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

Orjola Jaupi, Evjola Spaho — Thu, 02 Oct 2025 00:00:00 +0200

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, Ramya Mandava — Sat, 06 Dec 2025 00:00:00 +0100

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.

Machine Learning Analysis of Social Media Usage Patterns and Mental Health Indicators

Dhoha Raad Hussein, Ali Subhi Alhumaima; Hussein Alkattan; Mostafa Abotaleb — Sat, 20 Dec 2025 00:00:00 +0100

The rapid growth of social media has created new opportunities for connection but has also raised concerns about its impact on mental health. This study investigates how demographic factors, digital behaviour, and self-reported psychological indicators jointly relate to users’ mental states. Using an open dataset of 5000 social media users, we analyse numerical and categorical variables including age, gender, daily screen time, social media time, counts of positive and negative interactions, sleep duration, physical activity, anxiety, stress, mood, and a three-level mental-state label (Healthy, At_Risk, Stressed). Descriptive statistics and correlation analysis show that longer daily screen and social media time are strongly associated with higher stress and anxiety and lower mood, while sleep and physical activity display the opposite pattern. K-means clustering applied to combined behavioural and psychological features reveals three coherent user profiles that align with the Healthy, At_Risk, and Stressed categories, highlighting a clear gradient from balanced to high-risk digital lifestyles. A decision-tree classifier trained only on behavioural features (excluding anxiety, stress, and mood to avoid target leakage) achieves an overall accuracy of about 97% on a held-out test set and provides interpretable if then rules linking specific usage patterns to mental states. The results emphasise that intensive, unbalanced social media use especially when coupled with reduced sleep and low physical activity is strongly linked to adverse mental-health outcomes, and they illustrate how simple machine-learning models can support early risk detection based on non-intrusive behavioural data.

K-Means Clustering for Evolutionary Staging in a Human Evolution Dataset

Azhar Hamid Elias, Ahmed Hamid Elias, Sajjad Mohammed Hasan, Mostafa Abotaleb — Mon, 22 Dec 2025 00:00:00 +0100

This research work applies unsupervised machine learning to explore evolutionary patterns in hominin morphological and temporal data. A dataset comprising 6,000 records of hominin specimens was analysed using three quantitative attributes: geological age (1–8 million years), cranial capacity, and estimated stature. Following data cleaning and z-score normalization, K-means clustering (K = 4) was employed to identify coherent evolutionary groupings without prior taxonomic labelling. The resulting clusters exhibit a clear temporal and morphological progression. The earliest cluster (mean age ≈ 6.66 Ma) is characterized by the smallest cranial capacity (≈156 cm³) and stature (≈106 cm), consistent with early hominin forms. A second cluster (≈3.89 Ma, 367 cm³, 117 cm) corresponds to Australopithecine-like specimens, while a transitional cluster (≈1.96 Ma, 490 cm³, 119 cm) reflects early Homo characteristics. The most recent cluster (≈1.07 Ma) displays substantially larger cranial capacities and statures (≈1063 cm³ and ≈162 cm), aligning with later or near-modern Homo. Visualization through scatter plots, bar charts, and boxplots supports a monotonic increase in cranial capacity and height across evolutionary stages. These findings demonstrate that unsupervised clustering can recover biologically meaningful evolutionary patterns from morphological and temporal data, highlighting its potential as an exploratory tool in paleoanthropological research.

Pumped-Storage Energy Systems for the Drin River Cascade: A Case Study

Majlinda Alcani, Mariglen Shuli, Artan Hoxha — Thu, 25 Dec 2025 00:00:00 +0100

This article investigates the feasibility of implementing pumped-storage hydropower (PSH) systems within the existing hydropower plants (HPPs) of the Drin River cascade in Albania. Currently, five HPPs are operational along the Drin River, two of which are managed by private operators, while the remaining three are operated by the public utility “KESH sh.a”. Increasing climate variability necessitates adaptive operational measures to enhance the safety and resilience of these hydropower assets. In parallel, the rapid development of variable renewable energy sources, such as photovoltaic and wind power, has intensified interest in large-scale energy storage solutions. The study evaluates a case scenario involving the installation of a 200 MW pumped-storage system between two existing reservoirs in the Drin River cascade. Based on a hydrological year characterized by high rainfall, the results indicate that the proposed system could reduce downstream discharge by up to 30% while producing approximately 28 GWh of net electrical energy. Beyond energy generation, the assessment highlights broader system-level benefits, including improved flood risk management, enhanced climate resilience, and optimized hydropower operation. The findings suggest that pumped-storage integration could play a strategic role in supporting Albania’s energy transition and strengthening the operational flexibility of the Drin River hydropower system.