Intra-vehicular wireless sensor networks (IVWSN) is a promising new research area that can provide part cost, assembly, maintenance savings and fuel efficiency through the elimination of the wires, and enable new sensor technologies to be integrated into vehicles, which would otherwise be impossible using wired means, such as Intelligent Tire. However, there are currently no detailed models describing the UWB channel for IVWSN making it difficult to design a suitable communication system. We analyze the small- scale and large-scale statistics of the UWB channel beneath the chassis of a vehicle by collecting data at various locations with 81 measurement points per transmitter-receiver pair for different types of vehicles including the scenarios of turning the engine on and movement on the road. Moreover, the close interaction of communication with control systems, strict reliability, energy efficiency and delay requirements in such harsh environment containing a large number of reflectors operating at extreme temperatures are distinguishing properties of this network. We investigate optimal power control, rate adaptation and scheduling for UWB-based IVWSN for one Electronic Control Unit (ECU) and multiple ECU cases.

1. Intra-Vehicular Wireless Sensor Networks
Sinem Coleri Ergen
Wireless Networks Laboratory,
Electrical and Electronics Engineering,
Koc University

2. Outline
 Motivation for Intra-Vehicular Wireless Sensor Networks
 Physical Layer Design
 Medium Access Control Layer
 Conclusion
 Current Projects at WNL

3. Outline
 Motivation for Intra-Vehicular Wireless Sensor Networks
 Physical Layer Design
 Medium Access Control Layer
 Conclusion
 Current Projects at WNL

4. History of In-Vehicle Networking
 Early days of automotive electronics
 Each new function implemented as a stand-alone ECU, subsystem
containing a microcontroller and a set of sensors and actuators
 Data exchanged between point-to-point links
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5. History of In-Vehicle Networking
 In the 1990s
 Increase in the number of wires and connectors caused weight, cost,
complexity and reliability problems
 Developments in the wired communication networks
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6. History of In-Vehicle Networking
 In the 1990s
 Increase in the number of wires and connectors caused weight, cost,
complexity and reliability problems
 Developments in the wired communication networks
 Multiplexing communication of ECUs over a shared link called bus
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7. History of In-Vehicle Networking
 Today
 Increases in number of sensors as electronic systems in vehicles are
replacing purely mechanical and hydraulic systems causes weight, cost,
complexity and reliability problems due to wiring
 Advances in low power wireless networks and local computing
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8. History of In-Vehicle Networking
 Today
 Increases in number of sensors as electronic systems in vehicles are
replacing purely mechanical and hydraulic systems causes weight, cost,
complexity and reliability problems due to wiring
 Advances in low power wireless networks and local computing
 Intra-Vehicular Wireless Sensor Networks (IVWSN)
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9. First IVWSN Example: Intelligent Tire
Active Safety Systems
•Change the behavior of vehicle in pre-crash
time or during the crash event to avoid the
crash altogether
•Examples: Anti-lock Braking System (ABS),
Traction Control System (TCS), Electronic
Stability Program (ESP), Active Suspension
System
Requires accurate and fast estimation of
vehicle dynamics variables
•Forces, load transfer, actual tire-road friction,
maximum tire-road friction available
On-board sensors + indirect estimation
Enable a wide range of new applications
Intelligent Tire
•More accurate estimation
•Even identify road surface condition in
real-time

10. IVWSN: Distinguishing Characteristics
 Tight interaction with control systems
 Sensor data used in the real-time control of mechanical parts in different
domains of the vehicles
 Very high reliability
 Same level of reliability as the wired equivalent
 Energy efficiency
 Remove wiring harnesses for both power and data
 Heterogeneity
 Wide spectrum for data generation rate of sensors in different domains
 Harsh environment
 Large number of metal reflectors, a lot of vibrations, extreme temperatures
 Short distance
 Maximum distance in the range 5m-25m

11. Outline
 Motivation for Intra-Vehicular Wireless Sensor Networks
 Physical Layer Design
 Medium Access Control Layer
 Conclusion
 Current Projects at WNL

12. Wireless Channel Measurements
 Building a detailed model for IVWSN requires
 Classifying the vehicle into different parts of similar propagation
characteristics
 Collecting multiple measurements at various locations belonging
to the same class
engine
passenger compartment
beneath chassis
trunk

13. Wireless Channel Model: Beneath Chassis
 81*18 measurement points at
 Two different vehicles: Fiat Linea and Peugeot Bipper
 Different scenarios: engine off, engine on, moving on the road

14. Channel Model: Large Scale Statistics
 Path loss model

15. Channel Model: Large Scale Statistics
 General shape of impulse response: Saleh-Valenzuela Model
inter-arrival time
of clusters
cluster amplitude
ray decay rate

16. Channel Model: Small Scale Statistics
 Characterized by fitting 81 amplitude values to
alternative distributions

17. Channel Model: Simulation Results
 Qualitative comparison  Quantitative comparison
experimental power delay profile
simulated power delay profile

18. Outline
 Motivation for Intra-Vehicular Wireless Sensor Networks
 Physical Layer Design
 Medium Access Control Layer
 Conclusion
 Current Projects at WNL

19. Medium Access Control Layer: System Requirements
 Packet generation period, transmission delay and
reliability requirements:
(Tl ,dl ,rl )
 Network Control Systems: sensor data -> real-time control of
mechanical parts:
 Automotive system, no automatic way of validating the performance of
control systems for different
-> use extensive simulations of closed loop models for a given
 Main characteristics:
(Tl ,dl ,rl )
 Fixed determinism better than bounded determinism in control systems
 As increases, the upper bound on decreases down to zero
Tl
dl
(Tl ,dl ,rl )

20. Medium Access Control Layer: System Requirements
 Adaptivity requirement
 Nodes should be scheduled as uniformly as possible
EDF
Uniform

21. Medium Access Control Layer: System Requirements
 Adaptivity requirement
 Nodes should be scheduled as uniformly as possible
1
EDF Uniform

22. Medium Access Control Layer: System Requirements
 Adaptivity requirement
 Nodes should be scheduled as uniformly as possible
2
EDF Uniform

23. Medium Access Control Layer: System Requirements
 Adaptivity requirement
 Nodes should be scheduled as uniformly as possible
3
EDF Uniform

24. Medium Access Control Layer: System Model
(Tl ,dl ,rl )
T1 £ T2 £ ... £ TL
 given for each link l

 Choose subframe length as for uniform allocation
 Assume is an integer: Allocate every subframes
 Uniform distribution minimize max subframe active time
Ti /T1 = si
T1
si
º
EDF
Uniform
max active time=0.9ms
max active time=0.6ms
✓

25. Medium Access Control Layer: One ECU
Maximum active time of subframes
Periodic packet generation
Delay requirement
Energy requirement
Maximum allowed power by UWB regulations
Transmission time
Transmission rate of UWB for no
concurrent transmission case

26. Medium Access Control Layer: One ECU
 Optimal power and rate allocation is independent of optimal
scheduling
 One link is active at a time
 Given transmit power, both time slot length and energy
minimized at maximum rate
 Maximum rate and minimum energy at and

27. Medium Access Control Layer: One ECU
 Optimal scheduling problem decomposed from optimal power
and rate allocation: Mixed Integer Programming Problem
Periodic packet
generation
Maximum active time of subframes
 NP-hard: Reduce the NP-hard Minimum Makespan Scheduling
Problem on identical machines to our problem.

28. Medium Access Control Layer: One ECU
 Smallest Period into Shortest Subframe First (SSF) Scheduling
 2-approximation algorithm

29. Medium Access Control Layer: One ECU
 SSF Scheduling:

30. Medium Access Control Layer: One ECU Simulations
 Use intra-vehicle UWB channel model
 Ten different random selection out of
predetermined locations

31. Medium Access Control Layer: Multiple ECU
 How to exploit concurrent transmission to multiple ECUs to
decrease the maximum active time of subframes?
 Allow concurrent transmission of sensors with the same packet
generation period -> fixed length slot over all frame assignment
What is the power, rate allocation and resulting length of time slot
if they are combined?
How to decide which nodes are combined?

32. Medium Access Control Layer: Multiple ECU
 Optimal power allocation for the concurrent transmission of n
links: Geometric Programming Problem
-> Power control needed in UWB Packet based networks
Delay requirement
Energy requirement
Transmission time=
packet length/
rate of UWB for
concurrent transmission

33. Medium Access Control Layer: Multiple ECU
 Which slots to combine?
-> Mixed Integer Linear Programming problem
 Propose Maximum Utility based Concurrency Allowance
Scheduling Algorithm
 Define utility of a set: decrease in transmission time when
concurrent
 In each iteration, add the node that maximized utility
 Until no more node can be added to increase utility

34. Medium Access Control Layer: Multiple ECU

35. Outline
 Motivation for Intra-Vehicular Wireless Sensor Networks
 Physical Layer Design
 Medium Access Control Layer
 Conclusion
 Current Projects at WNL

36. Conclusion
 Intra-vehicular wireless sensor networks
 Increases in number of sensors causes weight, cost, complexity and
reliability problems due to wiring
 Advances in low power wireless networks and local computing
 Physical layer
 Large scale-statistics: path loss, power variation
 General shape of impulse response: Modified Saleh-Valenzuela model
 Small-scale statistics
 Medium access control layer
 Adaptivity requirement: Minimize maximum active of subframes
 Tight interaction with vehicle control systems
 Delay, energy and reliability requirements
 One ECU: 2-approximation algorithm
 Multiple ECU: Utility based algorithm to decrease subframe length

37. Publications
 Y. Sadi and S. C. Ergen, “Optimal Power Control, Rate Adaptation and
Scheduling for UWB-Based Intra-Vehicular Wireless Sensor Networks”, IEEE
Transactions on Vehicular Technology, vol. 62, no. 1, pp. 219-234, January 2013. [pdf
| link]
 C. U. Bas and S. C. Ergen, “Ultra-Wideband Channel Model for Intra-Vehicular
Wireless Sensor Networks Beneath the Chassis: From Statistical Model to
Simulations”, IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 14-25,
January 2013. [pdf | link]
 U. Demir, C. U. Bas and S. C. Ergen, "Engine Compartment UWB Channel Model
for Intra-Vehicular Wireless Sensor Networks", IEEE Transactions on Vehicular
Technology, vol. 63, no. 6, pp. 2497-2505, July 2014. [pdf | link]

38. Outline
 Motivation for Intra-Vehicular Wireless Sensor Networks
 Physical Layer Design
 Medium Access Control Layer
 Conclusion
 Current Projects at WNL

39. Current Projects

40. People

41. Thank You!
Sinem Coleri Ergen: sergen@ku.edu.tr
Personal webpage: http://home.ku.edu.tr/~sergen
Wireless Networks Laboratory: http://wnl.ku.edu.tr