Haswika, H.
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Digitizing Waste Management Using the Internet of Things: Research Opportunities Auliana, Windi; Qurtubi, Q.; Haswika, H.; Worasan, Kongkidakhon; Shbool, Mohammad A.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15696

Abstract

This study proposes an integrative multi-layered framework to address fragmentation in industrial waste management. The increasing volume of industrial waste creates an urgent need for a more precise, adaptive, and sustainable control system, as current practices often lack sufficient integration to ensure full environmental accountability. A critical gap exists in the lack of integration between real-time technical data and strategic governance, which hinders "intelligent compliance" in industrial settings. This research aims to identify trends, thematic scope, and research opportunities in IoT-based production waste control. The specific contribution of this study is the proposal of an integrative multi-layered framework that synchronizes monitoring, intelligent analytics, and blockchain-based accountability. The method was a PRISMA-based systematic review, search queries including 'IoT', 'Industrial Waste', and 'Blockchain' were applied to the Scopus database. 37 high-impact articles were selected based on three criteria: (1) industrial waste focus, (2) integration of Industry 4.0 pillars (AI, Blockchain, or 5G), and (3) publication within 2020–2025. Focusing on current system maturity over historical protocol evolution, this period reflects the state-of-the-art technological convergence. A rigorous Scopus screening narrowed 147 publications to 37 articles, enabling targeted qualitative synthesis. The results categorize IoT roles into thematic clusters: monitoring, process optimization, and circular economy integration. While promising, challenges such as data interoperability and security costs remain significant. This framework provides a blueprint for automated compliance. Future research should validate this model through cross-industry case studies. Study limitations include the reliance on a single database and the rapidly evolving nature of IoT technologies.