Anuar Bayakhmetov

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based system design for medical waste management that integrates: (i) Espressif Systems 32 (ESP32)-based edge devices for fill-level and Global Positioning System (GPS) telemetry; (ii) secure network communication; (iii) a cloud backend for data ingestion, storage, and analytics; and (iv) operator dashboards with event-driven alerting. The architecture extends our prior GPS-enabled tracking and route optimization by adding sensor-driven state monitoring, threshold-based decision support, and a time-series data pipeline designed for future AI-driven predictive analytics. In a 30-day pilot with five containers, the system collected one reading every 15 min (14,400 total readings). The backend demonstrated efficient processing with an average Application Programming Interface (API) response time of 45 ms, sub-50 ms database write latency, and high uptime; alerts were delivered promptly upon threshold violations. Compared with a fixed-schedule baseline, the system enabled condition-based collection scheduling with zero data loss. The proposed design emphasizes modularity, fault tolerance, and integration readiness for hospital information systems, providing a practical blueprint for scalable smart-healthcare waste logistics and a foundation for machine learning-based predictive waste management.

Medical waste management is a growing concern in Kazakhstan. Despite the presence of a regulatory framework, the current medical waste disposal system suffers from fragmentation, lack of transparency, and inefficient communication between stakeholders. These limitations result in illegal dumping, environmental pollution, and increased health risks. This paper presents the development and validation of an integrated Internet of Things (IoT)-based system designed to optimize and automate the monitoring, collection, and disposal of medical waste. The proposed architecture includes Global Positioning System (GPS) tracking, real-time sensor monitoring, cloud data analytics, and predictive routing algorithms, enabling efficient logistics and regulatory compliance. Utilizing a microcontroller and sensors, the system continuously transmits data to a centralized server for monitoring. Experimental deployments across urban and suburban routes in the Zhambyl region demonstrate that the system achieves a Circular Error Probable (CEP₅₀) of 11 m and a 95% positioning accuracy within 23 m, which aligns acceptably with the requirements for city-level route optimization. Statistical analysis confirms that the observed positioning accuracy is consistent with an urban propagation model and adequate for municipal dispatching, though it remains below automotive-grade precision. The system is further supported by a robust power supply solution, allowing up to 49 h of autonomous operation.