Volume no :
|
Issue no :
Article Type :
Author :
Mr.K.Murugan, M.Harishma, A.Imayasri
Published Date :
Publisher :
Page No: 1 - 10
Abstract : An AI-driven, IoT-empowered autonomous public toilet maintenance system is a cutting-edge solution aimed at transforming urban sanitation. By integrating advanced IoT sensors and artificial intelligence, the system monitors real-time parameters such as occupancy, air quality, water levels, and cleanliness. This data is analyzed using AI algorithms to optimize cleaning schedules, predict maintenance needs, and manage resources efficiently. Automated operations, including flushing, surface cleaning, and deodorizing, significantly reduce the need for manual intervention. The system also enhances hygiene by incorporating touchless features and self-sanitizing mechanisms, promoting a safer user experience. Additionally, it supports sustainability by minimizing water and energy usage and facilitating effective waste disposal. Designed for smart city integration, it can be connected to municipal dashboards for remote monitoring and decision-making. AI-based feedback loops further ensure continuous improvement by analyzing user inputs and operational data. By ensuring cleaner facilities, reducing resource wastage, and lowering operational costs, this autonomous maintenance system addresses the growing demand for sustainable public hygiene solutions. It not only improves the overall user experience but also contributes to public health and environmental conservation. As urban populations grow, this technology-driven approach will play a vital role in delivering efficient and hygienic sanitation services, making it an essential component of future-ready cities. The system exemplifies how artificial intelligence and the Internet of Things can be leveraged to enhance quality of life while supporting broader goals in smart infrastructure and sustainable development.
Keyword Intelligent Transportation System (ITS), Vehicular Ad-hoc Network (VANET), Low-Latency Communication, Network Congestion Management, Artificial Intelligence in Transportation, Cybersecurity in V2V Communication.
Reference:
  1. Lokman and R. K. Ramasamy, “Scheduling and Predictive Maintenance for Smart Toilet,” in Proc. IEEE, 2023.
  2. R. K. Ramasamy, V. Rajendran, and S. Murthy, “Smart Toilet: An IoT Implementation for Optimization of Resources,” Tech. Rep., Jul. 2018.
  3. S. E. Abney, K. R. Bright, J. McKinney, M. K. Ijaz, and C. P. Gerba, “Toilet Hygiene—Review and Research Needs,” J. Appl. Microbiol., vol. 130, no. 3, pp. 719–735, 2021.
  4. S. A. Parab, K. K. Meher, T. A. Patil, V. T. Badhe, and R. R. Gite, “E-Swachh Public Toilet Monitoring System,” Int. Res. J. Eng. Technol. (IRJET), vol. 7, no. 5, pp. 4031–4035, 2020.
  5. P. Deshmukh, A. Mohite, and H. Bhoir, “Intelligent Public Toilet Monitoring System Using IoT,” in Proc. IEEE Bangalore Humanitarian Technology Conf. (B-HTC), 2020, pp. 1–5.
  6.  P. Dhamale, S. Singh, S. Zadane, and M. Bhelande, “Smart Toilet Monitoring System Using IoT,” Int. J. Comput. Trends Technol. (IJCTT), vol. 68, no. 7, pp. 42–46, 2020.
  7. [K. Shafique, B. A. Khawaja, and F. Sabir, “Internet of Things (IoT) for Next-Generation Smart Systems: Future Trends and Prospects for Emerging 5G-IoT Scenarios,” IEEE Access, vol. 7, pp. 63063–63087, 2019.
  8. Deepa, R., Karthick, R., Velusamy, J., & Senthilkumar, R. (2025). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, 92, 103934.
  9. Senthilkumar Ramachandraarjunan, Venkatakrishnan Perumalsamy & Balaji Narayanan 2022, ‘IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques’, in Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2853-2868
  10. Senthilkumar, Dr.P.Venkatakrishnan, Dr.N.Balaji, Intelligent based novel embedded system based IoT Enabled air pollution monitoring system, ELSEVIER Microprocessors and Microsystems Vol.77, June 2020
  1. Prova, N. N. I. (2024, August). Advanced Machine Learning Techniques for Predictive Analysis of Health Insurance. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)(pp. 1166-1170). IEEE.
  2. Prova, N. N. I. (2024, October). Improved Solar Panel Efficiency through Dust Detection Using the InceptionV3 Transfer Learning Model. In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC)(pp. 260-268). IEEE.
  3. Devi, K., & Indoria, D. (2024). Impact of Russia-Ukraine War on the Financial Sector of India. Drishtikon: A Management Journal15(1).
  4. Devi, K., & Indoria, D. (2021). Role of Micro Enterprises in the Socio-Economic Development of Women–A Case Study of Koraput District, Odisha. Design Engineering, 1135-1151.
  5. Indoria, D. (2021). AN APPLICATION OF FOREIGN DIRECT INVESTMENT. BIMS International Research Journal of Management and Commerce6(1), 01-04.
  6. Devi, K., & Indoria, D. (2020). A STUDY ON ONLINE BANKING AND ITS EFFECT ON THE FINANCIAL BEHAVIOUR: A SPECIAL REFERENCE TO JEYPORE TOWN OF ODISHA. International Journal of Management (IJM)11(1).
  7. Indoria, D., Kiran, P. N., Kumar, A., Goel, M., Shelke, N. A., & Singh, J. (2023, November). Artificial intelligence and machine learning in human resource management and market research for enhanced effectiveness and organizational benefits. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)(pp. 1135-1140). IEEE.
  8. Indoria, D., & Devi, K. (2021). An Analysis On The Consumers Perception Towards Upi.
  9. Velayudham, A., Karthick, R., Sivabalan, A., & Sathya, V. (2025). IoT enabled smart healthcare system for COVID-19 classification using optimized robust spatiotemporal graph convolutional networks. Biomedical Signal Processing and Control100, 107104.
  10. Gayathri, P., Balamurugan, J., Gowthami, M., Usha, R., Karthick, R., & Selvan, R. S. (2025). Factors Influencing Customers’ Inclination to buy Green Products: An Indian Perspective. In Elevating Brand Loyalty With Optimized Marketing Analytics and AI(pp. 185-202). IGI Global Scientific Publishing.
  11. Ramkumar, G., Bhuvaneswari, J., Venugopal, S., Kumar, S., Ramasamy, C. K., & Karthick, R. (2025). Enhancing customer segmentation: RFM analysis and K-Means clustering implementation. In Hybrid and Advanced Technologies(pp. 70-76). CRC Press.
  12. Tamilselvi, M., Kalaivani, S. S. S., Sunderasan, V., Sailaja, K., Gopal, D., & Karthick, R. (2025). Deep learning for object detection and identification. In Hybrid and Advanced Technologies(pp. 218-223). CRC Press.
  13. Kalaiselvi, B., & Thangamani, M. (2020). An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques. Measurement162, 107885.
  14. Prabhu Kavin, B., Karki, S., Hemalatha, S., Singh, D., Vijayalakshmi, R., Thangamani, M., … & Adigo, A. G. (2022). Machine learning‐based secure data acquisition for fake accounts detection in future mobile communication networks. Wireless Communications and Mobile Computing2022(1), 6356152.
  15. Geeitha, S., & Thangamani, M. (2018). Incorporating EBO-HSIC with SVM for gene selection associated with cervical cancer classification. Journal of medical systems42(11), 225.
  16. Thangamani, M., & Thangaraj, P. (2010). Integrated Clustering and Feature Selection Scheme for Text Documents. Journal of Computer Science6(5), 536.
  17. Gangadhar, C., Chanthirasekaran, K., Chandra, K. R., Sharma, A., Thangamani, M., & Kumar, P. S. (2022). An energy efficient NOMA-based spectrum sharing techniques for cell-free massive MIMO. International Journal of Engineering Systems Modelling and Simulation13(4), 284-288.
  18. Narmatha, C., Thangamani, M., & Ibrahim, S. J. A. (2020). Research scenario of medical data mining using fuzzy and graph theory. International Journal of Advanced Trends in Computer Science and Engineering9(1), 349-355.
  19. Thangamani, M., & Thangaraj, P. (2013). Fuzzy ontology for distributed document clustering based on genetic algorithm. Applied Mathematics & Information Sciences7(4), 1563-1574.
  20. Surendiran, R., Aarthi, R., Thangamani, M., Sugavanam, S., & Sarumathy, R. (2022). A Systematic Review Using Machine Learning Algorithms for Predicting Preterm Birth. International Journal of Engineering Trends and Technology70(5), 46-59.
  21. Thangamani, M., & Thangaraj, P. (2010). Ontology based fuzzy document clustering scheme. Modern Applied Science4(7), 148.
  22. Ibrahim, S. J. A., & Thangamani, M. (2018, November). Momentous Innovations in the prospective method of Drug development. In Proceedings of the 2018 International Conference on Digital Medicine and Image Processing(pp. 37-41).
  23. Ramesh, T. R., Lilhore, U. K., Poongodi, M., Simaiya, S., Kaur, A., & Hamdi, M. (2022). Predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132-148.
  24. Ramesh, T. R., Vijayaragavan, M., Poongodi, M., Hamdi, M., Wang, H., & Bourouis, S. (2022). Peer-to-peer trust management in intelligent transportation system: An Aumann’s agreement theorem based approach. ICT Express8(3), 340-346.
  25. Ramesh, T. R., & Kavitha, C. (2013). Web user interest prediction framework based on user behavior for dynamic websites. Life Sci. J10(2), 1736-1739.
  26. Ali, A., Naeem, S., Anam, S., & Ahmed, M. M. (2022). Machine learning for intrusion detection in cyber security: Applications, challenges, and recommendations. UMT Artif. Intell. Rev2(2), 41-64.
  1. Ramesh, T. R., Raghavendra, R., Vantamuri, S. B., Pallavi, R., & Easwaran, B. (2023). IMPROVING THE QUALITY OF VANET COMMUNICATION USING FEDERATED PEER-TO-PEER LEARNING. ICTACT Journal on Communication Technology14(1).
  1. Jayapandiyan, J. R., Kavitha, C., & Sakthivel, K. (2020). Enhanced least significant bit replacement algorithm in spatial domain of steganography using character sequence optimization. Ieee Access8, 136537-136545.
  2. Sakthivel, K., Jayanthiladevi, A., & Kavitha, C. (2016). Automatic detection of lung cancer nodules by employing intelligent fuzzy c-means and support vector machine. BIOMEDICAL RESEARCH-INDIA27, S123-S127.
  3. Sakthivel, K., Nallusamy, R., & Kavitha, C. (2014). Color image segmentation using SVM pixel classification image. World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering8(10), 1924-1930.
  4. Jayapandiyan, J. R., Kavitha, C., & Sakthivel, K. (2020). Optimal secret text compression technique for steganographic encoding by dynamic ranking algorithm. In Journal of Physics: Conference Series(Vol. 1427, No. 1, p. 012005). IOP Publishing.
  5. Sakthivel, K., Abinaya, R., Nivetha, I., & Kumar, R. A. (2014). Region based image retrieval using k-means and hierarchical clustering algorithms. International Journal of Innovative Research in Science, Engineering and Technology3(1), 1255-1260.
  6. Gopinath, S., Sakthivel, K., & Lalitha, S. (2022). A plant disease image using convolutional recurrent neural network procedure intended for big data plant classification. Journal of Intelligent & Fuzzy Systems43(4), 4173-4186.
  7. Bharathi, V., & Sakthivel, K. (2022). Unmanned mobile robot in unknown obstacle environments for multi switching control tracking using adaptive nonlinear sliding mode control method. Journal of Intelligent & Fuzzy Systems43(3), 3513-3525.
  8. Sakthivel, K., Nallusamy, R., & Kavitha, C. (2014). Image retrieval using fused features. World Academy of Science, Engineering and Technology International Journal of Computer, Information, Systems and Control Engineering8(9).
  9. Kavitha, C., Krishnan, A., & Sakthivel, K. (2005, January). Similarity based retrieval of image database: using dynamic clustering. In Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, 2005.(pp. 147-151). IEEE.
  10. Sakthivel, K., Ravichandran, T., & Kavitha, C. (2009). Performance analysis in image retrieval using IRM and K-Means Algorithm. i-Manager’s Journal on Software Engineering3(4), 55.
  11. Sidharth, S. (2023). AI-Driven Anomaly Detection for Advanced Threat Detection.
  12. Sidharth, S. (2023). Homomorphic Encryption: Enabling Secure Cloud Data Processing.
  13. Sidharth, S. (2024). Strengthening Cloud Security with AI-Based Intrusion Detection Systems.
  14. Sidharth, S. (2022). Enhancing Generative AI Models for Secure and Private Data Synthesis.
  1. Sidharth, S. (2021). Multi-Cloud Environments: Reducing Security Risks in Distributed Architectures.