# A Data-Driven Situational Awareness System for Enhanced Air Cargo Operations Emergency Control
## 2 General Architecture  
The main goal of the \"INTELLICONT\" project is to develop, manufacture, and validate a new intelligent lightweight aircraft cargo container with integrated functions for restraining, transportation, and fire/smoke suppression, with sensing and wireless monitoring capabilities. The smart ULDs (Unit Load Devices) will be transferred upon a robotic platform and placed safely at the aircraft's cabin. Low-cost and low-energy sensors in the container with status (identification, location, locking state) detect critical events, fire/smoke, impacts, and accidental misuse during the loading and flight phases. The core of the system is the integrated Remote Monitoring System (RMS), which exploits recent advances in sensor network methodologies to assess the state of criticality, and to identify and monitor possible incidents. The general concept is presented in Figure 2.  
Figure 2: General Concept. The platform's design was based on LAROST(tm) [23] and PrismaSense(tm) [24] systems, which were redesigned to meet the standards of the Aviation industry [8] using the wireless communication protocols (Wi-Fi) to transmit all collected & synchronized data to the HMI, where it can be stored, also ensuring data integrity and enhancing transparency in the case of future analysis and/or investigation, in a very efficient manner in terms of cost, speed, and safety. Transferred data can be further processed at the fleet level and combined with data coming from remote systems (FR teams, drones, satellites, weather forecasts, etc.). A hybrid data fusion architecture will enable the distribution of both processing power and storage capacity to different levels. In short, the data flow pipeline of the RMS System will be the following:  
* Smart Collectors set up a secure wireless network inside the ULD to transmit the processed data to the HMI with a user-defined sampling rate and the ability to maintain and customize them remotely. The wireless protocol will be based on IEEE 802.15.4, with additional layers and data format to cover the requirements of the airport environment and increase the network quality of service, while secure transfer protocols ensure scalability of the solution;
* Data from collectors will further be transferred to the in-field HMI. All data are stored in data lakes for an extended period (up to one year, depending on the number of sensors and sampling rate). Being the upper layer of the onboard architecture at this level, two tasks will be performed: (a) the execution of ML and AI algorithms for early warning reasoning, (b) Real-Time Visualization via Augmented Reality alerting of all outcomes;
* Through HMI, data from the collector network on each of the ULDs are delivered to the robotic platforms. HMIs secure (a) the uninterrupted connectivity with dedicated collector networks, and (b) the collection of data that needs to be processed in real-time. HMIs on each air cargo loading/unloading operation establish a separate wireless network for data exchange between them;
* HMI periodically produces binary files and compresses them to reduce the size of the data to be sent via normal satellite broadband to the data center (Cloud Computing). The cloud could collect the processed data from a wide range of airplanes/ULDs (fleet wise) and handle them accordingly for the following fleet-level data analytics, data storage (Cloud level), and tracking/monitoring of incidents. Besides, third party data (e.g., weather, societal, satellite imaging disease dispersion), as well as data from in-field FR teams (drones, on-sea FR, nearby vessels), will be entered in the main database in the same format. The smooth flow of information between the different components of the RMS system solution is performed through the design and development of user-friendly, fully parameterized, flexible software components.