Webinar: Automating Complex Airport Operations with WSO2 Middleware Platform

Miyuru Dayarathna and Ramindu De Silva

Introduction● Operations conducted in a typical airport are

diversified and often span multiple information systems.

● Examples include,○ flight check-in○ flight information maintenance○ cleaning crew operations○ traffic control (air/ground) ○ etc.

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Problem● Information systems involved in airport operations are

mostly isolated [1].● We need comprehensive

enterprise middleware platforms over isolated solutions.● Why?

○ Reduced operation cost○ Improved service quality○ Time-based

competitiveness○ New products and services

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[1] Norman J. Ashford, Saleh Mumayiz, Paul H. Wright (2011), Airport Engineering: Planning, Design and Development of 21st Century Airports, John Wiley & Sons, 2011

WSO2 Data Analytics Server

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Real-time Analytics : WSO2 Complex Event Processor

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Predictive Analytics :WSO2 Machine Learner

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Solution

Use a comprehensive middleware platform to develop a unified solution.o Front-end services

o Provide real-time information on average wait time

o Back-end serviceso Material serviceso Fleet services

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Front-end information services : Providing real-time information• Information such as wait time are

important for individual travellers.

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Providing real-time information to individuals● We have implemented an iBeacon based wait

time prediction application.

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WSO2 DAS

Reorder Kalman Filter

Average beacon locations

Update waiting time of specific areas

Publisher

Receiver

GeoDashboard

Supervisor

Trajectory Smoothing + Waiting time calculation

Re-ordering

● Out-of-order events are possible in most of the event processing scenarios.

● Multiple approaches to deal with the disorder introduced by the out-of-order events exist.○ Buffer-based techniques○ Punctuation-based techniques○ Speculation-based techniques○ Approximation-based techniques

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Re-ordering : k-slack

● Uses a buffer to sort tuples from the input stream in ascending timestamp order before presenting them to the query operator.

● k-slack uses a buffer of length K to delay an event for at most K time units

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timestamp > greatestTimestamp

timeDifference > k

timeDifference < MAX_K

timeDifference = greatestTimestamp - minTimestamp

k = timeDifference k = MAX_K

entry.getKey() + k <= greatestTimestamp

Take next element

Emit element

Has more elements?yes

yes

yes

yes

yes

Kalman Filter

● Estimate the real values from the measured values in-order to smooth the trajectory.

● Given position and velocity sensor readings (Zk), we need to estimate the real reading (X’^k).

● Kalman filter is a set of mathematical equations.

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X’^k

Zk

Providing real-time information to individuals - Contd.

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Passenger trajectory before Kalman filtering Passenger trajectory after Kalman filtering

Kalman Filter (Contd.)• Calculate the best estimate (i.e.,

probably the real reading) X’^k and its covariance matrix P’k

• Updated Kalman gain matrix (K’k) need to be calculated using the covariance matrices of the previous estimates

• X’^k and P’k values are fed back to the Kalman filter in the next round of predict or update as many times as required.

X’^k = X^k-1 + Kk-1 ( Zk - H*X^k-1) ---------------------------------- (1)

PredictionX^k = A*X^k-1 ----------------------------------------------------------- (2) Pk = A*Pk-1AT ---------------------------------------------------------- (3)UpdateS = H*Pk*HT+R ------------------------------------------------------- (4) K’k = Pk*HT*S-1 ------------------------------------------------------- (5) X’^k = X^k + K’k ( Zk - H*X^k) ------------------------------------ (6) P’k = Pk - K’k*H*Pk ------------------------------------------------- (7)

Where,A = [1 timeDifference; 0 1]

Initially, the time difference is assumed as 0X = [previouslyEstimatedValue; ChangingRate]

Initially, the previously estimated value was assumed as the initial measured valueP = [1000 0; 0 1000]H = [1 0; 0 1]R = [standardDeviationOfNoise 0; 0 standardDeviationOfNoise]

standardDeviationOfNoise = 0.01

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Front-end Information Services :Predicting the service time

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WSO2 DASCustomer

Persisted Wait-time

stream

DASTable Access

Linear RegressionAnalysis

Model REST API

Wait time information stream

WSO2 ML

Mobile App

Waiting time prediction

Back-end Information Services : Integrating diversified information - Airplane Maintenance

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Back-end Information Services : Integrating diversified information - Maintenance crew management

• Technicians - need to quickly access timely information pertaining to the process of aircraft maintenance.

• Cleaning crew - need to indicate the start/end times of the cleaning process so that the airline officers can start boarding process.

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Conclusion

• Typical airport operations are diversified and often require interaction of multiple disconnected information systems.

• We described how WSO2’s comprehensive middleware platform could be leveraged to create integrated, seamless solution for airport operations.

• DAS’s batch and interactive analytics could also be utilized in this process in future.

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