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Go to Editorial ManagerInternet of Things (IoT) technologies, particularly the Internet of Vehicles (IoV), have transformed transportation, enabling safer, more efficient, and intelligent mobility solutions. As mobile data and devices increase, cellular networks can support vehicular communication features for safety and non-safety purposes. This paper examines IoV integration with 5G communication technology in a smart city. With varying levels of vehicles numbers.5G efficiently supports internet vehicle communications with slicing technology offering a practical solution for IoV services. This research includes the description of the Internet of Vehicles with 5G system components. Covering the 5G with IoV in the smart cities framework for the development industry. Provide the simulation result for the IoV-5G proposed system. The results show that 5G-IoV outperforms IoV and LTE in every measured parameter, delivering up to 32% greater channel gain rate, about 65–70% lower network latency, and roughly 20–25% higher network transfer rate. The study examines and summarizes our simulation platform's performance. The analysis will be implemented by SUMO, Simu5G in the OMNeT++ simulation program.
This paper presents a simulation-based framework to optimize 5G/6G mmWave network deployments in enterprise environments. Using ns-3 and NYUSIM, it evaluates next-generation Node B (gNB) placement, beamforming, and handover strategies across factory, office, and campus settings. Leveraging the inherent high bandwidth and low latency capabilities of mmWave technology, this study systematically addresses critical challenges such as severe signal attenuation, dynamic blockage, and efficient beam management in complex indoor and outdoor enterprise settings, including large-scale industrial complexes, multi-floor smart offices, and expansive university campuses. Utilizing established open-source network simulators, specifically ns-3, and integrating publicly available, industry-standard channel models such as 3GPP TR 38.901 and NYUSIM, the research proposes and rigorously evaluates novel deployment strategies, advanced beamforming techniques, and intelligent handover mechanisms. The anticipated outcomes include validated guidelines for optimal base station placement, robust performance benchmarks for key enterprise applications (e.g., Ultra-Reliable Low-Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC)), and a robust, extensible simulation framework. This work aims to provide critical, data-driven insights for telecommunication providers and network planners, enabling them to design and implement superior, reliable, and future-proof 5G/6G connectivity solutions, thereby accelerating digital transformation across various industrial and commercial sectors.
Overhead crane systems are found in many industrial environments; however, controlling their motion in the presence of nonlinear and underactuated dynamics considers as a big challenge. To address this, a nonlinear control method proposed to enable the trolley to track the desired trajectory and quickly eliminate swing. First, feedback linearization is applied to the crane dynamics. Next, an energy-based compensation is implemented to ensure the boundedness of the system trajectories. Then, Particle Swarm Optimization (PSO) is utilized to optimally tune the controller parameters. The optimization relies on a multi-objective cost function formulated to simultaneously minimize steady-state error and overshoot, while improving robustness against model uncertainties and external disturbances. Finally, the robustness and validity of the proposed control method are demonstrated through the simulation of an underactuated crane system in several cases, including reference tracking, robustness against system uncertainty and external disturbances. Simulation results illustrated that the presented control method has minimum rise time, settling time with respect other control methods with zero steady state error.
This paper introduces a new class of adaptive WCCI-based non-inverting step-down/step-up converter that integrates an active Ripple Suppression Engine (RSE) and a dynamic mode-transition controller to simultaneously enhance efficiency and minimize ripple across buck, boost, and buck–boost operating modes. Unlike conventional WCCI ZVT based step-down converters which operate in a single region and rely primarily on passive filtering, the proposed topology employs active current injection and ripple-sensing compensation to reshape the inductor-current waveform and attenuate switching-related conduction losses. With the aid of a dual-threshold window comparator and FSM-based logic, the converter achieves highly stable mode transitions free from ringing, overshoot, or mode oscillation. Simulation results validate the superior performance of the proposed architecture, demonstrating more than a 70% reduction in inductor-current ripple and nearly an 80% decrease in output-voltage ripple compared with the existing work. The converter also exhibits substantially improved transient behavior, achieving faster settling times and significantly lower voltage undershoot during load-step events, all while utilizing smaller passive components. Furthermore, the proposed scheme maintains high efficiency throughout the full 2.5V–8V input range, offering a robust and adaptable alternative to traditional WCCI-based implementations. These findings confirm the suitability of the proposed converter for compact, high-performance power-management applications.
Smart cities represent a nexus where urban planning, engineering, digital technologies, and societal needs converge. In emerging economies such as Iraq, conventional top-down smart city models often fail to account for contextual realities, resulting in fragmented or unsustainable initiatives. This paper proposes a novel interdisciplinary smart city development framework that integrates Artificial Intelligence (AI)-based planning, engineering simulations, urban design heuristics, and insights from social sciences particularly those related to digital inclusion and governance. Leveraging publicly available datasets and simulation environments, we demonstrate that the proposed approach can reduce urban traffic congestion by up to 35%, improve equitable access to public services by over 30%, forecast energy demands with more than 85% accuracy, and detect cyber threats with a precision and recall of 85.7%. These results validate the feasibility of a modular, adaptable smart city blueprint that embeds cybersecurity and data governance principles from the outset offering a scalable alternative suited to the institutional and infrastructural realities of developing contexts like Iraq.
The growing sophistication of cyber threats exposes the limits of signature-based detection in Security Information and Event Management (SIEM) systems. User and Entity Behavior Analytics (UEBA) advances SIEM by enabling behavior-based anomaly detection, yet legacy approaches struggle with high false positives and poor adaptability to evolving threats. This research proposes an AI-driven UEBA framework that combines deep learning for modeling user behavior with graph-based tools to map system relationships, enhancing anomaly detection in enterprise environments. Using datasets such as CERT Insider Threat, UNSW-NB15, and TON_IoT, we simulate diverse behaviors and evaluate performance. Our Transformer-GNN ensemble achieved an F1-score of 0.90, reduced false positives by 40%, and cut incident triage time by 78% compared to rule-based SIEM. To support real-world use, we provide an open-source pipeline integrating with SIEM platforms via Kafka, Elastic search, and a modular ML inference layer. This work bridges AI research and deployable cybersecurity practice, advancing the development of adaptive, intelligent, and robust UEBA systems.