## 6 Conclusions  
The integration of vibration and AE sensors with IoT platforms marks a transformative shift in the NDT paradigm, advancing it toward a remote, intelligent, and predictive framework. Unlike traditional NDT, which relies on periodic, localized inspection, RNDT enables continuous monitoring of structural health through embedded sensor systems and real-time data analytics. This evolution is particularly significant for port cranes, which face extreme mechanical stress, corrosive environments, and operational constraints that make conventional inspection methods insufficient. This review consolidates insights from over 100 peer-reviewed studies and demonstrates that, while vibration and AE sensors have been widely adopted in other industrial sectors, their deployment in port crane applications remains limited but promising. The reviewed technologies show clear potential for early detection of mechanical degradation, crack propagation, and corrosion. Furthermore, the convergence with IoT platforms al-lows for seamless data transmission, cloud-based analysis, and automated maintenance alerts, offering reduced downtime, enhanced safety, and improved cost-efficiency. Despite these advantages, challenges such as sensor durability in marine environments, data interpretation complexity, and standardization of multi-sensor systems per-sist. Future research must prioritize the development of corrosion-resistant and self-powered sensors, AI-enhanced signal analysis, and interoperable IoT protocols tailored for heavy-lifting equipment. Importantly, the alignment of RNDT technologies with predictive maintenance frameworks and digital twin architectures will play a pivotal role in building smarter, safer, and more resilient port infrastructure in the Industry 4.0 era. Conceptualization, J.L.T. ; methodology, J.L.T. ; software, J.L.T. ; validation, M.T.H.S., F.S.S. and R.G. ; formal analysis, J.L.T. and R.G. ; investigation, J.L.T. ; resources, J.L.T. ; data curation, J.L.T. ; writing--original draft preparation, J.L.T. ; writing--review and editing, F.S.S. and R.G. ; visualization, J.L.T. and R.G. ; supervision, M.T.H.S. and R.G. ; project administration, F.S.S. and M.T.H.S. ; funding acquisition, M.T.H.S. and R.G. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & \multicolumn{2}{c}{**Current Limitation/Gap**} & \multicolumn{2}{c}{**Future Research Direction**} \\ \hline Sensor Integration & Lack of harmonized multi-sensor data fusion & Develop unified platforms integrating AE, vibration, and thermal sensors \\ \hline Real-Time Data Processing & High latency and poor edge processing in harsh marine environments & Use AI-driven edge computing and real-time analytics platforms \\ \hline Energy Sustainability & Battery-dependent wireless sensor nodes for cranes & Implement self-powered sensors (e.g., piezoelectric, triboelectric) \\ \hline Environmental Durability & Sensor degradation due to salt spray, humidity, and vibration & Develop corrosion-resistant, encapsulated sensor casings \\ \hline Standardization & Absence of unified IoT protocols or NDT communication standards in port environments & Create standardized, ISO-aligned frameworks for IoT-based crane monitoring \\ \hline Predictive Maintenance & Current systems reactive; minimal use of predictive analytics & Integrate machine learning for early damage prediction and remaining life estimation \\ \hline UAV and Mobile Platforms & Limited use in automated crane inspections & Expand UAV-assisted visual/NDT inspection with AI-based defect detection \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Identified research gaps and future research directions. **Funding:** The authors are grateful for the financial support given by The Ministry of Higher Education Malaysia (MOHE) under the Higher Institution Centre of Excellence (HICOE2.0/5210004) at the Institute of Tropical Forestry and Forest Products.