C-BIVM: A Cognitive-Based Integrity Verification Model for IoT-Driven Smart Cities

Abstract

The exponential growth of the Internet of Things (IoT) has revolutionized various domains such as healthcare, smart cities, and agriculture, generating vast volumes of data that require secure processing and storage in cloud environments. However, reliance on cloud infrastructure raises critical security challenges, particularly regarding data integrity. While existing cryptographic methods provide robust integrity verification, they impose significant computational and energy overheads on resource-constrained IoT devices, limiting their applicability in large-scale, real-time scenarios. To address these challenges, we propose the Cognitive-Based Integrity Verification Model (C-BIVM), which leverages Belief-Desire-Intention (BDI) cognitive intelligence and algebraic signatures to enable lightweight, efficient, and scalable data integrity verification. The model incorporates batch auditing, reducing resource consumption in large-scale IoT environments by approximately 35%, while achieving an accuracy of over 99.2% in detecting data corruption. C-BIVM dynamically adapts integrity checks based on real-time conditions, optimizing resource utilization by minimizing redundant operations by more than 30%. Furthermore, blind verification techniques safeguard sensitive IoT data, ensuring privacy compliance by preventing unauthorized access during integrity checks. Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40% compared to traditional bilinear pairing-based methods, making it particularly suitable for IoT-driven applications in smart cities, healthcare, and beyond. These results underscore the effectiveness of C-BIVM in delivering a secure, scalable, and resource-efficient solution tailored to the evolving needs of IoT ecosystems.