, R. A., Emordi, F. U., Geteloma, V. O., & Ejeh, P. O. (2024). Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost. Journal of Computing Theories and Applications, 2(1), 86–101. https://doi.org/10.62411/jcta.10562 [Google Scholar] [Crossref]

54. Ojie D., Akazue M., Imianvan A. (2023a). A Framework for Feature Selection using Data Value Metric and Genetic Algorithm, International Journal of Computer Applications, Volume 184, Issue 43, p14-21, doi:10.5120/ijca2023922533 [Google Scholar] [Crossref]

55. A. A. Ojugo, M. I. Akazue, P. O. Ejeh, N. C. Ashioba, C. C. Odiakaose, R. E. Ako, and F. U. Emordi (2023) Forging a User-Trust Hybrid Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study, Journal of Computing Theories and Applications, vol. 1, no. 2, page 1-11. [Google Scholar] [Crossref]

56. M. D. Okpor, F. O. Aghware, M. I. Akazue, A. A. Ojugo, F. U. Emordi, C. C. Odiakaose, R. E. Ako, V. O. Geteloma, A. P. Binitie, P. O. Ejeh (2024). Comparative Data Resample to Predict Subscription Services Attrition Using Tree-based Ensembles, Journal of Fuzzy Systems and Control, Vol. 2, No 2, 117-124, DOI: 10.59247/jfsc.v2i2.213. [Google Scholar] [Crossref]

57. Akazue, M., Esiri, K. H., & Clive, A. (2024b). Application of RFM model on Customer Segmentation in Digital Marketing. Nigerian Journal of Science and Environment, 22 (1) 57 –67. https://doi.org/10.61448/njse221245 [Google Scholar] [Crossref]

58. Ajenaghughrure, I., Sujatha, P., Maureen, A. (2017). Fuzzy based multi-fever symptom classifier diagnosis model. International Journal of Information Technology and Computer Science, Vol. 9, No. 10, pp. 13–28. DOI: 10.5815/ijitcs.2017.10.02. [Google Scholar] [Crossref]

59. Akazue, M.I and Onyekweli C.O, (2015) Survey of Automated Electricity Billing and Metering Systems: Acceptability and Implementability factors. Science Focus, Vol. 20 No.2, pp. 20 – 28. [Google Scholar] [Crossref]

60. Ali, I., Patterson, M., Rahman, S., Yao, L., & Wu, L. (2021). Australia’s transition towards smart metering: Issues, statuses, policies, and impacts on future grid. Renewable and Sustainable Energy Reviews, 135, 110105 [Google Scholar] [Crossref]

61. Ojie D.V, Akazue M, Omede E.U, Oboh E.O, Imianvan A. (May 2023b) Survival Prediction of Cervical Cancer Patients using Genetic Algorithm-Based Data Value Metric and Recurrent Neural Network, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307 (Online), Volume-13 Issue-2, May 2023 [Google Scholar] [Crossref]

62. Chen, Q., Zhang, X., & Sun, H. (2021). Application of Multi-source Heterogeneous Data Fusion for Power Equipment State Visualized Management. IOP Conference Series: Earth and Environmental Science, 787(2), 022032. [Google Scholar] [Crossref]

63. Dio T., M. I. Akazue, S. N. Okofu (November 2023). Development of an Online Examination Monitoring System using Zoom, International Journal of Computer Applications (0975 – 8887) Volume 185 – No. 40, pp 1 -10. [Google Scholar] [Crossref]

64. Chou, P. C., Guttromson, R. T., Bhatnagar, D., Lysiak, M. S., & Wang, J. (2020). Simulation-assisted distributed energy resource interconnection analysis. IEEE Transactions on Smart Grid, 12(1), 79-88 [Google Scholar] [Crossref]

65. Cardenas, J. A., Gemoets, L., Ablanedo Rosas, J. H., &Sarfi, R. (2014). A literature survey on Smart Grid distribution: An analytical approach. Journal of Cleaner Production, 65, 202--216. https://doi.org/10.1016/j.jclepro.2013.09.019 [Google Scholar] [Crossref]

66. Albraheem, L., Alajlan, H., Aljenedal, N., Alkhair, L. A., &Gwead, S. B. (2022). An IoT-based smart plug energy monitoring system. Computers & Electrical Engineering, 102, 108253. https://doi.org/10.1016/j.compeleceng.2022.108253. [Google Scholar] [Crossref]

67. Asamoah, A., Zhang, J., Ramos, C., Neto, J. P., Kyriakides, E., Granados, J. A. T., ... & Shahidehpour, M. (2022). Machine learning in smart-grid operations: A comprehensive overview. Transactions of the Institute of Measurement and Control, 01423312221109579. [Google Scholar] [Crossref]

68. Andren, F., Vukovic, O., Karedal, J., & Jonsson, F. (2022). Performance Analysis of Wireless Technologies for Smart Metering Communications in a Digital Twin Testbed. Technologies, 10(1), 2. [Google Scholar] [Crossref]

69. Arias, M. V., Rey, J. M., & Vizcaino, I. (2022). Smart grid development: A worldwide policy analysis. Electric Power Systems Research, 208, 107703. [Google Scholar] [Crossref]

70. Wan, C., Li, D., Liu, X., & Qiu, J. (2018). A general test platform for smart grid applications. Energies, 11(1), 230. [Google Scholar] [Crossref]

71. Ihama E.I., Akazue M.I. , Omede E., Ojie D. (2023). A Framework for Smart City Model Enabled by Internet of Things (IoT). International Journal of Computer Applications (0975 – 8887) Volume 185 – No. 6, May 2023, pp 6-11 [Google Scholar] [Crossref]

72. M.I. Akazue and B. O. Ojeme (2016). An RFID Based Driver’s License Authentication Model for Enhancing City Transport. The Journal of the Nigerian Institution of Production engineers, Vol 20, pp 282-294. [Google Scholar] [Crossref]

73. IHAMA, E. I., AKAZUE, M.I., & AMENAGHAWON, V. A. (2025a). Vehicular Movement Prediction Via Supervised Vector Machine, FUPRE Journal of Scientific and Industrial Research, 9(1):206-215 [Google Scholar] [Crossref]

74. S. N. Okofu, T. Anning-Dorson, & H. I. Duh (2025). Consumer Adoption and Continual Use of E-Vouchers: A Study of the Nigeria Telecommunication, International Research Journal of Multidisciplinary Scope, 06(02):330-342, DOI:10.47857/irjms.2025.v06i02.03788 [Google Scholar] [Crossref]

75. Okofu, S. N., Akazue, M. I., Ajenaghughrure B. I., Efozia F. N. (2018). An Enhanced Speech Based Airline Ticket Reservation Data Confirmation Module, Journal of Social and Management Sciences 13 (1), 11-21 [Google Scholar] [Crossref]

76. IHAMA E. I., AKAZUE M.I. , OBAHIAGBON K.O., (2025b). A Survey of Smart City Development and the Role of Internet of Things, FUPRE Journal of Scientific and Industrial Research, 9(1):28-37(2025) [Google Scholar] [Crossref]

77. Akpoyibo, T. P., Akazue, M. I., & Ukadike, I. D. (2023). Development of a Floating Surface Water Robotic Oil Spillage Surveillance (Swross) System. Global Scientific Journal, 10(11), 2214 – 2230 [Google Scholar] [Crossref]

78. Asmus, P., Forsten, K., & Daniels, A. (2022). Next Wave of Grid Intelligence: Advanced Distribution Management Systems. Guidehouse Insights. [Google Scholar] [Crossref]

79. Giraldez, J., Diaz-Gonzalez, F., Gotseva, M., Gomis-Bellmunt, O., &Sumper, A. (2021). Testing Smart Distribution Grid Functionalities in a Hardware-in-the-Loop Environment. Applied Sciences, 11(3), 1280. [Google Scholar] [Crossref]

80. Jia, L., Yuchi, M., Yu, X., Gu, C., Li, R., & Dong, Z. Y. (2022). Detecting Cyber Intrusions in Distribution SCADA Systems Using Machine Learning Techniques. IEEE Transactions on Smart Grid, 13(3), 2326-2335. [Google Scholar] [Crossref]

81. Whaley, R. (2022). Progress in Smart Grid and Renewable Energy Integration Demonstrations. IEEE Power and Energy Magazine, 20(5), 19-28. [Google Scholar] [Crossref]

82. Erol-Kantarci, M., Mouftah, H. T., & Oktug, S. (2011). A survey of architectures and localization techniques for underwater acoustic sensor networks. IEEE Communications Surveys & Tutorials, 13(3), 487-502 [Google Scholar] [Crossref]

83. Mahmud, K., Hossain, M. J., & Ravishankar, J. (2019). Internet-of-things-enabled smart grid infrastructure supporting industry 4.0. IET Cyber-Physical Systems: Theory & Applications, 4(3), 260-268. [Google Scholar] [Crossref]

84. Yoro RE, Okpor MD, Akazue MI, Okpako EA, Eboka AO, Ejeh PO, et al. (2025) Adaptive DDoS detection mode in software defined SIP-VoIP using transfer learning with boosted meta-learner. PLoS One 20(6): e0326571. https://doi.org/10.1371/journal. pone.0326571 [Google Scholar] [Crossref]

85. Fortune Business Insights. (2022). Smart grid market size, share and industry analysis, By Software (Advanced Metering Infrastructure, Smart Grid Distribution Management, Smart Grid Network Management, Grid Asset Management), By Hardware (Smart Meters), By Service (Consulting, Deployment and Integration, Support and Maintenance), By End-User (Public Utility, Private Utility) and Regional Forecast, 2019 - 2026. Fortune Business Insights [Google Scholar] [Crossref]

86. Kezunovic, M., Ren, B., Hansen, M. R., Jacobson, D. A., Ehsani, M., Korunović, L. M., & Ghavami, M. (2022). Building blocks for smart and resilient distribution grid of the future: Texas A&M smart distribution grid testbed. Electric Power Systems Research, 207, 107743 [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet