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 Motivation for Intra-Vehicular Wireless Sensor Networks Physical Layer Design Medium Access Control Layer Conclusion Current Projects at WNL
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
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
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
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
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
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 Motivation for Intra-Vehicular Wireless Sensor Networks Physical Layer Design Medium Access Control Layer Conclusion Current Projects at WNL
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 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 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
Medium Access Control Layer: System Requirements Adaptivity requirement Nodes should be scheduled as uniformly as possible EDF Uniform
Medium Access Control Layer: System Requirements Adaptivity requirement Nodes should be scheduled as uniformly as possible EDF Uniform 1
Medium Access Control Layer: System Requirements Adaptivity requirement Nodes should be scheduled as uniformly as possible 2 EDF Uniform
Medium Access Control Layer: System Requirements Adaptivity requirement Nodes should be scheduled as uniformly as possible 3 EDF Uniform
Medium Access Control Layer: System Model IVWSN contains A certain number of ECUs A large number of sensor nodes One ECU selected as central controller Responsible for synchronization and resource allocation sensor ECU sensor actuator sensor ECU Body Control Module sensor
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
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
Medium Access Control Layer: One ECU 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
Medium Access Control Layer: One ECU Smallest Period into Shortest Subframe First (SSF) Scheduling 2-approximation algorithm
Medium Access Control Layer: One ECU 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?
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 Transmission time= packet length/ rate of UWB for concurrent transmission Energy requirement Delay requirement
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
Medium Access Control Layer: Multiple ECU
Outline Motivation for Intra-Vehicular Wireless Sensor Networks Physical Layer Design Medium Access Control Layer Conclusion Current Projects at WNL
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 , January [pdf | link]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 , January [pdf | link]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 , July [pdf | link]pdflink
Outline 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 Director Sinem Coleri Ergen Ph.D. Students Yalcin Sadi | Seyhan Ucar | Merve Saimler | Elif Dilek Salik | Utku Demir | Ali Vosoughi | Bugra Turan | Melih Karaman M.S. Students Anique Akhtar | Bakhtiyar Farayev Alumni Umit Bas | Nabeel Akhtar | Irem Nizamoglu | Mehmet Kontik
Thank You! Sinem Coleri Ergen: Personal webpage: Wireless Networks Laboratory: