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Denih, A., Matsumoto, T., Rachman, I., Putra, G.R., Anggraeni, I., Kurnia, E., et al. (2025) Identification of Plastic Waste with Unmanned Aerial Vehicle (UAV) Using Deep Learning and Internet of Things (IoT). Journal of Hazardous Materials Advances, 18, Article 100622.
https://doi.org/10.1016/j.hazadv.2025.100622

has been cited by the following article:

• TITLE: Critical Systematic Review of the Transferability of IoT Solutions for Smart Waste Collection in Low-Resource Settings

  AUTHORS: Didi Tshimpanga Badibanga, Olivier Baraka Mushage, Audace Manirabona, Jérémie Ndikumagenge

  KEYWORDS: IoT Technologies, Smart Waste Collection, Environmental Challenges, Low-Resource Settings

  JOURNAL NAME: Advances in Internet of Things, Vol.15 No.4, October 28, 2025

  ABSTRACT: This study assesses the transferability of IoT solutions applied to smart waste collection in low-resource settings. In line with PRISMA guidelines, a systematic review covering the period 2016-2024 was conducted across four databases (IEEE Xplore, ScienceDirect, SpringerLink, Scopus). Out of 329 records, 36 studies were included. Results highlight the predominance of ultrasonic sensors (~46%), the widespread use of Arduino/ESP32 (~72%) and Wi-Fi (~27%) despite their limited robustness in low-connectivity environments, while LoRa/LoRaWAN (~20%) is gaining ground. Community engagement remains marginal, with about 60% of the systems lacking any user interaction. The most transferable solutions are those that combine technical simplicity, low-power architectures, and lightweight interfaces (USSD/SMS), together with context-specific route optimization. It is therefore recommended to prioritize open, modular, and locally tested systems, to strengthen community involvement, and to align pilot projects with municipal policies.