Cost-Optimised IoT Architecture for Real-Time E-Waste Monitoring with Operational Validation

Authors

  • Belinda Ndlovu National University of Science and Technology, Zimbabwe
  • Zvinodashe Revesai Reformed Church University, Zimbabwe
  • Kudakwashe Maguraushe University of South Africa, South Africa
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DOI:

https://doi.org/10.63158/journalisi.v8i2.1553

Keywords:

IoT architecture, E-waste monitoring, Cost-performance analysis, Sensor-based waste monitoring, Deployment validation, Smart city waste management

Abstract

Electronic waste (e-waste) is the fastest-growing solid waste stream worldwide, yet formal collection systems remain limited. Many existing Internet of Things (IoT) solutions emphasize advanced functionality at the expense of cost efficiency and practical deployability. This paper presents a cost-optimized IoT architecture for real-time monitoring of e-waste bins. The proposed system adopts a four-layer architecture integrating ESP32 microcontrollers, ultrasonic sensors for fill-level detection, and infrared sensors for monitoring, supported by a Node.js backend that provides real-time data updates. System validation was conducted through sensor calibration (n = 30), functional testing, stress testing, and cost-performance benchmarking against RFID-, GSM-, and LoRa-based alternatives. Experimental results demonstrate a fill-level accuracy of ±3.2%, temperature precision of ±1.8°C, system reliability of 97.3%, uptime of 98.7%, and an average latency of 2.1 s. The deployment cost was USD 78 per bin, which is approximately 40% lower than comparable RFID-based systems. In addition, the system reduced unnecessary collection trips by 35% and yielded an estimated return on investment (ROI) of 8.5 months. These results show that a low-complexity, cost-efficient IoT design can provide a scalable and practical solution for e-waste bin monitoring.

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Published

2026-04-12

Issue

Vol. 8 No. 2 (2026): April

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How to Cite

[1]
B. Ndlovu, Z. Revesai, and K. Maguraushe, “Cost-Optimised IoT Architecture for Real-Time E-Waste Monitoring with Operational Validation”, journalisi, vol. 8, no. 2, pp. 1590–1616, Apr. 2026, doi: 10.63158/journalisi.v8i2.1553.

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