ArticleFrontiers in public health2026
A Blockchain-Enhanced Neural Network Framework for secure e-waste forecasting in smart cities.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
The exponential growth of electronic waste (e-waste) represents one of the most critical environmental challenges faced by contemporary urban systems. Accurate forecasting of e-waste generation is essential for supporting sustainable decision-making within smart cities. This study proposes the Blockchain-Enhanced Neural Network Framework (BENNF), a conceptual and architectural framework designed to enable secure, transparent, and scalable e-waste forecasting through the integration of artificial intelligence, big data analytics, and blockchain technology. The proposed framework is structured into three interconnected layers: (i) a data acquisition and preprocessing layer based on big data pipelines (Apache Spark and Hadoop); (ii) a prediction layer employing multilayer neural networks trained on socioeconomic and environmental variables; and (iii) a blockchain layer that ensures data integrity, transparency, and traceability through smart contracts and a Proof-of-Authority consensus mechanism. Unlike fully deployed empirical systems, BENNF is presented as a system-level and design-oriented framework, aimed at strengthening digital governance and trust in data-driven environmental management. The framework aligns with the Sustainable Development Goals (SDGs) 12 and 13, promoting responsible consumption, circular economy principles, and climate-resilient urban planning. Its potential applicability in urban contexts such as Guayaquil, Ecuador, highlights its scalability and relevance for sustainable smart city initiatives.
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