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<p>Electro-medical waste (EMW) management presents critical challenges related to traceability, regulatory compliance, and operational safety in healthcare environments, where improper handling can pose serious risks to public health and the environment. To address these challenges, this work proposes a blockchain-integrated Internet of Things framework, termed BIOT-EMW, which combines IoT sensing, blockchain-based auditability, and edge-level intelligence to enable secure and transparent EMW lifecycle management. A convolutional neural network-based computer vision module is deployed at the edge to automate EMW classification and reduce manual intervention. The performance evaluation of the proposed BIOT-EMW system emphasizes system-level operational metrics, including latency, delay, bandwidth, power consumption, resource utilization, and scalability, rather than exhaustive model-level benchmarking. Experimental results show that edge-level processing latency ranges from 3.45 to 9.33&#xa0;ms, end-to-edge communication delay from 6.43 to 12.11&#xa0;ms, bandwidth usage from 24.54 Mbps to 34.87 Mbps, and device-level power consumption from 6.5 to 13.31&#xa0;mW, with resource utilization between 78 and 92% and scalability reaching up to 97%. In a prototype-scale experimental testbed, the BIOT-EMW framework shows feasible and efficient operation with promising scalability for automated electro-medical waste management in smart healthcare facilities.</p>

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A blockchain-enabled IoT framework for smart electro-medical waste management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • K. Suresh Kumar,
  • T. Ananth Kumar,
  • Christo Ananth,
  • Mathiyazhagan Narayanan,
  • Hari Mohan Rai,
  • Saurabh Agarwal,
  • Wooguil Pak

摘要

Electro-medical waste (EMW) management presents critical challenges related to traceability, regulatory compliance, and operational safety in healthcare environments, where improper handling can pose serious risks to public health and the environment. To address these challenges, this work proposes a blockchain-integrated Internet of Things framework, termed BIOT-EMW, which combines IoT sensing, blockchain-based auditability, and edge-level intelligence to enable secure and transparent EMW lifecycle management. A convolutional neural network-based computer vision module is deployed at the edge to automate EMW classification and reduce manual intervention. The performance evaluation of the proposed BIOT-EMW system emphasizes system-level operational metrics, including latency, delay, bandwidth, power consumption, resource utilization, and scalability, rather than exhaustive model-level benchmarking. Experimental results show that edge-level processing latency ranges from 3.45 to 9.33 ms, end-to-edge communication delay from 6.43 to 12.11 ms, bandwidth usage from 24.54 Mbps to 34.87 Mbps, and device-level power consumption from 6.5 to 13.31 mW, with resource utilization between 78 and 92% and scalability reaching up to 97%. In a prototype-scale experimental testbed, the BIOT-EMW framework shows feasible and efficient operation with promising scalability for automated electro-medical waste management in smart healthcare facilities.