Page 13 - ITU-T Focus Group on Aviation Applications of Cloud Computing for Flight Data Monitoring - Key findings, recommendations for next steps and future work
P. 13

ITU-T Focus Group on Aviation Applications of Cloud Computing for Flight Data Monitoring
                                      Key findings, recommendations for next steps and future work



               v    AC-I-021: System wide information management (SWIM) by Eurocontrol.

               vi   AC-I-015: Flight operation messaging and potential use cases by Lufthansa Airlines, Germany.
               vii   AC-I-022: Example use cases by Controls and Data Services, United Kingdom.
               viii   A  C-I-023:  ATI  cloud  by  the  Société  Internationale  de  Télécommunications  Aéronautiques  (SITA),
                    Switzerland.
               ix    AC-I-027: Draft progress report Deliverable 2.

               x    AC-I-028: Draft progress report Deliverable 2.
               xi    AC-I-030: Proposal for "on-flight quarantine" and "smart quarantine".
               xii   AC-I-031: HLSC 2015 presentation on Global Aircraft Tracking Initiative, ICAO, Canada.

               xiii   AC-I-038: Draft progress report Deliverable 2.
               xiv  AC-I-040: Draft progress report Deliverable 2.
               xv   AC-I-042 (att1 and att2): Abnormal movements' detection in flight.

               xvi  AC-I-051: Draft progress report Deliverable 2.
               xvii  AC-I-059: Draft progress report Deliverable 2.

               3.2.2   Key findings

               Working Group 2 has identified 28 use cases that utilize data aggregated from an aircraft and transmitted
               wirelessly in-flight to the ground for further processing and correlation.
               The use cases can be categorized into two groups.

               The first group contains those use cases that require that data be transmitted virtually in real time; this means
               that data has to be transmitted during the flight and as quickly as possible after it has been generated. Examples
               for this category are flight tracking/following, search and rescue operations or mission support with in-flight
               aircraft condition monitoring.

               The second category deals with use cases that do not require a real-time transmission of data and where post-
               flight availability is sufficient. Two out of many examples are approach statistics and predictive maintenance.
               In this category the potential for innovation is limited. The use cases already exist in the aviation industry,
               by using post-flight downloads of the data. On some aircraft, the data is downloaded to rewritable compact
               discs (CDs) or universal serial buses (USB) sticks. Other aircraft uses cellular network data streaming on the
               ground. However, if a central data repository is being developed, because of the real-time use cases, then also
               the post-flight use cases can benefit from the repository. The airlines and maintenance, repair and operations
               (MROs) can use and process the data more efficiently, compared to today's many individual companies and
               manufacturers specific solutions. In addition, new applications might evolve if auto-correlation and automatic
               pattern recognition algorithms are applied to the collection of all data available from an aircraft and reveal
               previously unseen information.


               3.2.3   Recommendations and next steps

               Working Group 2 recommends:

               •    Regulatory authorities to mandate real-time flight data streaming. The list of use cases shows that the
                    data can be used in many different ways. Without a clear mission, the industry will develop various
                    products with different goals and certainly incompatibilities amongst each other.
               •    Regulatory authorities shall establish the appropriate detailed definition of real-time FDM in terms of
                    data types and data volume (parameters and recording intervals).





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