United operates 339 destinations in 54 countries with 4,523 daily departures and a fleet of 1,304 aircraft. IoT can improve airline operations through sensors that track assets and passengers and provide indoor navigation. Data from sensors is processed in the cloud using event processing, machine learning and predictive analytics. Applications include tracking TSA wait times, managing airplane turns and tasks, and providing wheelchair assistance through mobile apps. Future uses may include context-aware guidance, fleet management, and planning equipment like de-icing resources.

1. February 22, 2017
IoT in Airline Operations

2. About United
§ 339 Destinations in 54 Countries
§ 212 Domestic / 127 International
§ 9 Hubs (Chicago, Denver, Guam, Houston, Los Angeles, Newark, San Francisco, Tokyo, Washington, D.C.)
§ 4,523 Daily Departures
§ 143 Million passengers in 2016
§ Fleet Size: 738 Mainline / 566 United Express

3. IoT Layers – From sensors to apps
Things – Sensors, Devices etc.
Connectivity – Communications, protocols, networks etc.
Infrastructure – Public cloud, private DC, hybrid etc.
Data Ingestion – Event processing, storage etc.
Data Analysis – Data mining, machine learning etc.
Applications – Reporting, Sensing applications
People & Processes – Improving Customer Experience
Business
Value
Big
Data
FogCloud

4. Things Bluetooth Low Energy (BLE) Beacons
§ Low-energy “smart” Bluetooth beacons broadcast information that can
be received by nearby smartphones
§ Current beacons identify that a handset is adjacent to a specific beacon
Upcoming Bluetooth standards improve on precision
§ Range: 1M-30M, 2-Dimensional
***Sub 1 meter precision locations broadcast in the works

5. Things Geocoded High Resolution Indoor Maps

6. Application Indoor Navigation with blue-dot experience

7. Data Ingestion Event Processing and Storage
AWS cloud
Amazon API
Gateway
Airports
CEP
Processor
Amazon
Kinesis
AWS
S3 Storage
Amazon API
Gateway
DB
Dashboards
iBeacon

8. Infrastructure Elastic infrastructure running in cloud
8

9. Infrastructure Elastic infrastructure running in cloud
9

10. Infrastructure Elastic infrastructure running in cloud

11. Data Analysis TSA Wait Times

12. Data Analysis TSA Wait Times Cont.

13. Data Analysis Predicting TSA Wait Times
Event Processing with Machine Learning Models
using Long short-term memory (LSTM) Recurrent Neural Network (RNN)

14. Application Airplane Turn Management / Task tracking

15. Application Wheelchair App

16. Application Future Potential Use cases
§ Context aware guidance/help
§ Ground Fleet Management
§ Aircraft activity tracking
§ Deicing equipment planning and resource management

17. Thank You
suresh.subasinghe@united.com