1. Ultra-Wideband Channel Model for Intra-Vehicular Wireless Sensor Networks C. Umit Bas Electrical and Electronics Engineering, Koc University

2. 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 sensor ECU Body Control Module ECU

3. 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 sensor ECU sensor actuator sensor ECU Body Control Module

4. 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 sensor ECU sensor actuator sensor ECU Body Control Module

5. 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 sensor ECU sensor actuator sensor ECU Body Control Module sensor ECU sensor

6. 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) sensor ECU sensor actuator sensor ECU Body Control Module sensor

7. Motivation for Intra-Vehicular Wireless Sensor Networks Provide savings in Part cost Cost of assembly, repair and maintenance Fuel consumption Decreases cost of change/inflexibility Cabling connectivity has little design flexibility and upgrades Enable new sensor technologies to be integrated into vehicles E.g. tire pressure monitoring systems, intelligent tire Replace current sensors not functioning well enough due to cabling.

8. 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

9. What is UWB? Transmission from an antenna for which the emitted signal bandwidth exceeds the lesser of 500MHz and 20% of the center frequency.

10. Motivation for Ultra-Wideband Vehicle control systems require Very high reliability, Strict delay guarantee. UWB provides Resistance to multipath fading, Resistance to power loss due to lack of line of sight, Resistance to interference, Robust performance at high data rate with very low transmit power.

11. 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

12. Literature Review

13. Measurement Setup Agilent 8719ES Vector Network Analyzer 3.1 GHz to 10.6 GHz using 1601 points

14. Measurement Locations 18 transmitter locations & 1 receiver location At each location 9 antenna positions on 3x3 square grids with 5 cm spacing Totally 81*18 measurement points

15. Data Processing

16. Large & Small Scale Fading Statistics Large-Scale Statistics 81 measurements at each location averaged to obtain the small- scale averaged PDP (SSA-PDP) Large-scale statistics derived by using 18 SSA-PDP Small-Scale Statistics Variations of 81 Local PDP around SSA-PDP used to derive small-scale statistics

17. Large Scale Statistics Modeled by using small scale averaged power delay profiles (SSA-PDP) for 18 locations

18. Path Loss Model

19. General Shape of Impulse Response Modified Saleh-Valenzuela Model cluster amplitude ray decay rate inter-arrival time of clusters

20. Small Scale Statistics Characterized by fitting amplitude values of 81 local PDP to alternative distributions

21. Distribution of Amplitudes for Delay Bins

22. σ of Lognormal Distributions σ is independent of time

23. σ of Lognormal Distributions means of σ have no trivial relation with distance

24. σ of Lognormal Distributions Small scale fading of different delays is not correlated

25. Susceptibility of Large-Scale Statistics

26. Susceptibility of Small-Scale Statistics

27. Regeneration of Statistical Channel Model

28. Model Validation – Qualitative Comparison Measured Power Delay ProfilesSimulated Power Delay Profiles

29. Model Validation – Quantitative Comparision

30. Conclusions Intra-vehicular wireless sensor networks Provide cost reduction Enable new sensor technologies to be integrated in vehicles Channel characteristics beneath the chassis Large scale statistics: path loss, power variation, General shape of impulse response: modified Saleh-Valenzuela model Small scale statistics Proposed model validated with both qualitative and quantitative comparisons

31. Publications 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] pdflinkpdflink 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] pdflinkpdflink C. U. Bas and S. C. Ergen, “Ultra-Wideband Channel Model for Intra-Vehicular Wireless Sensor Networks”, IEEE WCNC, April 2012. [pdf | link] pdflinkpdflink

32. 32 Thank You! QUESTIONS? Umit Bas: cbas@ku.edu.trcbas@ku.edu.tr Wireless Networks Laboratory: http://wnl.ku.edu.trhttp://wnl.ku.edu.tr