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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.
In an era shaped by rapid urbanization and digital transformation, smart cities have become a global imperative for sustainable, efficient, and citizen-centric development. This article analyzes Amman’s development into a smart city, highlighting its role as a model for emerging urban areas. Leveraging recent technologies such as AI, IoT, blockchain, and big data, Amman is actively transitioning from a traditional city to a smart one enhancing mobility, energy efficiency, education, healthcare, and citizen engagement. This study examines Amman’s smart city vision and roadmap, technological infrastructure, key application domains, implemented innovation projects, and global rankings. It also explores the challenges the city faces, future research opportunities across various domains, the role of software in urban development, and the critical factors contributing to Amman’s success as a smart city. This article serves as a vital reference for researchers, policymakers, urban planners, and practitioners aiming to shape next-generation smart cities. The case of Amman underscores how strategic governance, public-private collaboration, and the effective use of emerging technologies can accelerate sustainable urban transformation.
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.
With the rapid popularization of 5G edge communication systems, their decentralized architecture and the ultra-large attack surface brought by massive access devices have created severe network security challenges. Although current artificial intelligence-based intrusion detection systems and conventional federated learning schemes can achieve high intrusion detection accuracy, they universally suffer from two major limitations: excessive computational overhead and vulnerability to poisoning attacks, which make them unable to adapt to resource-constrained edge deployment environments. To address this issue, this paper proposes a lightweight anti-poisoning federated learning framework for low-latency secure 5G edge communications, with two core design elements: First, it integrates lightweight GRU/CNN models and a trust-aware aggregation mechanism to weaken the negative impact of malicious model updates; second, it introduces a deviation-based poisoning detection strategy to identify and filter adversarial updates during the federated learning process. We used the public CICIDS2017 dataset to complete tests in a simulated 5G edge environment equipped with 100 to 1000 edge devices, covering multiple scenarios including DDoS, MITM, and poisoning attacks. The results show that compared with existing similar schemes, the proposed framework reaches a maximum detection accuracy of 98.3\%, reduces communication latency by 30-35\%, cuts computational overhead by 40-60\%, and sees only a negligible drop in accuracy under poisoning attacks. It can support a wide range of real-time 5G edge applications such as intelligent transportation, smart cities, and the industrial Internet of Things.
Smart city applications demand lightweight, efficient and dependable communication protocols to facilitate the functioning of resource-limited Internet of Things (IoT) devices. This work performs an extensive empirical study of the three most prominent IoT standards; Message Queuing Telemetry Transport (MQTT), the Constrained Application Protocol (CoAP) and Hypertext Transfer Protocol (HTTP) by emulating real-world smart city use cases using a Raspberry Pi based testbed. The primary metrics based on which the protocols are analyzed are latency, message overhead, delivery rate and energy consumption. ANOVA and Tukey's HSD tests are used to determine the statistical significance of experimental data. The test results indicate that CoAP under (QoS-1 reliability) shows the least latency and energy consumption and MQTT due to its support for Quality of Service (QoS) is the most reliable. Among the others, HTTP is in general performance terms certainly at the bottom of all metrics mainly for its verbosity and synchronous nature. The paper then also suggests a decision flowchart for developers to choose the suitable protocol according to application requirements. The results are more than just numbers on a graph, and the research can be deployed for advice for protocol selection in practice, where this study helps identify issues with encryption overhead (over 75\%) while showcasing multi-hop network scalability and adaptive switch mechanisms as areas that remain to be resolved. Such findings can be used as a basis for design approaches to construct secure, efficient and scalable communication protocols in urban IoT settings.
The rapid expansion of smart home and smart city technologies has introduced a complex array of interconnected Internet of Things (IoT) devices, exposing both cyber and physical infrastructures to a growing spectrum of security threats. Traditional cybersecurity models are insufficient to address the dynamic and distributed nature of modern cyber-physical environments, particularly in emerging economies where standardized security frameworks are often lacking. This research proposes a unified, hybrid cyber-physical security framework tailored for smart home and smart city IoT systems. Leveraging publicly available datasets such as UNSW-NB15, TON_IoT, and CICIDS2019, we simulate various attack vectors and evaluate a multi-layered intrusion detection system (IDS) that combines both signature-based and anomaly-based machine learning models. The proposed framework is validated using simulated network topologies built with NS-3 and Cooja, focusing on performance metrics including detection accuracy, false-positive rate, and computational overhead. Results demonstrate that our hybrid approach achieves over 95% accuracy in detecting complex multi-stage attacks, while maintaining scalability and adaptability across different IoT environments. The findings contribute to the development of more secure, resilient, and context-aware smart infrastructure systems offering a practical foundation for real-world deployment in smart cities and connected home ecosystems, especially within developing regions such as Iraq.