1. Road-Based Routing in Vehicular Networks
PhD Dissertation Defense
by Josiane Nzouonta
Advisor: Cristian Borcea

2. Today: Smart Vehicles Geographical Positioning System (GPS)
Digital maps or navigation system
On-Board Diagnostic (OBD) systems
DVD player

3. Tomorrow: Vehicular Networks
Roadside
infrastructure
Internet
Cellular
Vehicle-to-vehicle
Vision vehicular networks
Benefits: saving lives, time;
saving gas and implicitly money
Proactively for
Applications
Accident alerts/prevention
Dynamic route planning
Entertainment
Communications
Cellular network
Vehicle to roadside
Vehicle to vehicle

4. Vehicular Ad Hoc Networks (VANET)
My focus in this research
Benefits
Scalability
Low-cost
High bandwidth
Purpose of enabling communication between cars
Challenges
Security
High mobility

5. VANET Characteristics
High node mobility
Constrained nodes movements
Obstacles-heavy deployment fields, especially in cities
Large network size
Can applications based on multi-hop communications work in such environment?
The answer lies in part with the performance of routing and forwarding protocols in VANET

6. Problem Statement
How to design efficient routing and forwarding protocols in VANET?
Do existing MANET routing protocols work well in VANET?
If not, can we take advantage of VANET characteristics to obtain better performance?
Are current forwarding protocols enough or can they be optimized for VANET characteristics?

7. Contributions Road-Based using Vehicular Traffic (RBVT) routing
Use real-time vehicular traffic and road topology for routing decisions
Geographical forwarding on road segments
VANET distributed next-hop self-election
Eliminate overhead associated with periodic “hello” messages in geographical forwarding
Effect of queuing discipline on VANET applications
LIFO-Frontdrop reduces end-to-end delay compared with FIFO-Taildrop
RBVT path predictions
Analytical models to estimate expected duration of RBVT paths

8. Outline Motivation RBVT routing Forwarding optimizations Conclusions
Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions

9. Node Centric Routing Shortcomings in VANET
Examples of node-centric MANET routing protocols
AODV, DSR, OLSR
Frequent broken paths due to high mobility
Path break does not correspond to loss connectivity
Performance highly dependent on relative speeds of nodes on a path S N1 D
a) At time t
b) At time t+Δt N2

10. Geographical Routing Shortcoming in VANET
Examples of MANET geographical routing protocols
GPSR, GOAFR
Advantage over node-centric
Less overhead, high scalability
Subject to (virtual) dead-end problem S D
Dead end road N1 N2
Uses nodes positions to perform multihop communication
Neighbor closest to destination position selected as next hop
Virtual dead-ends
VANET-specific routing
Perform geographical routing along road streets
Heuristics:
Use daily/hourly traffic flow data to select next road segment
Use roads most frequented by buses
Pros/cons:
Historical data not always a good indication of current road state

11. RBVT Routing Main Ideas
Use road layouts to compute paths based on road intersections
Select only those road segments with network connectivity
Use geographical routing to forward data on road segments
Advantages
Greater path stability
Lesser sensitivity to vehicles movements I2 I1 I3 I6 I8 E
car
Intersection j I7 I4 I5 Ij D S A B C
Source
Destination
Path in header: I8-I5-I4-I7-I6-I1

12. RBVT Protocols RBVT-R: reactive path creation
Up-to-date routing paths between communicating pairs
Path creation cost amortized for large data transfers
Suitable for relatively few concurrent transfers
RBVT-P: proactive path creation
Distribute topology information to all nodes
No upfront cost for given communication pair
Suitable for multiple concurrent transfers

13. RBVT-R Route Discovery
Source broadcasts route discovery (RD) packet
RD packet is rebroadcast using improved flooding
Intersections traversed are stored in RD header S
Source I1 I2 I3 A N1
Re-broadcast
from N1 B
Re-broadcast
from B C I4 I5 I6 E I7 I8 D
car Ij
Intersection j
Destination

14. RBVT-R Route Reply
Destination unicasts route reply (RR) packet back to the source
Route stored in RR header
RR follows route stored in the RD packet S
Source I1 I2 I3 A B C I4 I5
Path in reply
packet header I1 I6 I6 E I7 I8 I7 D I4 I5 I8
car Ij
Destination
Intersection j

15. RBVT-R Forwarding Data packet follows path in header
Geographical forwarding is used between intersections
Path in data
header S
Source I1 I6 I1 I2 I3 I7 I4 A I5 I8 B C I4 I5 I6 E I7 I8 D
car Ij
Destination
Intersection j

16. RBVT-R Route Maintenance
Dynamically update routing path
Add/remove road intersections to follow end points
When path breaks
Route error packet sent to source
Source pauses transmissions
New RD generated after a couple of retries S
Source I1 I2 I3 A N1
Re-broadcast
from N1 B
Re-broadcast
from B C I4 I5 I6 E I7 I8 D
car Ij
Intersection j
Destination

17. RBVT-P Topology Discovery
Unicast connectivity packets (CP) to record connectivity graph
Node independent topology leads to reduced overhead
Lesser flooding than in MANET proactive protocols
Network traversal using modified depth first search
Intersections gradually added to traversal stack
Status of intersections stored in CP
Reachable/unreachable
CP generator n
n-1 I1 I2 I3 A 2 1 B 7 8 C 3 I4 I5 6 5 9 4 I6 E I7 I8
RBVT-P was co-designed in collaboration with Neeraj Rajgure, a master student in the CS dept
multiple nodes generate CPs based on a simple randomized scheme to ensure that connectivity info is
available even in case of network partitions
car i
Step i Ij
Intersection j

18. RBVT-P Route Dissemination & Computation
CP content is disseminated in network at end of traversal
Each node
Updates local connectivity view
Computes shortest path to other road segments
Reachability
Intersection j
I2: I1, Iv2
I4: I7, I5, Iv3
I6: I1, I7
I5: I4, I8, Iv4
I7: I6, I4
I1: I2, I6, Iv1
Iv3
Iv2
RU content I1 I2 I3 I4 I5 I6 I7 I8
Iv4 Ij
Iv1

19. RBVT-P Forwarding and Maintenance
RBVT-P performs loose source routing
Path stored in every data packet header
Intermediate node may update path in data packet header with newer information
In case of broken path, revert to greedy geographical routing
Source appends path in data header

20. RBVT Evaluation
Perform simulations to compare against existing protocols
Comparison protocols:
AODV (MANET reactive)
GPSR (MANET geographical)
OLSR (MANET proactive)
GSR (VANET)
Metrics
Average delivery ratio
Average end-to-end delay
Routing overhead

21. Simulation Setup Network Simulator NS-2
Map: 1500m x 1500m from Los Angeles, CA
Digital map from US Tiger/Line database
SUMO mobility generator
Obstacles modeled using random selection of signal attenuation
Range [0dB, 16dB]
Shadowing propagation model

22. Simulation Setup (cont’d)
Data rate Mbps

23. Average Delivery Ratio
RBVT-R has the best delivery ratio performance
RBVT-P improves in medium/dense networks
The denser the network, the better the performance for road-based protocols in these simulations

24. Average End-to-end Delay
RBVT-P performs best, consistently below 1sec in the simulations
RBVT-R delay decreases as the density increases (less broken paths)

25. Outline Motivation RBVT routing Forwarding optimizations Conclusions
Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions

26. The Problem with “hello” Packets
“hello” packets used to advertise node positions in geographical forwarding
“hello” packets need to be generated frequently in VANET
High mobility leads to stalled neighbor node positions
Presence of obstacles leads to incorrect neighbor presence assumptions
Problems in high density VANET
Increased overhead
Decreased delivery ratio

27. Distributed Next-hop Self-election
Slight modification of IEEE RTS/CTS
Backward compatible
RTS specifies sender and final target positions
Waiting time is computed by each receiving node using prioritization function
Next-hop with shortest waiting time sends CTS first
Transmission resumes as in standard IEEE ns n4 n1 n2 n3 n5 n6 D
RTS
CTS
(a) RTS Broadcast and Waiting Time Computation
(b) CTS Broadcast
(NULL)
(0.115ms)
(0.201ms)
(0.0995ms) r
Data
(c) Data Frame
ACK

28. Waiting function Function takes 3 parameters
Distance sender to next-hop (dSNi)
Distance next-hop to destination (di)
Received power level at next-hop (pi)
Weight parameters α set a-priori
Value of α determines weight of corresponding parameter
Adds signal quality in determination of next next-hop
Leveraged previous work on MANET done at EE, NJIT to VANET environment
Tmax is maximum waiting time for a next hop; value is transmission time of CTS + propagation delay

29. Waiting Function Results
Using multi-criteria function to select next hops leads to significantly lower packet loss and overhead in VANET

30. Evaluation of Self-election Performance
Goal: Verify and quantify if/how self-election improves performance in high congestion scenarios
Metrics
Average delivery ratio
Average end-to-end delay
Routing overhead
Used own mobility generator based on Gipps car-following and lane-changing models
Simulations parameters same as used for RBVT evaluation
Map used in the no obstacle simulations

31. Delivery Ratio & Delay Distributed next-hop self election
RBVT-R with source selection using “hello” packets vs. self-election
Average Delivery Ratio and Average Delay comparison between two types of geographical forwarding: source selection using “hello” packets and receiver self-election using the RTS/CTS-based mechanism under the scenario without obstacles. The routing protocol is RBVT-R, and the network size is 250 nodes.
Distributed next-hop self election
Increases delivery ratio
Decreases end-to-end delay

32. Outline Motivation RBVT routing Forwarding optimizations Conclusions
Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions

33. Effect of Current Queue Discipline on Delay
Current queuing discipline: FIFO with Taildrop (TD)
Wireless contention increase time packets spend in queue
Low percentage problem frames have significant impact on average delay
Average end-to-end delay as function of percentages of frames experiencing transmission delays greater than 2 seconds.

34. Improving Delay through Queuing Discipline
Why improve?
Delay sensitive but loss tolerant applications important in VANET/MANET
Applications: video streaming near an accident; search and rescue operations
Analyze four queuing disciplines
FIFO-Taildrop (FIFO-TD)
FIFO-Frontdrop (FIFO-FD)
LIFO-Taildrop (LIFO-TD)
LIFO-Frontdrop (LIFO-FD)
End-to-end solutions for decreasing delay have issues in ad hoc networks in general, and mobile ad hoc in particular

35. Single Queue Analysis Probabilities of service and failure
Probabilities of service/failure given that packet arrives with system in state k
And for all disciplines

36. Single Queue Analytical Results
Low traffic rate ρ = 0.75
Expected waiting times are similar for all 4 disciplines
Variance of waiting times higher for LIFO disciplines

37. Single Queue Analytical Results (cont’d)
High traffic rate ρ = 1.5
LIFO-FD presents low expected waiting times of packets served
Variance of waiting times of served packets is also lowest for LIFO-FD and highest for LIFO-TD

38. Network Evaluation Evaluation
Assess performance in ad hoc networks, static and mobile
Metrics: average end-to-end delay, end-to-end jitter, throughput
Static topology

39. Average End-to-end Delay
Static ad hoc network scenario
Comparable performance for low traffic
LIFO disciplines have best and worst performance in high traffic

40. Average Jitter Static ad hoc network scenario
Low traffic: less than 40ms jitter for all 4
FIFO has lowest jitter
High traffic: LIFO-FD maintains less than 1sec jitter with buffer size increase

41. Delay & Throughput in VANET
No obstacles map with 250 nodes, RBVT-R
LIFO-FD leads to lower delay (as much as 45%)
Throughput not aversely affected by LIFO-FD

42. Outline Motivation RBVT routing Forwarding optimizations Conclusions
Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions

43. Characterization of RBVT Paths
Why?
How long is the current route going to last?
Does it make sense to start a route discovery?
Can a 100Mb file be successfully transferred using the current route?
Is it possible to estimate the duration of a path disconnection?
How to estimate path characteristics (connectivity duration/probability)?
Simulations are specific to geographical area
Analytical models based on validated traffic models are preferred

44. Cellular Automata (CA) Traffic Model
(a) At time t
(b) At time t+1
car 2, v2=1
car 1, v1=2
car 3, v3=1
car 2, v2=2
gap1 = 4 cells
gap2 = 1 cell
gap3 ≥ 3 cells
Lc = 7.5m
gap1 = 3 cells
gap3 ≥ 2 cells
Update rules at vehicle i
Acceleration: if vi < vmax, vi = vi + 1
Slow down (if needed): if vi > gapi, vi= gapi
Randomization: vi = vi – 1 with probability p
Move car: xi = xi + vi
Microscopic vehicular traffic generator
Validated in literature
Discrete-space and discrete-time model
Divide road in cells of length Lc
Lc sometimes 7.5m
Car length ~ 5m, safety distance 2.5m
Discrete speed set {0,1, 2, …, vmax}

45. DTMC-CA Model Discrete-time and discrete space model
Uses CA microscopic traffic model for vehicle movements
Portion of road between source and destination divided in k cells of length Lc
Markov chain M = (S, P, s0)
State space S = {s = (c1, c2, …, ck), ci є V, i=(1,…, k)}
Cell values V = {0, 1, 2, …, vmax, ∞}
Interested only in stationary measures

46. State Reduction: Invalid States
As described, |S| = |V|k
Many potential states are transient states
Violate updating rules
Not reachable from any other state in the system
Algorithm to output non-transient states
Directly obtaining non-transient states needed 2
Time t 1
Time t-1 3

47. State Reduction: Lumpability
Markov chain is lumpable w.r.t
with
Example
Additional 80% decrease in size of space set observed when lumping the Markov chain
CA Rules:
Deterministic (rule 1 and 2)
Stochastic (rule 3)
Deterministic (rule 4)
If two states x, y equal after rules 1 and 2, identical rows in transition matrix
States can be lumped because they are “equivalent”
Lumping preserves the stationary and transient properties of interest

48. Transition Matrix
Generic transition probability from state of aggregated Markov chain 2 1
Road section 3 4 5 6 7 8 9 10
Cell number
For cell 2:
2 = 3 2 = 0
For cell 3:
3 = 5 3 = 0
Borders:
0 = 3 10 = 9
For cell 6:
6 = 7 6 = 5

49. Probabilistic Measures
Stationary distribution π
Connected states S1
Disconnected states S S1US2 =S S1∩S2 =Ø
Expected duration of connectivity

50. Probabilistic Measures (cont’d)
Expected duration of disconnection
Probability of connection duration

51. Extending Basic Model Bidirectional Traffic
Each lane is divided in k cells, juxtaposed, independent
Markov chain
Moving endpoints and lane change
Speed relative to source speed
Possible cell values
Moving endpoints: Value of k changes during communication, with maximum distance considered
Lane change rules added to basic CA model
Lc = 7.5m

52. Evaluation Method and Setup
Simulation to validate model
Simulate CA freeway model and SUMO
Large ring layout
Connectivity of shaded area is analyzed
Complete ring affects shaded area
DTMC-CA considers shaded area only
Area of observation
with k cells
Total number of cells = 320 cells
Source
Destination

53. Expected Connectivity Duration
50 vehicles
75 vehicles
DTMC-CA match well with simulation results
Increase in transmission range leads to increase in connectivity duration (as expected)
Stochastic nature of CA model: 11 cells out of 12 cells for connectivity leads to average of < 80 sec with 0.23 density

54. Expected Disconnectivity Duration
35 vehicles
50 vehicles
DTMC-CA match well with simulation results
Increasing connectivity range decreases expected disconnectivity duration
Impact of density on expected disconnectivity duration reduced compared to impact on expected connectivity duration

55. Probability Connectivity Duration
k = 8 cells
k = 10 cells
Longer uninterrupted connectivity less likely
Larger k leads to smaller probabilities of connectivity duration

56. Incorporating Path Estimates in RBVT
Road-side sensors or historical data
Road segment densities and entry speeds probabilities
Improving route selection
Duplicate routes received at the destination
Enhancing route maintenance of RBVT-R
How long should the source wait when a route breaks
Determining RBVT-P CP generation interval
Period between CP generation based on connectivity duration
Reducing overhead network traffic
Likelihood of success of 100MB transmission (delay or divide in smaller chunks)

57. Conclusion Existing MANET routing protocols do not work well in VANET
Better routing and forwarding possible by integrating VANET characteristics such as road layouts and node mobility
Contributions:
RBVT routing: Stable traffic-aware road-based paths
Distributed VANET next-hop self-election: Significant overhead reduction in geographical forwarding
Impact of queuing discipline on latency: LIFO-TD improves performance for delay sensitive applications
RBVT paths predictions: Analytically compute path estimates, which can be used to improve data transfer performance

58. Future Work Adaptive queuing mechanism
Route lifetime prediction independent of the vehicular traffic model used
Apply knowledge of expected route duration in RBVT
Security issues in RBVT

59. Thank you! Acknowledgments: This research was supported by the NSF
grants CNS and CNS

60. Impact of Number of Flows
The data rate is fixed at 4 packets/second and the network size is 250 nodes
Delivery ratio is stable in the simulations performed

61. Node Selection Using Waiting Function

62. Overhead Routing packets exchanged for each received data packet
Removing “hello” packets essentially eliminates most overhead

63. Single Queue Analysis
Interested in time elapsed from packet arrival to service
Markov chain model X(t) on the state space {−1, 0, · · · ,N}
If packet arrives when state (k), k < N
State changes to (k + 1)
New packet goes in position k+1
If a packet arrives while the system is in state (N)
System remains in this state
Under Taildrop, the arriving packet is dropped and all the other packets remain in their old positions
Under Frontdrop, the packet in position 1 is dropped, other packets move up one position (j -> j −1), arriving packet goes to position N

64. TCP Throughput and Fairness
10 flows, 2MB each
6 flows, 5MB each
Static ad hoc network scenario
Transfers complete at comparable times for LIFO-FD and FIFO-TD
LIFO-FD does not disadvantage any specific flow in those simulations

65. DTMC-CA: Effect of Removing Invalid States

66. SUMO Results

67. Different Traffic Models - Different Results

68. Connectivity Window Model
Provide analytical model independent of the traffic model
Uses the concept of connectivity window
Count vehicles in each window