| Abstract |
As urbanization accelerates, smart cities are emerging as innovative ecosystems that integrate technology to address challenges related to sustainability, mobility, and infrastructure. Among these technologies, edge computing has gained prominence as a transformative solution to optimize data processing and resource management in urban environments. This paper explores the role of edge computing in enabling efficient, real-time decision-making by bringing computational power closer to data sources. Unlike traditional cloud-centric models, edge computing reduces latency, enhances data security, and improves bandwidth utilization by distributing data processing across decentralized nodes. The integration of edge computing in smart cities supports various applications, including intelligent transportation systems, energy-efficient smart grids, and real-time public safety monitoring. By processing data locally, edge devices can handle massive volumes of information generated by Internet of Things (IoT) devices, ensuring seamless service delivery without overwhelming centralized systems. Furthermore, this decentralized approach enhances resilience by reducing dependency on remote servers, a crucial factor for mission-critical urban applications. A significant focus of this paper is on resource management, particularly the allocation of computational resources across edge nodes. Strategies such as dynamic resource scheduling, load balancing, and adaptive task offloading are analyzed for their effectiveness in maintaining operational efficiency. Moreover, the research highlights the importance of leveraging machine learning and artificial intelligence algorithms within edge computing frameworks to predict traffic patterns, optimize energy consumption, and enhance waste management systems. Security and privacy concerns, often considered barriers to edge computing adoption, are addressed through advanced encryption techniques and secure communication protocols. This paper also evaluates challenges associated with edge computing deployment, such as hardware limitations, interoperability issues, and the need for robust regulatory frameworks. Case studies from leading smart city projects illustrate successful implementations and offer insights into overcoming these obstacles. In addition to technical aspects, this research underscores the socioeconomic benefits of edge computing in urban settings. Improved public services, reduced environmental impact, and cost-effective infrastructure management demonstrate the potential of edge computing to revolutionize city living. By enabling real-time analytics and localized decision-making, edge computing supports a more responsive and adaptive urban ecosystem. The findings presented in this paper emphasize the critical role of edge computing in bridging the gap between urban challenges and technological solutions. As cities continue to evolve, adopting edge computing technologies will not only enhance operational efficiency but also foster innovation, sustainability, and inclusivity. Future research directions include exploring hybrid models combining edge and cloud computing, advancing hardware capabilities, and developing standardized frameworks to accelerate adoption. This paper contributes to the growing body of knowledge on edge computing, offering a comprehensive analysis of its applications, challenges, and potential in shaping the future of smart cities. By optimizing data processing and resource management, edge computing emerges as a; cornerstone technology for creating smarter, more resilient urban environments. Copyright © 2024 by Author/s and Licensed by JISEM. |
