Intelligent Telemetry for Freight Trains using Wireless Sensor Networks

1. Intelligent Telemetry for Freight Trains using Wireless Sensor Networks What we learned and next steps

2. Outline • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

3. 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)

4. 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

5. Hot-box Detector AEI Reader AEI tag affixed to the side of a freight car. Acoustic Sensor Wheel Impact Load Detector Railroad track-side sensors: railcar identification and fault prevention • 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)

6. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

7. Railcar Tracking Brake control Bearing temperature Weight distribution Problem Summary • Data 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

8. 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) Sensing Computation Communication Telosb from Crossbow • Design Tradeoffs: • Energy Vs Performance • Cost Vs Computational power and reliability

9. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

10. 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).

11. 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

12. 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 gateways • Locomotive gateways communicate to the enterprise via an uplink (Cellular, WiFi, Satellite, proprietary RF bands, etc.)

13. 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

14. 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)

15. Gateway-to/from-Railroad WSN Architecture • 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

16. Car D’s motes leave the network as car D is disjoined from the train and the train in no longer in range E D 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 reportingviaaMote 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 acar-specific mote in the network • Motes can detect the status of their car and change their mode of operation: join => active reporting, disjoin => hibernation gateway motes Wayside A C B

17. Consist Identification: Basic Operation • Iterative application that has four major phases: • Associate cars to the train • Measure closeness between each neighboring car (or pair of nodes) • Report closeness measurements • Apply the ordering algorithm • Ordering Algorithm Considering n cars in a train {N | i = 1,...,n}, the ordering algorithm operates in three steps: • Compute a car closeness metric {dij} from the node measurements • Refine the car closeness metric using a correlation based operator • 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.

18. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

19. Proof-of-Concept (PoC) Testbed Deployed on the Roof our Yorktown Facility Segment of Deployment • 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

20. 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

21. Graphical view of a consist being constructed(a screenshot of the research prototype)

22. Outline Progress • Background on N.A. Freight Railroads • Why wireless sensor networks for railroads • Railroad sensor network solution • Some Results • Next Steps

23. Application Layer Presentation Layer Transport Layer Network Layer Link Layer Physical Layer Next Steps: Continue the conversation about industry standardization Timeline: North America RailroadsAEI Deployment • PoC was a good starting point • PoC 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

24. 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?

25. 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)

26. Thanks for your attention. Questions?