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Dependability Assessment of Two Network Supported Automotive Applications Ossama Hamouda, Mohamed Kaniche, Karama Kanoun DENETS ghly DE pendable IP-based NET works and S ervices NODES Winter School and Seminar Dependability and Computer

  1. Dependability Assessment of Two Network Supported Automotive Applications Ossama Hamouda, Mohamed Kaâniche, Karama Kanoun DENETS ghly DE pendable IP-based NET works and S ervices NODES Winter School and Seminar “Dependability and Computer Engineering: Concepts for Software Intensive Systems”; IGI Global book 1-3 February 2012, Turku, Finland 1

  2. Context: communicating automotive systems Servers Servers … Internet UMTS WLAN WLAN GPRS Wireless and mobile technologies for automotive applications o n Car-to-car communication with server-based infrastructure Increase traffic capacity and safety o Dependability challenges: design and assessment o 2

  3. Applications o Automated highway systems o Virtual black box (VBB) (AHS) n Platooning Data storage: VBB Internet Original data Contributors Data owner Data replication and temporary backup on neighboring cars o Aim n Quantify and analyze dependability n Support Design tradeoffs 3

  4. Challenges o Dynamicity/mobility n changing topologies and ☞ Compositional connectivity characteristics Model-based approach integrating o Complexity dependability & mobility related n large number of components characteristics and interactions ■ Stochastic Activity n multiple failure modes and Networks (SAN) recovery scenarios ■ Möbius tool o Performance/dependability tradeoffs 4

  5. Automated Highway Systems (AHS) o Objective n Improve the flow and capacity of the traffic n Enhance safety by reducing accidents Exit Inter-platoon Intra-platoon ∆ y = 30 to 60 m ∆ x = 1 to 5 m Traffic Flow 5 5

  6. Automated Highway Systems (AHS) o Objective n Improve the flow and capacity of the traffic n Enhance safety by reducing accidents Traffic Flow n Intra-platoon coordination Fixed ¡Infrastructure Exit • Centralized • Decentralized n Inter-platoon coordination • Centralized • Decentralized 6 6

  7. Aim o Quantify safety n Taking into account different types of failure modes affecting the vehicles or their communication, and the associated recovery maneuvers o Compare different coordination strategies 7 7

  8. Failure modes and maneuvers • PATH project, Berkeley University, USA 8 8

  9. Failure modes and maneuvers FM3 KO TIE-N KO TIE FM2 FM1 KO GS KO CS KO AS FM5 KO TIE-E FM6 v_KO safe state FM4 (v_OK) 9

  10. Mutiple Failures: Catastrophic situation catastrophic Combination of failure modes situation n Measure: ¡Unsafety ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ = ¡Probability ¡{ ¡ system ¡state ¡at ¡instant ¡(t) ¡ ∈ ¡ST 1 , ¡ST 2 ¡, ¡or ¡ST 3 ¡ } S ( t ) 10

  11. Modeling o Case study n Highway with 2 lanes n Vehicles may change from one lane to another n Maximum number of vehicles per platoon: N o SAN model Configuration ..... One_vehicle Severity Dynamicity One_vehicle One_vehicle 11

  12. Modeling o Case study n Highway with 2 lanes n Vehicles may change from one lane to another n Maximum number of vehicles per platoon: N o SAN model Configuration ..... One_vehicle Severity Dynamicity One_vehicle One_vehicle behavior of a vehicle as resulting from its failure modes and the maneuvers 12

  13. Modeling o Case study n Highway with 2 lanes n Vehicles may change from one lane to another n Maximum number of vehicles per platoon: N o SAN model Configuration ..... One_vehicle Severity Dynamicity One_vehicle One_vehicle Initializes the other submodels and synchronizes their evolution according to the whole system evolution 13

  14. Modeling o Case study n Highway with 2 lanes n Vehicles may change from one lane to another n Maximum number of vehicles per platoon: N o SAN model Configuration ..... One_vehicle Severity Dynamicity One_vehicle One_vehicle models the dynamics of the system in the absence of failures, resulting from join and leave events that correspond to vehicles entering or getting out of the highway 14

  15. Modeling o Case study n Highway with 2 lanes n Vehicles may change from one lane to another n Maximum number of vehicles per platoon: N o SAN model Configuration ..... One_vehicle Severity Dynamicity One_vehicle One_vehicle Describes the impact of multiple failures affecting several vehicles 15

  16. Modeling o Case study n Highway with 2 lanes n Vehicles may change from one lane to another n Maximum number of vehicles per platoon: N o SAN composed model 16

  17. One-­‑vehicle ¡S ub ¡ M odel ¡( 1-­‑AHS ) Configuration ..... One_vehicle Dynamicit Severity y One_vehicle One_vehicle back_to cc1 f1 SM1 OG1 IG1 AS OUT fm1 L1 cc2 f2 v_KO SM2 OG2 IG2 GS fm2 L2 cc3 f3 SM3 OG3 IG3 CS get_out IN fm3 L3 cc4 f4 SM4 OG4 IG4 TIE_E v_OK fm4 L4 cc5 f5 SM5 OG5 IG5 TIE fm5 L5 cc6 f6 SM6 OG6 IG6 TIE_N fm6 L6 17

  18. Number of Vehicles Impact N = number of vehicles • Centralized intra-platoon • Decentralized inter-platoon • Exponential distributions • L 1 = λ • L 2 = 2 λ • L 3 = 2 λ • L 4 = 2 λ • L 5 = 3 λ • L 6 = 4 λ λ = 10 -5 /hr , join rate = 12/hr, and leave rate = 4/hr where, λ is the smallest failure rate 18 18

  19. Coordination Strategy N = number of vehicles 19 19

  20. Automated Highway System: Summary n The analysis allowed us to quantify safety and perform a comparative analysis Ø Trip duration Ø Traffic dynamics Ø Platoon size Set by the Ø Coordination strategy designer 20 20

  21. Virtual Black Box Application (VBB) o Objective n Collect relevant information related to a vehicle and its environment, in a manner similar to the black box of an aircraft Replay historical data in the event of an accident § n Software-based data storage on the fixed infrastructure n Need to protect data against accidental and malicious threats  use data replication o Dependability attributes n Data availability n Data integrity n Data confidentiality 21

  22. Virtual Black Box Application (VBB) o Objective n Collect relevant information related to a vehicle and its environment, in a manner similar to the black box of an aircraft Replay historical data in the event of an accident § n Software-based data storage on the fixed infrastructure n Need to protect data against accidental and malicious threats  use data replication o Dependability attributes n Data availability n Data integrity n Data confidentiality 22

  23. Scenario Data Records continuously collected t1 t2 … tn o Time and temporary stored on the vehicle … R1 R2 Rn VBB resident on the infrastructure o replication To prevent data loss: o R1 R1 R1 R1 R2 Rn n Data records are replicated and backed up on encountered cars (Participants) Backup on encountered cars n Data stored on infrustructure when access available to Vehicle/Participants Internet VBB When an accident occurs, Original data the last z records gathered are sufficient to analyze the Vehicle Participants accident (or at least r among these z ) Data replication and temporary backup on encountered cars 23

  24. Data Records Replication o Replication strategies n Replication by duplication Create full copies of the data record § n Replication by fragmentation: Erasure codes Suitable to ensure data availability and confidentiality § o Erasure code (n, k) n Generates n fragments of the data record that are disseminated to encountered cars. n k fragments are sufficient to restore the original record n (n-k) fragments loss can be tolerated (besides original record) n n = k =1: replication by duplication n k ää  confidentiality ää 24

  25. Dependability Modeling o VBB unavailability assessment o Sensitivity analyses n Replication strategy: n, k n Number of records to analyze an accident: z , r n Other parameters Rate of data loss (Vehicle /Participants): failure rate λ § Car-to-Car encounter rate : α § Car-to-Infrastructure connection rate: β § o Two step approach n Connectivity dynamics analysis C2C and C2I encounter distributions and connection rates § n Availability modeling based on stochastic models using the results of the connectivity analyses as an input 25

  26. Estimation of connectivity dynamics: α , β o Techniques n Analytical proofs n Simulation n Processing of publicly available mobility traces CRAWDAD: http://crawdad.cs.dartmouth.edu § Multi-agent Traffic simulator developed by ETH Zürich § http://www.lst.inf.ethz.ch/research/ad-hoc/car-traces o Conclusions n C2C encounter times Distribution Freeways: Exponential § Urban traffic: Pareto § n C2I encounter times Distribution Exponential § 26

  27. Simulation of a freeway scenario (x 1 ,y 1 ) (x 6 ,y 6 ) W R R AP f(v) (x 2 ,y 2 ) (x 3 ,y 3 ) (x 5 ,y 5 ) f(v) (x 4 ,y 4 ) L § Cars move independently according to speed distribution f(v) • opposite directions on upper and lower half § Uniform Initial placement of cars ( ρ : car density) § Fixed communication radius for the cars: R 27

  28. Example of results: freeway mobility scenarios C2C encounter times C2I encounter times β = 0.011 meet / sec ≈ 40 meet / hr α = 0.31 meet / sec ≈ 1116 meet / hr o Exponential distribution well suited to describe C2C and C2I encounter times 28

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