Abstract
Cloud Computing (CC) provides essential computation and storage facilities for Internet of Things (IoT) environments. However, due to the physical distance between IoT devices and the cloud, higher latency and longer data transfer times are significant challenges. Additionally, the increasing volume of data generated by IoT devices exacerbates these issues. To overcome these limitations, Fog Computing (FC) has emerged as an intermediary layer between IoT devices and the cloud, offering benefits such as reduced latency, geo-distribution, mobility support, and real-time processing. FC has proven to be an effective platform for IoT applications, including smart cities, smart grids, smart vehicles, and traffic management systems. However, the decentralized and heterogeneous nature of FC introduces critical security and privacy challenges, such as trust management, secure data access, privacy preservation, and access control.This research addresses these challenges by proposing a comprehensive framework for privacy preservation and security in Vehicular Fog Computing (VFC) environments. The proposed framework leverages advanced mechanisms to ensure trusted access, data privacy, and secure communication. The key features of the framework include blockchain-based secure registration of vehicles and users, enabling decentralized and tamper-proof management of credentials. Attribute-Based Fuzzy Access Control Mechanism (A-FACM) is introduced for authentication, ensuring fine-grained access control based on dynamic policies.
The framework incorporates Partial Elliptic Strassen’s Matrix Cryptography (PESMC) to secure data transmission through partial encryption, protecting sensitive information without compromising computational efficiency. Trust evaluation of fog nodes is conducted using Knowledge, Experience, and Reputation-based Trust Score (KERTS), ensuring that only trusted nodes participate in data processing and sharing. Load balancing, a critical factor in VFC, is achieved through Max-pooled Agglomerative Spatial Clustering (MASC), which distributes workloads efficiently across fog nodes to reduce congestion and enhance system performance.
Privacy preservation is further strengthened through the integration of L-diversity and Weibull distribution, which transform data distributions to ensure protection against attribute disclosure. Bidirectional trust verification is employed to secure communication between fog nodes and cloud storage. Additionally, the framework utilizes advanced scheduling techniques to optimize resource allocation, reduce latency, and enhance overall system reliability.
By addressing the limitations of traditional cloud-based IoT systems, this framework offers a robust solution for ensuring privacy, security, and efficiency in VFC environments. The results demonstrate its potential to support real-time, latency-sensitive, and secure applications, paving the way for safer and more reliable vehicular networks.
| Date of Award | 2024 |
|---|---|
| Original language | English |