Page 10 - ITU-T Focus Group on Aviation Applications of Cloud Computing for Flight Data Monitoring - Key findings, recommendations for next steps and future work

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

3.1.2 Key findings

A cloud service provider can provide reliable, secure and affordable infrastructure in which to host the
applications needed to support flight data monitoring (FDM) and other types of data analytics. A cloud services
partner may provide additional data analytics tools and services to drive additional benefit from the data
and information that has been generated by standard FDM techniques and other data sources such as the
weather, the aircraft communications addressing and reporting system (ACARS), electronic flight bags (EFBs),
etc. The use of the cloud as a repository for sensitive data and information requires an assurance of security
and privacy such as ISO/IEC 27001 and ISO/IEC 27000 family to protect the applicable airline as the cloud
service customer (CSC).

3.1.2.1 Data analytics
The Internet of things (IoT) is driving exponential growth in sensors, networks and smart devices everywhere,
providing a huge increase in streaming data, or 'Data in Motion'. Although this data has tremendous potential,
much of it often retains its highest value for only a short period of time. 'Data in Motion' capabilities aim
to extract data "on the fly" before it is stored – specific for aviation – before the data is sent to the ground,
rather than 'Data at Rest' which refers to data that has been collected from various sources, stored and is
then analysed after the event occurs.

The key advantage provided by 'Data in Motion' analytics is the ability to identify potential problems and
initiate a rapid response while the aircraft is in flight. Data analytics offer significant improvement over today's
capabilities for several use cases, particularly related to FDM. For example, before each flight, the on-board
'Data in Motion' analytics function is set as per the normal aircraft systems operating parameters for the flight
such as the flight plan data. When on-board sensors or systems detect an 'out of bounds' parameter or a
deviation from the flight plan, the built-in logic can determine the most appropriate action (based on the event
or combination of events). This functionality can be provided simply as an alert to the ground with contextual
information. Ground support staff are quickly able to interpret these alerts and respond accordingly. A complex
alert may trigger initial processing of other on-board systems to get a better understanding of the problem.

3.1.2.2 Fog computing
Fog computing is a paradigm that extends cloud computing and services to the edge of the network. Similar
to cloud, fog provides data, compute, storage, and application services to CSCs. However, fog characteristics
are more towards its proximity to CSC users or sensing objects, its dense geographical distribution, and its
support for mobility, along with sensitivity to real-time problems identification, alerting and response.

By hosting services at the edge of the network, fog reduces service latency and improves quality of service
(QoS). Fog computing supports emerging Internet of things (IoT) applications that demand real-time/
predictable latency (industrial automation, transportation, networks of sensors and actuators). Due to its
wide geographical distribution, fog computing is well positioned for real-time big data and real-time analytics.
Fog supports densely distributed data collection points, hence adding a fourth axis to the often mentioned
big data dimensions (volume, variety, and velocity).
The main and most important capability of fog computing is a smart and efficient use of available bandwidth,
together with content security and privacy. Furthermore, both mobility and the wireless nature of flight data
monitoring are covered by this paradigm in the same manner as superior quality of service, strong presence
of streaming and edge analytics data mining. Hence, real-time, actionable analytics, and processes that filter
the data and push it to the cloud are fundamental needs covered by fog computing.
Transmitting all that data to the cloud and transmitting response data back puts a great deal of demand
on bandwidth, requires a considerable amount of time and can suffer from latency. In a fog computing
environment, much of the processing would take place in a router, decreasing the data volume that must
be moved, the consequent traffic, and the distance the data must go; thereby reduces transmission costs,
shrinks latency, and improves QoS.

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