Author
Listed:
- Elsisi, Mahmoud
- Amer, Mohammed
- Su, Chun-Lien
- Aljohani, Tawfiq
- Ali, Mahmoud N.
- Sharawy, Mohamed
Abstract
Maritime emissions are a major environmental challenge, with the shipping industry significantly contributing to air pollution and climate change. Port operations, as key hubs of maritime activity, present vital opportunities to reduce emissions and optimize energy usage. This paper offers a comprehensive review of machine learning (ML) and Internet of Things (IoT) technologies for real-time emission monitoring and sustainable energy management in port environments. The integration of ML and IoT is explored as a strategy to minimize ship emissions and improve energy efficiency within ports. Current emission management practices are analyzed, focusing on their environmental and health impacts. Advanced monitoring methods, such as drone-based sensing and ensemble ML algorithms, are evaluated for their effectiveness in real-time emission detection and mitigation. Energy management approaches like bidirectional cold ironing, microgrids, and shore power infrastructure are discussed, emphasizing their role in both emission control and energy optimization. Drones are highlighted as critical tools for continuous, dynamic monitoring of vessel emissions within ports, offering substantial potential to reduce pollution. The paper further examines the integration of real-time emission data with power-sharing mechanisms to optimize energy distribution. Integration challenges are addressed with scalable cloud platforms, standardized communication protocols, and phased implementation strategies for IoT and artificial intelligence (AI) systems in existing port operations. Economic feasibility considerations for adopting technologies such as cold ironing and renewable energy systems in ports are discussed. These considerations include solutions like bidirectional cold ironing, public-private partnerships, and smart grid investments. Furthermore, the paper explores cybersecurity risks associated with the integration of IoT technologies into port operations, highlighting potential vulnerabilities and proposing mitigation strategies, including encryption, secure communication channels, and regular vulnerability assessments. Finally, the review calls for further research to align maritime practices with emerging sustainable technologies. This will support environmental stewardship and enhance operational efficiency in port areas.
Suggested Citation
Elsisi, Mahmoud & Amer, Mohammed & Su, Chun-Lien & Aljohani, Tawfiq & Ali, Mahmoud N. & Sharawy, Mohamed, 2025.
"A comprehensive review of machine learning and Internet of Things integrations for emission monitoring and resilient sustainable energy management of ships in port areas,"
Renewable and Sustainable Energy Reviews, Elsevier, vol. 218(C).
Handle:
RePEc:eee:rensus:v:218:y:2025:i:c:s1364032125005167
DOI: 10.1016/j.rser.2025.115843
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