The document discusses using wireless sensor networks for remote condition monitoring and asset optimization. It describes how sensor networks can integrate various types of sensors, mobile devices, and manual inputs to provide real-time visibility, event monitoring, and optimization of complex systems like terminals, ports, and transportation assets. The sensor fabric allows integrating data from different sources to provide a common operating picture and optimize business processes.

1. Wireless Sensor Networks –
A Sensor Fabric for
Remote Condition Monitoring and
Asset Optimization
The Next Generation
John Dorn
© 2008 IBM Corporation

2. Which is Correct ?
1. A "Mote" is a fairy character
in William Shakespeare's play, "A Midsummer Night's Dream."
2. A “Mote” a small particle; a speck
Term was first used by Kris Pister to describe his Smart Dust
radio/sensors Term was later adopted by Dave Culler (TinyOS
author)
3. A “Mote” is a Wireless Mesh Low Power Sensor Node,
in a wireless sensor network that is capable of performing some
processing, gathering sensory information and communicating with
other connected nodes in the network* .
*Wikipedia
2

3. What is a Mote?
Technology:
• Hardware Components:
RTD100 from Sensicast
– Active radio frequency transceiver
• 433MHz, 900MHz, 2.4GHz
• 19.2kbps - 250kbps EMS100 from Sensicast
• 200 – 1000 feet LOS, outdoors
– 8- ,16-, 32-bit Microprocessor
• 8051, TI MSP430, Atmel ATmega128L,
ARM920T
– Memory: Flash & DRAM
Tmote Sky/TelosB
• 10kB-512kB RAM, 64kB-4MB Flash
from MoteIV/Crossbow
– Sensors
– A/D & D/A converters, 10, 12, 16-bit
• Software Components:
– Embedded programmable runtime:
• Executes protocols: MAC, network,
security Mica2 from Crossbow
• Executes application-specific code
• Mostly proprietary
• TinyOS/TinyDB & MANTIS open
source
Sun SPOT from Sun
• C-like programming, Java is emerging
3

4. Wireless Sensor Network
Sensing VolcanoNet
Computation
Communication
BridgeNet
Integrated Humidity, Temperature and Light sensors
8MHz Texas Instruments MSP430 microcontroller
mote 10kRAM, 48k Flash
250kbps 2.4GHz IEEE 802.15.4 Wireless Transceiver TurtleNet
4

5. What are mote-based
wireless sensor networks?
• Discovery
– Finding neighbors, gateways, and services
• Networking To Enterprise
– Routing data to gateways through a mesh of motes Network
• Communications Paradigms To Enterprise
– Query-Respond: Gateway queries network for data; Network
motes respond
– Event Triggered: Gateway configures motes to respond
upon some event happening
• Low-Power Strategies
– Low Power Sleep Modes: 10-20 uW for radios; 5-10 uW
for processors
– Duty Cycling: synchronous sleep scheduling
• Management
– Remote Configuration: Gateways issue commands to Gateway
change parameters, which alter mote behavior
Mote (routing & sensing)
– Remote Programming: Gateways send software updates
Mote (sensing only)
• Collaborative/Distributed Computation
– Localization, Aggregation, Object Tracking, Sensing Wireless Links
Wired Links
5

6. Bridge
Monitoring
2005
Joe Polastre, CTO with Sentilla 6

7. Rail Car
Monitoring
1 - Continuous real-time capture and analysis of critical
and periodic sensor data ;
- wheel bearing temperature trends + other sensors
- operational data - manifest verification,
car drop-off location and time, freight condition
2 - Event publication ;
events, alerts and alarms to
locomotives local crews and the enterprise
7

8. Train Linear Mesh Network
• Motes are mounted on the railcars.
–Transmission rang R of each mote covers multiple cars
• Gateways are installed at the locomotive
–One train may have one or more gateways
R R
• Multiple hop linear mesh network
car
8

9. Overview of Rail Application
Railcar
• Focus on freight trains. Tracking
• on operational and
economics
–Railcar tracking Consist
–Failure detection orientation
• Sensor networks
–Real-time data
–Online processing Bearing
temperature
Weight
railcar distribution
9

10. Scenarios used to design the WSN Rail system (partial)
1. Hot Box Detection* 12. Gateway and MOTE Network
2. Air Pressure and Brake Monitoring Remote Management,
3. EOT Function Configuration and Auto
Provisioning*
4. Wayside Gateway 13. Installation and Testing
5. Order of Cars in Consist (and 14. Repair and Replace in Field
orientation) 15. Emergency Messages, Event
6. Determine Length of Consist Notification*
16. Send External Notifications
7. Connect/Disconnect Car Detection
17. Capture and relay sensor readings
8. Dark Car Drive By* and control parameters*
9. Access Customer Cargo Data 18. Carry experimental data from ad
hoc sensor platforms
10. Intrusion/Tampering Detection
19. Hazmat Location, Condition and
11. Determine Weight of Train Tamper Detection
20. Bio/Chem/Rad Detection
21. AEI tag integration
* Original scenarios 4

11. Sensors in motion on Trucks and Bearings
Temperature
Weight
Vibration
Speed Location
Acoustics – (flat wheel)
Tampering – (open Lid)
11

12. Motes connected to sensors can have different
roles and modes
• Sensing Configuration • Periodic Reporting
– Additional bearing sensors attached – Technique used to improve reliability and
sensor interface reduce energy consumption
– Air pressure-based power subsystem – Basic concept is to trade-off latency for
reliability
– Does not perform routing
– Applicable to applications that have a
• Forwarding Configuration latency tolerance greater than real-time
– No bearing sensors attached to sensor
interface
• Alert Reporting
– Technique used to report critical events in
– Solar-based power subsystem near “real-time”
– Performs routing – Requires low latency
forwarding mote
mote mote
mote mote
S4_1 S3_1 S2_1 S1_1
sensing mote
12

13. Connecting to the enterprise
Enterprise
Multiple
as many
wireless
as Sensing ways off
140 Motes
coal hops G
Gateway on
cars Locomotive
S S S SS S S SS S S S
13

14. Connecting to the enterprise (side)
Enterprise
as many
as Sensing
140 Motes
coal hops Gateway on
cars Locomotive
S S S SS S S SS S S S
G
AEI
14

15. Rail Car Monitoring Vision
Other RRs
Operations/
Service Technicians Dispatch
Direct to Train Engineers
Hazmat Customers
Strategic
Companies
Partners
Dashboards
Engineering
Hazmat Enterprise Users
Analysts, Help Desk
Dark Car on Drive by Application
Server
Asset Monitoring
Event Management Database/
History
G
Wide Area
Network
Gateway Enterprise Service Bus
on G Message Bus - Event/Message Broker - WebServices Gateway
Wayside
450 4.5
400 4
O 2 S 1 1 & 1 2 & F u e ls y s
G 350 3.5
v _ m is 1 0 0 0 [0 ]
300 3
250 2.5
200 2
150 1.5
100 1
50 0.5 Avg Good Avg Bad Avg Distance Maximum Number of Percentage of
Belief Belief to Good Rules Cluster Size Clusters Signal Labeled Bad
0 0 4 (fuzzy terms) 4 4 4 4 4
12
36
60
108
120
132
144
156
168
180
192
204
216
228
240
252
264
276
288
300
312
324
336
348
360
372
384
9 6 .1
0 .0 5
2 4 .1
4 8 .1
7 2 .1
8 4 .1
0.611 1.0 972 19 1 100%
• Service & Parts • Logistics
Time
G Hazmat G
Dispatch • Parts
• Remote
• Operations • Maintenance Ordering Engineering
Diagnostics
•Customer Scheduling •Logistics Data Mining/
• Prognostics Analysis
Service • Work •Inventory
• Condition-Based NetCool
• Maximo Management •ERP
Maintenance
Asset Mgmt
Main Lines Yards, Trains & Customer Assets
Enterprise Business Systems, SOA Components, Applications and Users
15

16. User view of a consist, with drill down manifest, car type
orientation, sensor data, calculated data and alarms
16

17. An Even Bigger Picture
ine B
Short line Mainl Regional
Main
line
A
Shipper Short line Class 1 Car Owner Car Owner Class 1 Regional Consignee
Intermediate Switches Municipalities Intermodal Carriers
Other car owners Federal and State Organizations
Car Repair
International Entities UPS
Walmart
Fire Department
Military
From Brian Webb at 17

18. A Terminal Asset and Freight Monitoring Vision
Direct to
Service Technicians Shippers - Cargo Owners
Hazmat
Port Engineers Terminal
Companies
Operations
Customers
G G
Strategic
Partners
Yard Hazmat
Dashboards Local Government
Enterprise Users
Analysts, Help Desk
Event
Monitoring Database/
Visibility Sensor Event History
G G
Framework
New SOA
Optimization
Port Process
G SubSystem
Video Vehicles Wide Area
Network
Enterprise Service Bus
Message Bus - Event/Message Broker - WebServices Gateway
G
450 4.5
400 4
O 2 S 1 1 & 1 2 & F u e ls y s
350 3.5
v _ m is 1 0 0 0 [0 ]
300 3
250 2.5
200 2
150 1.5
Gateway 100
50
1
0.5 Avg Good
Belief
Avg Bad Avg Distance
Belief to Good Rules
Maximum
Cluster Size
Number of Percentage of
Clusters Signal Labeled Bad
0 0
or G 4 (fuzzy terms) 4 4 4 4 4
12
36
60
108
120
132
144
156
168
180
192
204
216
228
240
252
264
276
288
300
312
324
336
348
360
372
384
9 6 .1
0 .0 5
2 4 .1
4 8 .1
7 2 .1
8 4 .1
0.611 1.0 972 19 1 100%
• Service & Parts • Logistics
Time
Reader Dispatch • Remote Maximo
Cranes • Operations • Maintenance •Logistics
Diagnostics Asset
G G • Customer Scheduling •Inventory
• Prognostics Management
Video Service • Work •ERP
Video • Condition-Based
Management Maintenance
Ships 12345
E-Lock
Terminal - Trains Ships Cranes Trucks Assets Enterprise Business Systems, SOA, Applications and Users

19. A Terminal Asset and Freight Monitoring Vision
Direct to
Service Technicians Shippers - Cargo Owners
Hazmat
Port Engineers Terminal
Companies
Operations
Customers
G G
Strategic
Partners
Yard Hazmat
Dashboards Local Government
Sensor Fabric Sensor Event Enterprise Users
Analysts, Help Desk
integrating... Event
Monitoring
Fabric Database/
Visibility Sensor Event History
G G
Framework integrating...
Optimization
New SOA
Process
Port
G SubSystem
Video •Video
Vehicles
• Complex Event
Wide Area
•RFID
Network
Processing
Enterprise Service Bus
•Sensors motes Message Bus - Event/Message Broker - WebServices Gateway
G
• Process Integration
•Mobile devices • Process Optimization 450
400
4.5
4
O 2 S 1 1 & 1 2 & F u e ls y s
350 3.5
v _ m is 1 0 0 0 [0 ]
Gateway•Manual inputs
300 3
250 2.5
200 2
or G
with 150
100
50
0
1.5
1
0.5
0
Avg Good
Belief
4 (fuzzy terms)
Avg Bad Avg Distance
Belief to Good Rules
4 4
Maximum
Cluster Size
4
Number of Percentage of
Clusters Signal Labeled Bad
4 4
with
12
36
60
108
120
132
144
156
168
180
192
204
216
228
240
252
264
276
288
300
312
324
336
348
360
372
384
9 6 .1
0 .0 5
2 4 .1
4 8 .1
7 2 .1
8 4 .1
0.611 1.0 972 19 1 100%
• Service & Parts • Logistics
Time
Reader
Cranes • Operations LOB Applications
Dispatch
• Maintenance
• Remote
Diagnostics
•Logistics Maximo
enterprise data
Video
G G
Video
• Customer
Service
Scheduling
• Work
• Prognostics
• Condition-Based
•Inventory
•ERP
Asset
Management
Management Maintenance
Ships 12345
E-Lock
Terminal - Trains Ships Cranes Trucks Assets Enterprise Business Systems, SOA, Applications and Users

20. Very Harsh environment
20

21. AEI tags and readers have been out there for 15 years
SmartPass Reader can read
both Passive and Active AEI
tags
21

22. Next Generation Motes

23. WSN Solutions require Mote platforms that are complex
• Main modules: Tmote, Power-
Memory-Interface (PMI) board, solar
panel, sensor board, and GPS module
• All other components are integrated on Mote
the PMI or are connected to the
external sensor interface Analog Control I2C UART Vcc/
I/O I/O Bus Bus Gnd
• Modular design: Tmote, sensor board,
power scavenging with solar panels, or 5 5 2 2 2
any of the external sensors can be
replaced with any alternatives that
Solar Panel
adhere to the same interfaces V
Power-Memory-Interface Board
• External analog sensors attach via
micro screw I/O terminal
16 8 16
• External sensor lines are addressable
from external
over I2C bus GPS sensors
P Sensor
Module Board
P P
23

24. Six steps to Deployments for North America
1. Lab Prototype (November, 2006. through November, 2007): Initial Mote
implementation working in lab setting, but not necessarily in a railroad environment.
Gateway integrated with Motes. Interim deliverable (TBD) late June (consist join/dis-
join, dark car)
2. Field Testing (February, 2008 through August, 2008): MOTEs enclosed to operate in
a railroad environment. Measurements and evaluation exercises in a controlled railroad
here environment. Gateway to enterprise communications implemented. Both wayside and
engine-hosted gateway configurations will be tested. Validation of business case.
3. Deployment Prototype (March, 2008 – October, 2008): MOTE hardware redesign for
reduced manufacturing cost incorporating lessons learned during field testing.
Continued improvement and refinement of Gateway to/from enterprise inter-operation,
and integration to enterprise systems.
4. Pilot Deployment (November, 2008 – June, 2009): Initial deployment of MOTE sensor
network into an operational environment on a limited basis to continue to identify and
correct hardware, software, and system short-fallings. Identify and correct any
emerging performance issues with the system. Re-validation of business case.
5. Production Platform Manufacturing (concurrent with Pilot Deployment): Working
with one or more manufacturers of devices (MoteIV, Arch Rock, Crossbow). Additional
engineering challenges need to be identified and addressed. Any production platform
must be made with a standardized sensor and power interface as specified by the
architecture.
6. Production Roll-out (TBD): equip railcars with MOTEs and sensor packages, install
rail-side gateways, and start installing engine gateways.
24

25. Thank You -- Questions ?
John Dorn
Wireless Sensor Networks Solutions
Condition Monitoring, Asset Optimization
+1 917-453-9863
jzdorn@us.ibm.com
25

26. 26

27. Event Optimization:
the Next Generation of Condition Monitoring
5. Business Optimization
4. Process Integration
3. Event optimization
2. Event publication
• events, alerts and alarms integrated with operational
processes
1. Continuous real-time capture and analysis of
critical and periodic sensor data
• operational data - manifest verification, car drop-off
location and time, freight condition
• temperature trends + other sensors
• video events and OCR
• RFid events and data

28. The Bigger Picture – from Railinc.
ine B
Short line Mainl Regional
Main
line
A
Shipper Short line Class 1 Car Owner Car Owner Class 1 Regional Consignee
Intermediate Switches Municipalities Intermodal Carriers
Other car owners Federal and State Organizations
Car Repair
International Entities UPS
Walmart
Fire Department
Military

29. IUN reference architecture for distributed analytics

30. From patent application
Architectural Approach: Big Building Blocks
Summary: XML over Comm Enterprise
Comm. Comm.
on Locomotives G Adapter Adapter
and
on Wayside
COMM COMM
R Native Comm Protocol
sensor
M
Intra-Mote Wireless Mote wired interface
Protocol (read/write primitive data
registers)
Multi-hop Mote to Mote
Rail Customer
on Rail Cars
M M M Container Controllers
and M
on Customer assets sensor Analog/Digital Sensor
sensor
interfaces

31. Example layout of Rfid readers at a rail facility
Approximately
22 Points of
Interests
Each RFiD point
of interest needs a
fixed reader and a
communications
infrastructure
31

32. WSNs can have continuous monitoring and periodic event
reporting based on a set of rules or conditions that you can
specify and change remotely….
Approximately
22 Points of
Interests
Each RFid point of
interest needs a
fixed reader and a
communications
infrastructure
with WSN
Each Point of
interest can be
a set of
calculated
events that are
programmed
into the
Wireless Sensor
Network and
these events
can be changed
remotely
32

33. Some of the Challenges
• Very Low power (<40mW) needs Supercaps batteries,
small size, cost is ?
• Wireless receive and transmit, 1mW -10mW and
internal vs external antennas
• Packaging, mounting, low tech installation, field
replacement
• Remotely provisioned, configured on demand, by
location
• Remotely managed over a 5+ year life - without being
touched
• Power scavenging, charging multiple type cells, and
distribution of power to components, sensors
• Very Harsh environment – Low and Very Hot temps.
• Complex event processing at the enterprise
33