Background and Objectives: With the rapid expansion of maritime and commercial activities in ports and offshore regions, the need for intelligent systems to monitor and manage infrastructure resources has become increasingly critical. Building Management Systems (BMS) are recognized as key technologies in enhancing energy efficiency, safety, and smart port infrastructure. However, traditional BMS architectures are often inadequate for addressing real-time processing, distributed decision-making, data security, and scalability requirements in complex port environments. In this context, cloud computing and its related architectures (edge, fog, and cloud) offer transformative capabilities for reengineering BMS systems. This study aims to investigate the role of cloud computing in optimizing the performance, resilience, and efficiency of BMS systems in port and offshore infrastructures.
Methods: In this study, a new cloud computing-based model has been designed and proposed through extensive literature review, conceptual analysis, and comparison of traditional and modern architectures. Within this framework, a multi-layer architecture incorporating edge, fog, and cloud computing has been developed using microservices and Infrastructure as a Service (IaaS). The model's components include data collection units, real-time analysis, storage, a management dashboard, and intelligent alerting. Tools such as Zabbix and Nagios have been employed for data gathering, and comparative analyses have been conducted based on criteria such as scalability, processing speed, security, and cost.
Findings: The results demonstrate that the proposed cloud-based architecture significantly outperforms traditional BMS in reducing processing latency, optimizing energy consumption, improving system scalability, enhancing data security, and enabling centralized resource management. Comparative analysis reveals that, in addition to real-time monitoring, the proposed model supports parallel processing, multi-cloud service provider integration, and intelligent data-driven analysis. The use of microservices architecture also leads to increased operational flexibility, higher accuracy in data log analysis, proactive alerting, and efficient system responsiveness to anomalies.
Conclusion: The proposed multi-layered cloud computing architecture—divided into edge, fog, and cloud layers—offers an effective framework for intelligent, scalable, and resilient BMS deployment in port infrastructures. It proves particularly beneficial in high-data, unstable network environments typical of offshore facilities. The study confirms that this approach supports digital transformation by improving responsiveness, operational efficiency, and system sustainability. Furthermore, it emphasizes the importance of developing localized AI algorithms, enhancing data protection, fostering inter-organizational collaboration, and integrating emerging technologies such as blockchain and digital twins. Despite its benefits, initial implementation costs, technical training requirements, and cybersecurity concerns remain as limitations to be addressed in future system designs. |
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