The supply chains of smart cities are rapidly becoming multifaceted, interconnected ecosystems with numerous stakeholders and high-frequency transactions that require transparency, accountability, and operational resilience. Although digital management platforms are becoming increasingly popular, most of the available platforms do not have end-to-end trust, effective data validation, or scalability among the range of actors. Blockchain technology provides traceability and immutability; however, its high energy consumption and lack of a high level of confirmation make it impossible to scale its extensive use in real-world supply chain settings. This study presents an IoT-blockchain framework (three layers) to overcome these issues, including edge-based data filtration and a permissioned blockchain with a proof-of-authority (PoA) consensus mechanism. The IoT layer captures, authenticates, and filters transactional data, followed by safe on-chain recording. Smart contracts enforce regulatory compliance, anomaly detection, and real-time verification. Two complementary algorithms were created so that the participants, onboard, transaction integrity, and compliance scoring could be performed reliably. The framework was tested on Raspberry Pi validators and synthetic and real mixed datasets. It was found that, in experimental settings, the average confirmation delay was 105±8 ms, and the sustained throughput was greater than 420 transactions per second, and over 90% tamper detection accuracy under realistic smart-city operating conditions. even in a replay and Sybil attack environment. These results indicate the best compromise among security, transparency, and energy efficiency, which makes the proposed model a scalable and sustainable solution for digital auditing, urban logistics, and improvements in consumer trust in smart city supply chain ecosystems. • Develop an analytical framework combining Internet of Things and blockchain for supply chain analytics. • Enhance supply chain transparency through secure data recording and real-time validation. • Improve operational efficiency by reducing transaction latency and energy consumption. • Strengthen decision-making using data filtration and edge-based analytics for scalable performance. • Support sustainability through intelligent monitoring, traceability, and stakeholder accountability.
Randhawa et al. (2026) studied this question.