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

Intra-Vehicular Wireless Sensor Networks

Sinem Coleri Ergen

Wireless Networks Laboratory,

Electrical and Electronics Engineering,

Koc University

Outline

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

Outline


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

sensorsensor sensorsensor

ECUECU

Body Control Module

Body Control Module

ECUECU


In the 1990s Increase in the number of wires and connectors caused weight, cost,

complexity and reliability problems Developments in the wired communication networks

sensorsensor

ECUECU

sensorsensor actuatoractuator sensorsensor

ECUECUECUECU

Body Control Module

Body Control Module


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

sensorsensor

ECUECU


ECUECUECUECU

Body Control Module

Body Control Module


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

sensorsensor

ECUECU


ECUECUECUECU

Body Control Module

Body Control Module


sensorsensor

sensorsensor

sensorsensorsensorsensor

ECUECU

sensorsensor


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)

sensorsensor

ECUECU


ECUECUECUECU

Body Control Module

Body Control Module


sensorsensor

sensorsensor

sensorsensorsensorsensorsensorsensor

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

Intelligent Tire

•More accurate estimation

•Even identify road surface condition in real-time

Enable a wide range of new applications

First IVWSN Example: Intelligent Tire

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

Outline


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

beneath chassis

passenger compartment

trunk

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

Channel Model: Large Scale Statistics

Path loss model

Channel Model: Large Scale Statistics

General shape of impulse response: Saleh-Valenzuela Model

cluster amplitude

ray decay rate

inter-arrival time of clusters

Channel Model: Small Scale Statistics

Characterized by fitting 81 amplitude values to alternative distributions

Channel Model: Simulation Results

Qualitative comparison Quantitative comparison

experimental power delay profile

simulated power delay profile

Outline


Medium Access Control Layer: System Requirements

Packet generation period, transmission delay and reliability requirements: 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: Fixed determinism better than bounded determinism in control systems As increases, the upper bound on decreases down to zero


Adaptivity requirement Nodes should be scheduled as uniformly as possible

EDF

Uniform



EDF Uniform

1



2

EDF Uniform



3

EDF Uniform

Medium Access Control Layer: System Model 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

EDF

Uniform

max active time=0.9ms

max active time=0.6ms

✓

Medium Access Control Layer: One ECU

Transmission rate of UWB for no concurrent transmission case

Transmission time

Maximum allowed power by UWB regulations

Energy requirement

Delay requirement

Periodic packet generation

Maximum active time of subframes


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


Optimal scheduling problem decomposed from optimal power and rate allocation: Mixed Integer Programming Problem

NP-hard: Reduce the NP-hard Minimum Makespan Scheduling Problem on identical machines to our problem.

Periodic packet generation

Maximum active time of subframes


Smallest Period into Shortest Subframe First (SSF) Scheduling 2-approximation algorithm


SSF Scheduling:

Medium Access Control Layer: One ECU Simulations

Use intra-vehicle UWB channel model Ten different random selection out of

predetermined locations

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?


Optimal power allocation for the concurrent transmission of n links: Geometric Programming Problem

-> Power control needed in UWB Packet based networks

Transmission time=packet length/rate of UWB for concurrent transmission

Energy requirement

Delay requirement


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


Outline


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

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]

Outline


Current Projects

People

Thank You!

Sinem Coleri Ergen: [email protected]

Personal webpage: http://home.ku.edu.tr/~sergen

Wireless Networks Laboratory: http://wnl.ku.edu.tr

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

Documents

control systems sensor

standalone ecu

wnl slide

intelligent tire slide

low power wireless networks

number of sensors

electronic systems

koc university slide