A Systematic Literature Review on Integrating VANETs, VDTNs, 5G, and IoT for Smart Cities: Current Approaches, Challenges, and Future Directions

Orjola Jaupi — 2025-10-02

Nowadays smart cities have become a necessity for rapidly changing and transforming urban environments and the core technologies enabling this development are Vehicular Ad Hoc Networks (VANETs), Vehicular Delay Tolerant Networks (VDTNs), 5G networks, and Internet of Things (IoT). These technologies alone offer an important contribution, but when integrated effectively, they offer the opportunity of uninterrupted connectivity, real-time data sharing and management of urban resources. This paper conducts a comprehensive literature review to study existing techniques/approaches and challenges for integrating VANET, VDTN, 5G, and IoT within smart cities based on three research questions. Recent articles from databases such as Google Scholar, ResearchGate and MDPI, were reviewed to examine this integration, to identify recent advancements in this topic with focus on innovative methodologies proposed in an international context and to highlight the research gaps, challenges and solutions. The VOSviewer software was used to build the keyword co-occurrence network and to cluster the relevant literature. Our findings reveal that although promising solutions exist, issues such as high mobility, heterogeneous network architecture, and resource constraints remain critical barriers to large-scale deployment of smart city applications. Furthermore, this review proposes a conceptual framework for intelligent and adaptive network integration of VANET, VDTN, IoT, and 5G for future smart city applications.

AI-Enabled Distributed Cloud Frameworks for Big Data Analytics with Privacy Preservation

Gullapalli Lasya Sravanthi — 2025-12-06

The fast rise of the Big Data and the faster adoption of Artificial Intelligence (AI) have changed the modern computational ecosystems allowing to conduct real-time analytics and automation in some of the most vital areas: healthcare, finance, and industrial IoT. Nevertheless, there are notable problems with traditional centralized cloud designs such as poor scalability, high latency, network overload, and augmented privacy and security threats in distributing sensitive information that is sensitive. To fill these gaps, this paper suggests a new AI-enabled distributed cloud framework (AIDCF) that combines federated learning, differential privacy, and homomorphic encryption to facilitate secure, privacy-preserving and scalable analytics on the Big Data without centralized sharing of data. The mixed-method research design was chosen, which involved the development of theoretical frameworks, the modeling of algorithms, and simulation of experiment, based on synthetic multi-domain data (healthcare, finance, IoT) running on distributed cloud nodes (10-100). The outcomes indicate the novelty and high-performance AIDCF, which has a high accuracy (93.7%), low latency (139 ms), high throughput (1585 MB/s) and high computational performance (89.5 percent), as well as the significant reduction in privacy loss (ε = 1.3) compared to other models such as DP-FedAvg, SecureML, and Baseline Cloud Analytics (BCA). These results confirm that the presented framework provides a feasible trade-off between analytical and confidentiality protection, which makes it be deployed in privacy-sensitive, real-time, and large-scale distributed systems. Altogether, AIDCF offers a scalable, secure, and high-performance distributed AI system, which will push the state of the art of privacy-aware Big Data processing.

Journal of Transactions in Systems Engineering

A Systematic Literature Review on Integrating VANETs, VDTNs, 5G, and IoT for Smart Cities: Current Approaches, Challenges, and Future Directions

AI-Enabled Distributed Cloud Frameworks for Big Data Analytics with Privacy Preservation