# A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations
## 4 Conclusions  
The ability to exploit data to obtain useful and actionable information, make predictions, and provide insights is an essential element today for continuous process improvements and optimal performance throughout the lifetime of assets. The aviation industry is continually evolving in the context of Industry 4.0. Used wisely, data can help the aviation sector to achieve operating cost savings and efficiency increase, higher safety, and security of assets. From cargo loading to fire detection during flight, IoT technology can be integrated into all phases of the asset's lifecycle and trigger/spur new developments as part of an iterative process.  
In the current work, a novel intelligent IoT system for improving the safety and planning of air cargo operations was presented. The system is part of a smart ULD that integrates three types of sensors for structural health monitoring, fire detection, and locking status indication. The measured data are collected via a dedicated platform incorporating a microcontroller and several specialized interfaces and are visualized on an HMI. While not yet fully developed, the main benefit of the platform is its ability to harmonize data collected from various sensors onboard and to implement large-scale processing techniques, to perform operational efficiency optimization, performance optimization, and safety issue detection. The developed platform was based on a scalable edge-fog architecture and was tested and benchmarked on a series of pilot demonstrations. The system can contribute towards safer, environmentally friendlier, faster and less costly air transportation.  
As a next step to our research, both the operational efficiency and cybersecurity of the system will be evaluated. Currently, we are working towards the implementation of an edge computing smart gateway located either on each ULD or on the aircraft that could provide decision support by employing machine learning techniques for ULD structural health monitoring, advanced fleet management, etc. Emergency data transfer to either a local cloud dedicated to an aircraft or a ULD fleet or to a bigger, central cloud that collects data from various aircraft and/or ULD fleets is also being considered.  
Conceptualization, C.S.; Data curation, E.S. and P.T.; Formal analysis, C.S., E.S. and F.G.; Funding acquisition, C.S.; Investigation, F.G., P.T. and N.G.; Methodology, C.S. and E.S.; Resources, C.S.; Software, K.C.; Supervision, C.S.; Validation, C.S., E.S., F.G., A.P. and P.T.; Visualization, A.P. and K.C.; Writing--original draft, C.S., F.G., A.P. and N.G.; Writing--review & editing, C.S. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by Clean Sky Joint Undertaking (project ID: 785472), under: H2020-EU.3.4.5.1. IADP large passenger aircraft.  
This work was supported by the project of development and manufacturing of intelligent lightweight composite aircraft container (project ID: 785472), funded under: H2020-EU.3.4.5.1. IADP large passenger aircraft.  
The authors declare no conflict of interest.