Intra-Vehicular Wireless Sensor Networks - #hayalinikeşfet

Intra-Vehicular Wireless Sensor Networks

Sinem Coleri Ergen Wireless Networks Laboratory,

Electrical and Electronics Engineering, Koc University

Outline

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

History of In-Vehicle Networking

q  Early days of automotive electronics q  Each new function implemented as a stand-alone ECU, subsystem

containing a microcontroller and a set of sensors and actuators q  Data exchanged between point-to-point links

sensor sensor

ECU

Body Control Module

ECU


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

complexity and reliability problems q  Developments in the wired communication networks

sensor

ECU

sensor actuator sensor

ECU ECU

Body Control Module


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

complexity and reliability problems q  Developments in the wired communication networks q  Multiplexing communication of ECUs over a shared link called bus

sensor

ECU


ECU ECU

Body Control Module


q  Today q  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

q  Advances in low power wireless networks and local computing

sensor

ECU


ECU ECU

Body Control Module

sensor sensor

sensor

sensor

sensor sensor

ECU

sensor


q  Today q  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

q  Advances in low power wireless networks and local computing q  Intra-Vehicular Wireless Sensor Networks (IVWSN)

sensor

ECU


ECU ECU

Body Control Module

sensor sensor

sensor

sensor

sensor sensor sensor

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

q  Tight interaction with control systems q  Sensor data used in the real-time control of mechanical parts in different

domains of the vehicles

q  Very high reliability q  Same level of reliability as the wired equivalent

q  Energy efficiency q  Remove wiring harnesses for both power and data

q  Heterogeneity q  Wide spectrum for data generation rate of sensors in different domains

q  Harsh environment q  Large number of metal reflectors, a lot of vibrations, extreme temperatures

q  Short distance q  Maximum distance in the range 5m-25m

Outline


Wireless Channel Measurements

q Building a detailed model for IVWSN requires q Classifying the vehicle into different parts of similar propagation

characteristics q Collecting multiple measurements at various locations belonging

to the same class

engine

beneath chassis

passenger compartment

trunk

Wireless Channel Model: Beneath Chassis

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q  81*18 measurement points at q  Two different vehicles: Fiat Linea and Peugeot Bipper q  Different scenarios: engine off, engine on, moving on the road

Channel Model: Large Scale Statistics

q Path loss model

Channel Model: Large Scale Statistics

q  General shape of impulse response: Saleh-Valenzuela Model

cluster amplitude

ray decay rate

inter-arrival time of clusters

Channel Model: Small Scale Statistics

q Characterized by fitting 81 amplitude values to alternative distributions

Channel Model: Simulation Results

q Qualitative comparison q Quantitative comparison

experimental power delay profile

simulated power delay profile

Outline


Medium Access Control Layer: System Requirements

q Packet generation period, transmission delay and reliability requirements: q Network Control Systems: sensor data -> real-time control of

mechanical parts: q Automotive system, no automatic way of validating the performance of

control systems for different -> use extensive simulations of closed loop models for a given

q Main characteristics: q Fixed determinism better than bounded determinism in control systems q As increases, the upper bound on decreases down to zero

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q Adaptivity requirement q Nodes should be scheduled as uniformly as possible

EDF

Uniform



EDF Uniform

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Medium Access Control Layer: System Model

q  IVWSN contains q A certain number of ECUs q A large number of sensor nodes

q  One ECU selected as central controller q Responsible for synchronization and resource allocation

sensor

ECU


ECU ECU

Body Control Module

sensor sensor

sensor

sensor

sensor sensor sensor

Medium Access Control Layer: System Model

q  given for each link l q  q  Choose subframe length as for uniform allocation q  Assume is an integer: Allocate every subframes

q  Uniform distribution minimize max subframe active time

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T1 ≤ T2 ≤ ...≤ TL

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Ti /T1 = si

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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


q  Optimal power and rate allocation is independent of optimal scheduling q One link is active at a time q Given transmit power, both time slot length and energy

minimized at maximum rate

q Maximum rate and minimum energy at and


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

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

Periodic packet generation

Maximum active time of subframes


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


q  SSF Scheduling:

Medium Access Control Layer: One ECU Simulations

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

predetermined locations

Medium Access Control Layer: Multiple ECU

q  How to exploit concurrent transmission to multiple ECUs to decrease the maximum active time of subframes? q 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?


q  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


q  Which slots to combine? -> Mixed Integer Linear Programming problem

q  Propose Maximum Utility based Concurrency Allowance Scheduling Algorithm

q Define utility of a set: decrease in transmission time when concurrent

q  In each iteration, add the node that maximized utility q Until no more node can be added to increase utility

Outline


Conclusion

q  Intra-vehicular wireless sensor networks q  Increases in number of sensors causes weight, cost, complexity and

reliability problems due to wiring q  Advances in low power wireless networks and local computing

q  Physical layer q  Large scale-statistics: path loss, power variation q  General shape of impulse response: Modified Saleh-Valenzuela model q  Small-scale statistics

q  Medium access control layer

q  Adaptivity requirement: Minimize maximum active of subframes q  Tight interaction with vehicle control systems q  Delay, energy and reliability requirements q  One ECU: 2-approximation algorithm q  Multiple ECU: Utility based algorithm to decrease subframe length

Publications

q  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]

q  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]

q  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

Intra-Vehicular Wireless Sensor Networks • Supported by Marie Curie Reintegration Grant

Energy Efficient Robust Communication Network Design for Wireless Networked Control Systems • Supported by TUBITAK (The Scientific and Technological Research Council of Turkey)

Energy Efficient Machine-to-Machine Communications • Supported by Turk Telekom

Cross-layer Epidemic Protocols for Inter-vehicular Communication Networks • Supported by Turk Telekom

RSSI Fingerprinting based Mobile Phone Localization with Route Constraints • Supported by UC Berkeley

Intra Vehicular Sensor Networks • Supported by TOFAŞ

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 - #hayalinikeşfet

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