The proposed BlockSafeNet framework achieved significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures, providing a positive impact on the SC ecosystem.
Abstract
Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem.
Overall, this review demonstrates that blockchain-based cybersecurity frameworks provide a secure, transparent, and resilient foundation for protecting smart digital environments against increasingly sophisticated cyber threats while supporting trustworthy and scalable digital transformation.
M. Kayla, Crispinus Ode, Marion Sanaipei· The Eastasouth Journal of In...· 0 citations
The smart grid systems are increasingly becoming digital where the system is taking a new direction into greater operational efficiency and real time energy management. The increased number of Internet of Things (IoT) devices and communication networks has widened the area of attack as well, thus smart grids are vulnerable to more sophisticated cyber attacks like false data injection, denial-of-data manipulation and service attacks. The traditional centralized security controls are not sufficient because of the limitation of single point failure, scalability limitations, and demand decentralized and resilient security controls. The use of blockchain technology in this regard has become a potential solution in order to improve the level of security by utilizing the transparent, decentralized, and immutable features of the technology. To provide cybersecurity approaches of smart grid systems, a taxonomical structure of blockchain technology is proposed in this paper. Besides that it shows the categorization of the various techniques into secure data management, secure energy trading, device authentication, data integrity assurance and communication protection. A detailed analysis of existing works was undertaken to determine their efficiency based on the security, scale and complexity of implementation. The results of the review describe some of the major challenges, such as latency and energy overhead and provide future research opportunities on effective smart grid security frameworks.
F. Basheer, Hari Gobind Pathak, Meena Malik et al.· International Conference on...· 0 citations
The rapid evolution of Internet of Things (IoT) ecosystems produces large quantities of heterogeneous data streams, resulting in major issues related to security, scalability, privacy, and real-time data analytics. This paper suggests a novel framework of a blockchain-based and AI-oriented big data analytics system that supports secure and scalable IoT-based smart grid environments. The proposed architecture combines data acquisition using IoT, big data processing using a distributed system, intelligent predictions using machine learning techniques such as LightGBM, Random Forest, and XGBoost, and integrity preservation using a blockchain-based system, such as Hyperledger Fabric. In the proposed system, the Energy Efficiency Scores are predicted using advanced regression techniques, and LightGBM performs better with the least MAE of 0.4512, MSE of 0.325, RMSE of 0.5701, and the highest R² of 0.9988. To guarantee data immutability and trust, prediction records are cryptographically hashed using SHA-256 hashing and stored on a blockchain ledger, while sensitive payloads are kept off-chain for privacy preservation purposes. Experimental results show that accuracy is improved, residual variance is reduced, and system stability is improved. This proposed framework successfully leverages decentralized trust, intelligent analytics, and scalable processing to deliver a powerful tool for secure, transparent, and privacy-preserving IoT-based energy management systems.
Hema Malini G.B., Agnes Sheila S.P, Anitha S et al.· International Journal of Ele...· 0 citations
The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs.
Marbiyat Tahir Gidado, Bashiru Abdulganiyu, Mohammed Nasir Musa et al.· Journal of Advanced Science...· 0 citations
Smart cities increasingly depend on large-scale Internet of Things (IoT) infrastructures for traffic management, smart grids, and environmental monitoring. Ensuring data integrity, transparency, and privacy in such systems remains a major challenge because centralized platforms are vulnerable to manipulation, while conventional blockchain-based solutions suffer from scalability and confidentiality limitations. This study proposes TrustIoT-Chain, a privacy-preserving blockchain framework that integrates off-chain digital twins, cryptographic data commitments, zero-knowledge compliance verification, and a sharded blockchain architecture for scalable smart city monitoring. The objective of this work is to provide real-time verifiable IoT monitoring with strong privacy guarantees and high system throughput. Large-scale simulations with one million synthetic IoT events demonstrate that the proposed framework achieves up to 24,910 events/s throughput with an average verification latency of 410 ms using 16 shards. Energy consumption is reduced by approximately 45% compared with non-sharded blockchain systems with zeroknowledge proofs, while privacy leakage measured by mutual information decreases to 0.05 bits. The key novelty lies in the joint integration of the digital twins with the blockchain-based zero-knowledge auditing, and sharding for the smart city IoT systems. This approach enables transparent regulatory compliance verification without exposing the raw sensor data, offering the scalable, and privacy-aware foundation for the future smart city governance, and trusted IoT ecosystems.
Shrutika Khobragade, J. Bakal· 2026 7th International Confe...· 0 citations
BELS-IoT is proposed, a novel decentralized protection architecture that integrates a cryptocurrency-based blockchain layer with a multi-layer ensemble learning engine that rewards honest behavior and penalizes malicious activities while maintaining privacy through federated learning with blockchain-verified reputation scores.
Anwar Kalghoum, Leila Azouz Saidane· SN Computer Science· 0 citations
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