Accident reduction through a privacy-preserving method on top of a novel ontology for autonomous vehicles with the support of modular arithmetic

Cloud of Things (CoT) emerges as a pivotal paradigm, connecting Internet of Things (IoT) devices to the Cloud Computing space, facilitating the efficient management of smart cities. In navigating the intricate landscape of smart city environments, this paper confronts two paramount challenges- heterogeneity and privacy preservation. Heterogeneity, rooted in the diverse origins of CoT devices from various vendors, intro- duces compatibility gaps and data format variations, impeding seamless communication among devices. Simultaneously, privacy preservation concerns itself with averting the inadvertent disclo- sure of sensitive data generated by CoT devices. Existing solutions often exhibit limitations in effectively addressing both challenges concurrently. To bridge this gap, our proposed solution employs a novel ontology-based approach, commencing with the introduction of a groundbreaking "Ontology" using the Protege software. This foundational tool serves a dual purpose-standardizing and unifying general and privacy-related information among diverse CoT devices. The ontology addresses the heterogeneity challenge by fostering a shared understanding and vocabulary, promoting interoperability for smoother communication among disparate devices. Complementing the ontology, a privacypreservation method, implemented with "MININET-WIFI" and grounded in Modular Arithmetic, dynamically adjusts the privacy-preserving rules of each CoT device. This adaptive mechanism signifi- cantly enhances security, mitigating the risk of unintentional data disclosure-a critical aspect evaluated extensively within the context of a widely used CoT application, specifically, the Autonomous Vehicle (AV) environment. The computational cost is meticulously evaluated, showcasing that our solution introduces a modest overhead, notably below 1.8 s, compared to alternative models. Furthermore, the penetration rate analysis reveals the solution's resilience against honest but curious Remote Service Units (RSUs). Communication overhead is quantified for various privacy-preserving methods, providing a comprehensive view of the solution's performance. Through rigorous simula- tions, encompassing assessments of communication overhead, computational costs, and penetration rates, our solution exhibits not only affordability for a diverse array of CoT devices in smart cities but also heightened resilience against malicious activities and adversaries, surpassing current studies. This paper, therefore, not only presents a novel ontology-based solution but also delves into the nuanced intricacies of heterogeneity and privacy preservation within CoT-based smart cities. The proposed approach, characterized by its dual focus on standardization and dynamic privacy adaptation, signifies a significant stride towards fostering secure, interoperable, and privacy-aware CoT ecosystems amid the dynamic landscape of smart cities.