Smart City Gnosys
Smart city article details
| Title | Securing Smart Cities: Privacy-Preserving Iot With Optimized Gated Lounge Attention Dropout Mechanism |
|---|---|
| ID_Doc | 47789 |
| Authors | Alshmrany S. |
| Year | 2025 |
| Published | Neural Computing and Applications, 37, 18 |
| DOI | http://dx.doi.org/10.1007/s00521-025-11110-y |
| Abstract | The emerging use of Internet of Things (IoT) raises concerns about the security and privacy of personal data. With numerous devices collecting sensitive information, safeguarding privacy in large urban infrastructures is challenging. Blockchain technology provides a solution by enabling secure, decentralized data transmission, ensuring data integrity through its immutability and transparency. Sustainable cities, which rely on IoT for efficient resource management, require robust privacy protection to maintain trust and ensure the success of IoT-based innovations.However, conventioanlapproaches strugglesinprocessinghuge data while ensuring privacy protection within IoT networks. This paper proposes a novel approach, the Optimized Gated Lounge Attention Dropout (OGLD) mechanism, aimed at improving privacy preservation in smart IoT infrastructures, particularly in sustainable cities. As urban areas increasingly integrate IoT devices for enhanced resource management, ensuring the security and privacy of collected data becomes paramount. To address these challenges, the OGLD method is combined with blockchain technology, enabling decentralized and secure data transmission. This integration safeguards sensitive data from unauthorized access while maintaining reliable communication between devices. Additionally, several preprocessing techniques are incorporated in the OGLD approach to standardize raw data, reducing noise and inconsistencies, and enhancing overall model performance. A Crossover strategy-based Coati optimization method is utilized to fine-tune model parameters, improving adaptability to varying data characteristics and increasing the effectiveness. The effectiveness of the OGLD approach through multiple performance metrics, is evaluated demonstrating an accuracy of 98.56%. The results indicate that the OGLD method outperforms existing techniques, showcasing its superior capability in securing privacy within smart IoT frameworks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. |
| Author Keywords | Blockchain technology; Coati optimization technique; IoT; Optimized gated lounge attention dropout mechanism; Privacy-preserving; Sustainable cities; Unauthorized entity and intrusion classification |
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