IBM RESEARCH
© 2008 IBM CorporationJohnathan M. Reason
Intelligent Telemetry for Freight Trains using Wireless Sensor Networks
What we learned and next steps
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© 2008 IBM Corporation 2Johnathan M. Reason
Outline
Background on N.A. Freight Railroads
Why wireless sensor networks for railroads
Railroad sensor network solution
Some Results
Next Steps
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© 2008 IBM Corporation 3Johnathan M. Reason
The North America Railroad Industry
40% of U.S. freight travels by rail
• Major contributors are coal, chemicals, food, and machinery
• Intermodal rev. has been consistently growing
Railroads are three times as fuel-efficient as trucks
7 Class 1 railroads represent 90% of total freight revenue (each with over $320M in annual sales)
• Burlington Northern, Union Pacific, Canadian National Railway, Norfolk Southern, CSX, Kansas City Southern, Canadian Pacific Railway
30 Regional railroads
• e.g Florida East Coast Industries, …Hundreds of locals (short line operators)
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Union Pacific Railroad Fast Facts (2007 data)
• Largest railroad in NA• Op. Revenue $15.5B
• Industrial, energy, intermodal, agricultural, chemicals, auto, etc.
• Route Miles 32,300• Employees 50,000• Annual Payroll $3.7 billion• Purchases Made $6.9 billion• Locomotives 8,500• Freight Cars 104,700• Fuel efficiency 780 ton-mile/g• More than 70% of IT budget is
spent on supporting the operations
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Railroad track-side sensors: railcar identification and fault prevention
AEI Reader
Acoustic Sensor
Hot-box Detector
Wheel Impact Load Detector
AEI: Automatic Equipment Identification• NA railroad standard: identify railroad
equipment while enroute • passive UHF RFID tags mounted on each
side of rolling stock• trackside readers
• Adopted since early 1990’s• As of 2000, over 95% railcars were
tagged with 3000+ trackside readers In addition to AEI readers, additional sensors are
deployed along the track, including• Hot Box Detectors (bad bearings)• WILDs or Wheel Impact Load Detectors (bad
wheels)• TADs or Trackside Acoustical Detectors
(cracked or flat wheels)
AEI tag affixed to the side of a freight car.
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Outline Progress
Background on N.A. Freight Railroads
Why wireless sensor networks for railroads
Railroad sensor network solution
Some Results
Next Steps
IBM RESEARCH
© 2008 IBM Corporation 7Johnathan M. Reason
Problem SummaryData from trackside sensors are sparse
•Does not provide timely information to prevent or mitigate all problems (sample every 45 min, on avg.)
•Each technology is one-dimensional; not capable of supporting all the operational needs
•Does not scale well for multiple sensor modalities
Proposed next-generation infrastructure requires•On-board telemetry for real-time visibility, using wireless sensor nodes or motes
•One infrastructure supporting multiple sensor modalities
•One infrastructure for communicating data, control, and events
•Localized analytics•Demonstrable ROI•Large-scale deployment
Railcar Tracking
Bearing temperature
Brake control
Weight distribution
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Capabilities of a Wireless Sensor Node (or mote)?
Computation:• Low-power microProcessor
(e.g. TI MSP430)• Small amount of memory
(e.g. 10KB RAM, 48KB ROM)
Sensing:• Temperature and light onboard• Embedded A/D converter• SPI bus for expansion
Communication:• low-power energy efficient radio
(e.g. 802.15.4)
Iris mote from Crossbow
Mica2 from Crossbow
Design Tradeoffs: Energy Vs Performance Cost Vs Computational power and reliability
Communication
SensingComputation
Telosb from Crossbow
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Outline Progress
Background on N.A. Freight Railroads
Why wireless sensor networks for railroads
Railroad sensor network solution
Some Results
Next Steps
IBM RESEARCH
© 2008 IBM Corporation 10Johnathan M. Reason
SEAIT: Sensor-Enabled Ambient-Intelligent Telemetry for Trains
SEAIT is a WSN-based architecture and framework for building advanced railroad applications.
The framework provides a collection of protocols, services, and a data model that serve as the building blocks to enable intelligent telemetry through
• timelier sensing, • localized analytics, and• robust communications.
The architecture specifies an onboard infrastructure to facilitate real-time data capture and analysis for better visibility and in-field management of the rolling stock.
At the heart of the architecture are intelligent wireless sensing nodes that form the on-board WSN and continuously monitor the health of critical components (e.g., wheel bearings).
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Goals, Applications, and Benefits of SEAIT
Goal• Improve operational business objectives by providing real-time
visibility into the rolling stock
Some Enabled Applications• Real-time Fault Detection with Closed-loop Notification• Train Configuration Monitoring• Asset Tracking • Predicative Maintenance• Continuous Health Monitoring
Some Key Business Benefits• Schedule Optimization• Accident Prevention• Asset Utilization• Customer Satisfaction
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Basic Approach of SEAIT Illustrated through a Hot Bearing Detection Solution
WSN nodes • perform timelier sensing of wheel bearing temperature, • local analytics to detect overheated bearings, and • robust communications to relay “hot” bearing events to the gateway
Gateways • aggregate hot bearing events with other situational awareness data, • perform train-wide analytics, and then • provide closed-loop event notification directly to the engineer
WSN nodes communicate to gateways on locomotives or trackside gatewaysLocomotive gateways communicate to the enterprise via an uplink (Cellular, WiFi,
Satellite, proprietary RF bands, etc.)
...
a) hopper carf) locomotiveb) WSN node
c) thermocoupled) bearing adapter
e) gateway
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Key Technology Components
Gateway Software• Information model called the Railroad Business Object Model (RRBOM)
• RRBOM is the meta-model for all railroad objects (trains, cars, axles, wheels, bearings, motes, sensors, etc.)
• Uniform information model for enterprise applications to configure, query, and control the mote network
•Performs onboard, train-specific analytics (enables closed-loop control)•Supervise railroad communication protocols and services
WSN Node Software•Uniform information and messaging model for managing and reporting sensor, configuration, and application data; provides hooks to gateway to map into RRBOM
•All communication protocols and services to realize railroad applications and support application requirements
• Low Latency• High Reliability• Long Life
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Key Technical Challenges to Realizing the Benefits of WSN for Railroads
Detection and Measurement Accuracy• Reliable detection and prediction of catastrophic faults (e.g., over heated
bearing) with low false positive rate• Accurate reporting of train consist and parameters for operational optimization
Alert Latency• Predictable, low end-to-end latency from detection of a fault to alerting the
engineer of such an event over many hops
End-to-end Data Reliability• End-to-end reliability over many communication hops under various conditions
(weather, speed, terrains, ...)
Service Lifetime• The energy source for each mote must last at least the maintenance cycle of its
associated car (> 5 years)
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Gateway-to/from-Railroad WSN Architecture
Node ServicesRailroad ApplicationsHot Bearing
DetectionConsist
Identification
Car-to-Train Association
Dark Car Detection
Synchronization
ReportingInformation
IEEE 802.15.4
Rx/Tx Queues
Neighbor List
Router Message ManagerReceiverSender
WakeupsARQ Multisend Delay LQIPacket Delivery Packet Measurements
RSSI PSR
...
SchedulerDispatcherNext -hop List
Proto N
...P
hyL
ink
Net
wor
kA
pps
& S
ervi
ces
nodeId Addr Position Hops
Proto 1
Cost
Applications and services send and receive messages through the interface to the communication stack
The information and reporting services realize the execution a uniform information model for managing and reporting sensor, configuration, and application data
The synchronization service realizes simple and robust management of a software RTC
Network features time-scheduled queues and cross-layer optimized routing
Link features semantic-based wakeups and delay measurements
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Car D’s motes leave the network as car D is disjoined from the train and the train in no longer in range
Consist Identification: Car Disjoining from the Train
Problem: Dynamic join/disjoin of rail cars•No real-time or near real-time visibility of what cars are actually on the train
Possible Solution: Periodic car ID reporting via a Mote network• If one or more motes are uniquely associated with each car, then dynamic join/disjoin is a simply application that detects the presence/absence of a car-specific mote in the network
•Motes can detect the status of their car and change their mode of operation: join => active reporting, disjoin => hibernation
WaysideABCE D
motesgateway
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Consist Identification: Basic Operation
Iterative application that has four major phases: 1. Associate cars to the train2. Measure closeness between each neighboring car (or pair of nodes)3. Report closeness measurements4. Apply the ordering algorithm
Ordering AlgorithmConsidering n cars in a train {N | i = 1,...,n}, the ordering algorithm operates in three steps:
1. Compute a car closeness metric {dij} from the node measurements2. Refine the car closeness metric using a correlation based operator3. Construct a weighted digraph, G= (N,E), where each edge has a
weight of dij.
The closeness metric reflects the closeness between two cars Ni & Nj. The closer the two cars are, the greater the value for dij. Consist ID is equivalent to finding the max. Hamiltonian path for graph G.We use a greedy algorithm to construct this path.The gateway in the locomotive serves as an anchor node.
IBM RESEARCH
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Outline Progress
Background on N.A. Freight Railroads
Why wireless sensor networks for railroads
Railroad sensor network solution
Some Results
Next Steps
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© 2008 IBM Corporation 19Johnathan M. Reason
Proof-of-Concept (PoC) Testbed Deployed on the Roof our Yorktown Facility
Deployed 32 WSN platforms along the front metal railing of the roof to emulate a 16-car train
WSN platform: • TmoteSky node, a sensor board, batteries,
an embedded antenna, an input/output connection board, and a weatherproof enclosure. The sensor board included temperature, light, and accelerometer sensors.
On average, freight railroad cars are about 60 feet long, ranging from as little as 40 feet up to 90 feet
Two WSN platforms per car (one at each end), each car 60 feet long and an inter-car node spacing of 10 feet
Sample segment of the deployment showing four cars. The entire deployment spans about one fourth of a mile.
The curvature of the front face of the building is such that, from any point along the front edge, no more than 300 feet are visible via line-of-sight.
WSN Platform
Segment of Deployment
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PoC Results for Consist Identification
Setup:• Used periodic reporting with hop-based
routing. Period was every 2 minutes • During slot time, each node measured
closeness to its neighbors and reported these measurements to the gateway
• Closeness measurements consume most of the time during each slot
• Gateway runs Consist Identification algorithm• Error = # of cars that need to be moved to
match the actual consist
Key Observations:• Algorithm is robust within 1-car transpositions
or flips• A flip is equivalent to a 2-car error• Ignoring flips, the algorithm is 100% accurate
0
1
2
3
4
1 6 11 16 21 26 31 36 41 46Report Cycle
Error
including flips
ignoring flips
Flips ignored 0 1 2
Accuracy (%) 93.0 99.0 100.0
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Graphical view of a consist being constructed(a screenshot of the research prototype)
IBM RESEARCH
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Outline Progress
Background on N.A. Freight Railroads
Why wireless sensor networks for railroads
Railroad sensor network solution
Some Results
Next Steps
IBM RESEARCH
© 2008 IBM Corporation 23Johnathan M. Reason
Next Steps: Continue the conversation about industry standardization
PoC was a good starting pointPoC touched many areas requiring standardization
•Communication (mote-mote, mote-gateway)•Message/Query• Industry semantic model/ontology •Power•SW life-cycle management
Like RFID, broad adoption of WSN will be driven by industry applications and require industry collaboration
Network Layer
Link Layer
Physical Layer
Presentation Layer
Transport Layer
Application Layer
Burlington Northern started atesting program and selected two
vendors for full-scale testing
1/88 1500 cars were tagged8/88 reported 99.99% accuracy
AAR formed std. committeeMore RRs started testing
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
8/89 Amtech selectedData format defined
10/90 AEI std approved
8/91 AEI mandaterectified by AAR
3/92 - 12/94Mandatory rollout
1.4 millionrailcars tagged
Timeline: North America Railroads AEI Deployment
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Next Steps: Some Possibilities
Continue PoC investigation by conducting field tests on real trains
Quantify the value proposition of real-time visibility with research study•Does more timely data really yield greater efficiencies in operations?• If so, how much?•What localized analytics are needed?
Explore how WSN technology can complement positive train control•As the PTC industry standard develops, what conversation should the industry be having about a path to on-board sensing and actuation?
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Acknowledgements
Union Pacific Railroad•Lynden Tennison, Dan Rubin
IBM•Co-authors: Han Chen and Sastry Duri (IBM Research), Riccardo Crepaldi (Intern)
•Contributors: Maria Ebling and Paul Chou (IBM Research), Xianjin Zhu (Intern), Keith Dierkx (GRIC)
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Thanks for your attention.
Questions?