IntraVehicular Wireless Sensor Networks Sinem Coleri Ergen Wireless

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

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

Outline q q q Motivation for Intra-Vehicular Wireless Sensor Networks Physical Layer Design Medium Access Control Layer Conclusion 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 Body Control Module ECU sensor

History of In-Vehicle Networking q In the 1990 s q Increase in the number of wires and connectors caused weight, cost, complexity and reliability problems q Developments in the wired communication networks Body Control Module ECU sensor ECU actuator sensor

History of In-Vehicle Networking q In the 1990 s 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 Body Control Module ECU sensor ECU actuator sensor

History of In-Vehicle Networking 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 Body Control Module sensor ECU ECU sensor actuator sensor

History of In-Vehicle Networking 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) Body Control Module sensor ECU ECU sensor actuator sensor

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 Enable a wide range of new applications • Forces, load transfer, actual tire-road friction, maximum tire-road friction available Intelligent Tire • More accurate estimation On-board sensors + indirect estimation • Even identify road surface condition in real-time

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 5 m-25 m

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

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 passenger compartment engine trunk beneath chassis

Wireless Channel Model: Beneath Chassis 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 inter-arrival time of clusters cluster amplitude ray decay rate

Channel Model: Small Scale Statistics q Characterized by fitting 81 amplitude values to alternative distributions

Channel Model: Simulation Results q Qualitative comparison experimental power delay profile simulated power delay profile q Quantitative comparison

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

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

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

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

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

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

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 max active time=0. 9 ms EDF max active time=0. 6 ms Uniform ✓

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

Medium Access Control Layer: One ECU 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

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

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

Medium Access Control Layer: One ECU 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?

Medium Access Control Layer: Multiple ECU q 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

Medium Access Control Layer: Multiple ECU 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

Medium Access Control Layer: Multiple ECU

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

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

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

People • Sinem Coleri Ergen Director Ph. D. Students M. S. Students • Yalcin Sadi | Seyhan Ucar | Merve Saimler | Elif Dilek Salik | Utku Demir | Ali Vosoughi | Bugra Turan | Melih Karaman • Anique Akhtar | Bakhtiyar Farayev • Umit Bas | Nabeel Akhtar | Irem Nizamoglu | Mehmet Kontik Alumni

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