Road-Based Routing in Vehicular Networks PhD Dissertation Defense by Josiane Nzouonta Advisor:...
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Transcript of Road-Based Routing in Vehicular Networks PhD Dissertation Defense by Josiane Nzouonta Advisor:...
Road-Based Routing in Vehicular Networks
PhD Dissertation Defense
by Josiane NzouontaAdvisor: 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
Applications Accident alerts/prevention Dynamic route planning Entertainment
Roadside infrastructure
Internet
CellularCellular
Vehicle-to-vehicle
Roadside infrastructure
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
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?
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
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
SN1 D
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
DDead end road
N1
N2
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
I2I1 I3
I6 I8E
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
I2I1 I3
I6 I8E
carIntersection j
I7
I4 I5
Ij
D
S
AB
C
Source
Destination
N1
Re-broadcast from B
Re-broadcast from N1
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
I2I1 I3
I6 I8E
car
Intersection j
I7
I4 I5
Ij
D
S
A
B
C
Source
Destination
Path in reply packet header
I8
I4
I6
I5
I7
I1
15
RBVT-R Forwarding
Data packet follows path in header
Geographical forwarding is used between intersections
I2I1 I3
I6 I8E
car
Intersection j
I7
I4 I5
Ij
D
S
A
B
C
Source
Destination
Path in data header
I8
I4
I6
I5
I7
I1
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
I2I1 I3
I6 I8E
carIntersection j
I7
I4 I5
Ij
D
S
AB
C
Source
Destination
N1
Re-broadcast from B
Re-broadcast from N1
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
I2I1 I3
I6 I8E
carIntersection j
I7
I4 I5
Ij
A
B
C
CP generator
12
3
4
5
6
7 8
9
nn-1
i Step i
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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
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 11Mbps
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
150 nodes
0
10
20
30
40
50
60
70
80
90
100
0.5 1 1.499 2 3.003 4 4.505 5
Packet sending rate (Pkt/s)
Ave
rag
e d
eliv
ery
rati
o (
%)
AODV
GPSR
RBVT-P
OLSR
GSR
RBVT-R
250 nodes
0
10
20
30
40
50
60
70
80
90
100
0.5 1 1.499 2 3.003 4 4.505 5
Packet sending rate (Pkt/s)
Ave
rag
e d
eliv
ery
rati
o (
%)
AODV
GPSR
RBVT-P
OLSR
GSR
RBVT-R
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)
250 nodes
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0.5 1 1.499 2 3.003 4 4.505 5
Packet sending rate (Pkt/s)
En
d-t
o-e
nd
del
ay (
Sec
on
ds)
AODV
GPSR
RBVT-P
OLSR
GSR
RBVT-R
150 nodes
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0.5 1 1.499 2 3.003 4 4.505 5
Packet sending rate (Pkt/s)
En
d-t
o-e
nd
del
ay (
Sec
on
ds)
AODV
GPSR
RBVT-P
OLSR
GSR
RBVT-R
25
Outline Motivation RBVT routing Forwarding optimizations
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 802.11 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 802.11
ns
n4
n1
n2
n3
n5
n6
DRTS
CTS
(a) RTS Broadcast and Waiting Time Computation
(b) CTS Broadcast
(NULL) (0.115ms)
(0.201ms)(0.0995ms)r
rns
n4
n1
n2
n3
n5
n6
D
ns
n4
n1
n2
n3
n5
n6
DData
(c) Data Frame
r
ACKrns
n4
n1
n2
n3
n5
n6
D
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
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 Increases delivery ratio Decreases end-to-end delay
RBVT-R with source selection using “hello” packets vs. self-election
32
Outline Motivation RBVT routing Forwarding optimizations
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
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)
35
Probabilities of service and failure
Probabilities of service/failure given that packet arrives with system in state k
And for all disciplines
Single Queue Analysis
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
UDP sending rate 5 Packet/seconds
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
5 10 20 30 50 70 90 100
Buffer size
En
d-t
o-e
nd
del
ay (
seco
nd
s)
FIFO-FD
FIFO-TD
LIFO-FD
LIFO-TD
UDP sending rate 20 Packet/seconds
0
2
4
6
8
10
12
14
16
18
20
5 10 20 30 50 70 90 100
Buffer size
En
d-t
o-e
nd
del
ay (
Sec
on
ds)
FIFO-FD
FIFO-TD
LIFO-FD
LIFOTD
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
UDP sending rate 5 Packet/second
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
5 10 20 30 50 70 90 100
Buffer size
Ave
rag
e ji
tter
(S
eco
nd
s)
FIFO-FD
FIFO-TD
LIFO-FD
LIFO-TD
UDP sending rate 10 Packet/second
0
1
2
3
4
5
6
5 10 20 30 50 70 90 100
Buffer size
Ave
rag
e ji
tter
(S
eco
nd
s)
FIFO-FD
FIFO-TD
LIFO-FD
LIFO-TD
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
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
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
(a) At time t
(b) At time t+1
car 2, v2=1car 1, v1=2
car 3, v3=1
car 1, v1=2
car 2, v2=2
car 3, v3=1
gap1 = 4 cells gap2 = 1 cell gap3 ≥ 3 cells
Lc = 7.5m
gap1 = 3 cells gap2 = 1 cell gap3 ≥ 2 cells
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
0 2Time t
1 0Time t-1
0 3Time t
2 0Time t-1
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
48
Transition Matrix Generic transition probability from state of aggregated
Markov chain 2 1Road section 1 0
1 2 3 4 5 6 7 8 9 10Cell 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 S2 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
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 observationwith k cells
Total number of cells = 320 cells
SourceDestination
53
Expected Connectivity Duration
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
50 vehicles 75 vehicles
54
Expected Disconnectivity Duration
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
50 vehicles35 vehicles
55
Probability Connectivity Duration
Longer uninterrupted connectivity less likely Larger k leads to smaller probabilities of connectivity
duration
k = 8 cells k = 10 cells
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
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
58
59
Thank you!
Acknowledgments: This research was supported by the NSF
grants CNS-0520033 and CNS-0834585
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
0
10
20
30
40
50
60
70
80
90
100
1 5 10 15 20
Number of concurrent flows
Ave
rag
e d
eliv
ery
rati
o (
%)
AODV
GPSR
RBVT-P
OLSR
GSR
RBVT-R
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
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
6 flows, 5MB each10 flows, 2MB each
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