Blockchain Papers

Ahed Abugabah

No abstract is available for this record.

Jamal M. Al-Abdi, Adnan H. Al-Helali

The Internet of Things (IoT) is expected to interconnect more than 75 billion devices worldwide, yet device authenticity remains one of the most pressing unsolved security challenges in the IoT space. Typical IoT nodes have limited computing power, memory, and battery capacity, making traditional public-key-based authentication difficult to implement without compromising either security or resource conservation. This paper presents a structured narrative review and quantitative comparison of lightweight authentication protocols for IoT environments published between 2024 and 2026, spanning seven families: Elliptic Curve Cryptography (ECC)-based, ECC for Radio Frequency Identification (RFID), hash-based, Physical Unclonable Function (PUF)-based, biometric and behavioural, blockchain-assisted, and machine-learning-augmented protocols. The review adds message-level protocol-flow comparisons for representative ECC- and PUF-based schemes, a benchmarking table of published latency, message-size, and energy indicators, and five sector-specific case studies. Reported findings include dynamic-credential ECC schemes reducing communication and computational overhead by more than 37% over prior ECC schemes; PUF-based techniques using machine learning to improve modelling-attack resistance by more than 35% over earlier techniques; blockchain-assisted authentication for fog-enabled IoT; and multi-sector schemes such as SELAP, reducing computation and communication cost to 422 ms and 960 bits respectively, against 548 ms and 2048 bits for the earlier ELWSCAS protocol. Protocols are also examined against ephemeral information leakage, modelling attacks on PUFs, node cloning, and physical tampering. No protocol category is universally optimal; selection depends on a deployment's constraints, threat model, and sector. Research is converging on hybrid designs combining hardware-rooted trust, efficient public-key primitives, decentralised trust, and intelligent anomaly detection.

Weiqiang Chen, Zhiyao Zhao, Haisheng Li, Jiping Xu · 6 authors

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems.

Rafael Hoffmann, Carlos Moratelli, Alex S. R. Pinto

ABSTRACT Background Preserving the quality and safety of perishable products requires continuous monitoring and reliable traceability. Although the Internet of Things (IoT) enables real‐time data collection, multi‐organizational supply chains lack a common mechanism for assigning data custody while maintaining transparency, integrity, and performance. Objective This study proposes and evaluates an architecture integrating IoT, edge/fog computing, and hybrid storage—an off‐chain traditional database combined with a permissioned blockchain—to monitor and trace perishable products. Methods A prototype was implemented using IoT devices and simulators, edge and fog components, and hybrid storage. High‐volume sensor data and critical records were stored off‐chain in MongoDB, while their corresponding hashes were stored on‐chain using Hyperledger Fabric. Four controlled experiments assessed insertion response time, the impact of increasing sensors and edge devices, blockchain queue performance under burst workloads, and blockchain storage consumption. The hybrid approach was compared with MongoDB‐only and Hyperledger Fabric‐only storage. Results Hybrid storage achieved insertion up to six times faster than blockchain‐only storage. Response times increased with simultaneous requests and additional edge devices, while asynchronous ordered insertion prevented transaction conflicts during bursts. The prototype achieved 18.5 transactions per second, below the 65 estimated for an illustrative supply‐chain scenario. Blockchain storage grew approximately 8 MB per 100 records, reaching about 1 GB for 12,800 hashes. Conclusion The prototype demonstrates the feasibility of combining off‐chain storage, permissioned blockchain records, and edge/fog processing to provide verifiable traceability while reducing on‐chain load. Larger‐scale, real‐world evaluations and storage‐management strategies remain necessary.

Agnes Nalini Vincent, Nassirah Laloo, Mohammad Sameer Sunhaloo, Uhoze Bagurubumwe

Digital elevation model (DEM)-based terrain analysis is an important geographic information system (GIS) methodology that serves as the core aspect for spatial analysis applications in geomorphological studies. These analyses can be performed using cloud-based platforms like Google Earth Engines or ArcGIS online, or using a local GIS platform called quantum GIS (QGIS). The resulting terrain data must be disseminated. Geospatial data sharing and dissemination are crucial for promoting cooperation, effectiveness, efficiency, and optimized decision-making in a variety of industries. Geospatial terrain data plays a crucial role in site selection and industrial planning, automated logistics, autonomous vehicle navigation, and infrastructure resilience in manufacturing ecosystems. However, existing literature states that traditional systems lack mechanisms to detect tampering in elevation models, land surveys, or hydrological data. Because of this, manufacturing systems face risks such as flawed factory site selection, disrupted supply routes, or unsafe autonomous vehicle navigation, data tampering in production logs, 278 counterfeit parts in supply chains, and a lack of real-time traceability. Hence, to manage the limitations of conventional terrain data storage and handling, this study proposes a blockchain-based framework to secure QGIS-processed terrain data, ensuring immutability and traceability for smart manufacturing applications. Blockchain distributed ledgers can permanently store high-resolution terrain data, minimize the chance of unintended alterations, and promote transparent, unrestricted collaboration. Using the country of the Republic of Mauritius as a case study toward tropical island states, this work demonstrated how elevation, slope, and aspect data extracted via QGIS can be securely stored and verified on a distributed ledger. Furthermore, this study incorporates an integration layer into the framework. The purpose of this integration layer is to enable real-time terrain alerts, smart contract-driven compliance checks, and to arrive at closed-loop feedback from IoT sensors. Thus, this proposed framework bridges blockchain-secured terrain data with manufacturing execution systems (MES) and IoT-enabled logistics networks.