Emerging Computing Technologies for Smart Roads: A Systematic Review of Enabling Systems, Challenges, and Future Directions

Enablers of Data-Driven Intelligent Transportation Systems for Sustainable Smart Cities (SDG 11): A Systematic Literature Review

Traditional transportation systems have evolved into more advanced systems, namely the Intelligent Transportation Systems (ITS). But despite this transformation, modern transportation systems still face problems. Challenges such as traffic congestion, accidents, and high emissions are yet to be effectively solved. Gathered data show that around 10% of the world's emissions come from the transportation sector, and approximately 1.3 million deaths happen every year from road accidents. These recurring issues demonstrate the necessity for ITS to be enhanced and evolved to gain the ability to solve those problems. Numerous existing literatures have covered the topic of data-driven ITS, with the primary focus on explaining the technological side of transportation innovations. In this context, ITS has become a foundation of smart city development, enabling data-driven and sustainable mobility systems. However, there remains a knowledge gap as no research specifically learned about the enablers behind those successful ITS implementations. Studying enablers helps to create knowledge on how previous ITS implementations were successfully launched. This can aid transportation providers to replicate those functional outcomes for the creation of smarter mobility solutions. This study uses the Systematic Literature Review (SLR) methodology, where 22 documents were collected from databases using relevant search words before being processed using a five-step framework. This study contributes by suggesting a taxonomy of ITS enablers, grouped into three categories: technological, institutional, and human. The long-term goal of this study is to improve how ITS responds to real-world transportation challenges and to fully eliminate those problems using knowledge from previous studies.

The Role of Smart Technologies (IoT and AI) in Promoting Sustainability in Urban Developments

The study examines how technology, particularly the Internet of Things (IoT) and artificial intelligence (AI), might support sustainable urban development and real estate. Cities' effects on the environment, including energy use, garbage output, and resource demand, present serious problems as they grow. As a result, sustainable urban development has become a vital tactic to lessen these effects, with the goal of minimising ecological footprints while improving the quality of life for locals. By enabling data-driven decision-making, real-time monitoring, and automated controls, IoT and AI technologies present intriguing solutions that support the shift to more sustainable urban environments. The study examines how IoT and AI technologies support resource management, trash reduction, and energy efficiency—three important urban sustainability objectives. For example, precise energy monitoring and consumption optimisation are made possible by smart grids and IoT-enabled sensors, and AI-driven algorithms help increase building energy efficiency. IoT sensors that monitor garbage levels, optimise collection routes, and lower emissions are beneficial to waste management. Real-time monitoring of air and water quality also improves resource management by facilitating prompt responses and more sustainable resource utilisation. To comprehend the potential of IoT and AI in improving urban sustainability, the research takes a conceptual approach, combining current literature, theoretical frameworks, and case studies from smart city projects. The analysis emphasizes these technologies' advantages as well as their drawbacks, including ethical issues with data privacy and environmental effects. This study highlights the revolutionary potential of smart technology in building durable, effective, and sustainable urban developments, despite its limitations due to its reliance on secondary data. The results indicate that IoT and AI will play a significant role in creating sustainable cities of the future with careful deployment and regulatory support.

RENEWABLE ENERGY INTEGRATION IN SMART TRANSPORTATION NETWORKS: A SYSTEMATIC REVIEW OF TECHNOLOGIES, CHALLENGES, AND FUTURE DIRECTIONS

The rapid growth of urbanization, transportation demand, and environmental concerns has intensified the need for sustainable transportation systems worldwide. Transportation accounts for approximately 23–25% of global energy-related carbon dioxide emissions, making it one of the largest contributors to climate change. Simultaneously, renewable energy technologies such as solar, wind, hydrogen, and bioenergy have experienced significant advancements, creating new opportunities for their integration into smart transportation networks. Smart transportation networks leverage digital technologies, artificial intelligence (AI), the Internet of Things (IoT), cloud computing, and advanced communication systems to improve mobility, efficiency, safety, and sustainability. The convergence of renewable energy systems with intelligent transportation infrastructure has emerged as a promising pathway toward achieving global sustainability goals and carbon neutrality targets. This study presents a systematic review of renewable energy integration in smart transportation networks, synthesizing recent developments from 2018–2026. Using a PRISMA-based review methodology, relevant literature was identified, screened, and analyzed from major academic databases. The review examines renewable energy-powered electric vehicles, smart charging infrastructures, vehicle-to-grid technologies, hydrogen mobility systems, renewable-powered public transportation, and intelligent energy management systems. The findings reveal that renewable energy integration significantly reduces greenhouse gas emissions, enhances energy efficiency, and supports grid resilience. However, challenges including infrastructure limitations, intermittency of renewable resources, cybersecurity concerns, regulatory barriers, and high initial investment costs continue to hinder large-scale deployment. The study develops a comprehensive framework highlighting the interaction between renewable energy sources, intelligent transportation technologies, and sustainability outcomes. Furthermore, future research directions are proposed, including AI-driven energy optimization, digital twins, blockchain-enabled energy transactions, autonomous electric mobility ecosystems, and integrated renewable microgrids. The findings contribute to both academic research and practical implementation by providing a comprehensive understanding of the technological, managerial, and sustainability implications of renewable energy integration in smart transportation systems. Keywords: Renewable Energy, Smart Transportation Networks, Electric Vehicles, Vehicle-to-Grid, Sustainable Mobility, Intelligent Transportation Systems, Energy Management

A systematic review of green IoT technologies, communication protocols, security challenges, and applications in smart cities

A Comprehensive Survey on Connectivity in the Internet of Vehicles: Trends, Architectures, Technologies, and Challenges

Due to increasing vehicle density, urbanization, and complex mobility patterns, road traffic injuries continue to pose a serious threat to public health and safety on a global scale. This ongoing crisis highlights the urgent need for intelligent, connected, and proactive vehicular systems that can prevent collisions, reduce injuries, and optimize traffic flow in real-time. The Internet of Vehicles (IoV) has become a key component of next-generation intelligent transportation, enabling seamless communication, data sharing, and collaborative decision-making among vehicles, roadside infrastructure, and cloud services. However, the effectiveness of current IoV communication frameworks in the real world is hampered by issues such as high latency, inefficient bandwidth utilization, limited scalability, and inadequate trust management. To address these challenges, this survey thoroughly examines 50 cutting-edge studies (2021–2025), including V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure), and hybrid V2X (Vehicle-to-Everything) communication, edge–fog–cloud orchestration, 5G/6G integration, SDN (Software Defined Networking)/NFV (Network Function Virtualization) programmability, and security and trust-aware techniques. We provide a structured comparative analysis of communication types, enabling technologies, and limitations. Building on these insights, we propose an adaptive multi-tier IoV connectivity architecture that offers ultra-low latency, high scalability, and robust interoperability through distributed edge–cloud processing, AI-driven resource orchestration, adaptive blockchain-enabled security, and cross-technology communication control. Furthermore, we identify persistent research gaps and outline targeted future directions. The analysis suggests that AI-based optimization combined with hybrid and multi-tier designs has the potential to significantly improve network resilience, adaptability, and efficiency, offering a promising foundation for high-performance, secure, and reliable IoV systems.

Improve Smart City Operations through the Use of AI and the Internet of Things for Adaptive Management of Urban Infrastructure

The fast pace of urbanization has made smart and sustainable infrastructure management more important than ever. Because of their inherent silos, traditional urban management systems are unable to adapt in real-time to shifting demands in areas such as water distribution, public safety, energy consumption, traffic flow, and energy consumption. This study found that smart cities may use AI and the internet of things to adapt and manage their infrastructure using data. Sensors throughout the city’s infrastructure for transportation, power, buildings, and the environment provide data into Internet of Things devices. Analytics systems powered by AI can automate decision-making, enhance resource allocation, discover anomalies, and forecast demand using massive amounts of data. For predictive maintenance and real-time monitoring, the framework places an emphasis on interoperability, scalability, cybersecurity, and sustainability. By replacing reactive systems with proactive ones, adaptive algorithms and machine learning models can increase dependability, save costs, and revolutionize urban planning. Topics covered in the research include data privacy, infrastructure integration, and data governance. The convergence of AI with the Internet of Things (IoT) creates robust, efficient, citizen-centric urban ecosystems, as shown by comprehensive design and performance evaluation metrics. Smart cities that can adjust to changes in the environment, population, and economy are made possible by these discoveries.