1. Enhanced Spring Clustering in VANETs with Obstruction Considerations
Leandros A. Maglaras (presentation)
Dimitrios Katsaros
University of Thessaly, Greece 1
IEEE VTC, Dresden (Germany), 02-06/June/2013 1

2. Cluster formation in a typical vanet topology
Vanet issues: Safety, comfortable driving, fuel economy
Broadcast storm problem
Scaling down networks Clustering 2

3. Presentation outline Earlier work and background knowledge
Force – directed algorithms
Signal attenuation
Reliable Communication range
Performance evaluation
Conclusions 3

4. Clustering in Vanet's: Earlier work
Earlier work & background
Clustering in Vanet's: Earlier work
Low-id
Mobic
DDVC clustering (DLDC)
Affinity propagation APROVE
Density based clustering
Distributed group mobility adaptive clustering (DGMA)
Open IVC Networks (COIN)
Spring Clustering
In Lowest-ID, each node is assigned a unique ID, and the node with the lowest ID in its two-hop neighborhood
is elected to be the cluster head
MOBIC an aggregate local mobility metric is the basis for cluster formation instead of node ID. The node with
the smallest variance of relative mobility to its neighbors is elected as the cluster head.
the metric used for clustering is strictly based on a metric derived from the relative velocity between nodes obtained from the Doppler shift of control
packets exchanged between nodes during the initial phase of clustering. DDVC dynamically elects clusterheads and adds members to newly formed clusters.
The communicating nodes must know the communication frequency f for which they expect the signal to be received at. The ratio of the received (observed) frequency
of signal packet f0 and the known communication frequency f is used to distinguish between approaching and receding nodes, and to calculate the Doppler Value.
each node in the network computes and transmits the responsibility and availability messages to each of its neighbours. The result is that each node is performing distributive affnity propagation with only the nodes in its one-hop neighbourhood thus creating clusters
Density based clustering is based on how dense the network locally in order to create clusters
“micro” movement is ignored in DGMA and only “significant” location change is considered. Thus, the impact caused by frequent change in direction but for a short duration is alleviated.
COIN where cluster-head election is based on vehicular dynamics and driver intentions; 4

5. Contributions
• Incorporation of vehicles as obstacles in a VANET simulator.
• Reliable Communication Range based on the diffraction caused
by obstructing vehicles.
• Enhanced Spring Clustering method (incorporation of physical characteristics in clusterhead election)
• Evaluation of the performance of Enhanced Spring Clustering under different network characteristics (density, velocity, car dimensions, placement of antenna).

6. Relative force among nodes l,m
The concept of ‘Force directed algorithms’
Relative forces applied to nodes
Forces are assigned as if the edges were springs and the nodes were electrically
charged particles.The entire graph is then simulated as if it were a physical system
Special role of nodes
Relative force among nodes l,m
Distance among nodes 6

7. Important Characteristics of vehicles
Behavior of vehicles
Physical characteristics of vehicles

8. Special role of vehicles Enhanced Spring Clustering
Physical characteristics of vehicles
When a node finds itself to be among the tallest in its one-hop neighborhood then parameter q is used to favor it become a clusterhead
Beacon messages
node Identifier (ID), node location, speed vector, total force , state and timestamp,vehicle height
Initially each node has a static reliable communication range with all neighbors according to f and Ptx.
Different communication ranges has to be assigned to each pair of nodes for every time instance 8

9. Signal Attenuation - OLOS
Single knife-edge model
Epstein-Peterson method
Placement of antennas
Obstructed line of sight
Olos effects:
PHY layer
Link Layer
Network layer
PHY layer effect: RSS is optimistic
Link layer effects:
Overestimation of contention
Overestimation of network reachability
Network layer effects:
Overly optimistic hop count
End-to-end delay incorrectly calculated
Credibility of simulation results
5 dB attenuation and 20% packet loss on average are far from
negligible!
If vehicles are not accounted for, optimistic results are obtained
In reality, routing protocols will behave worse, network reachability will
be reduced, delay will be incorrect. 9

10. Reliable Communication Range
•Vehicles – buldings, modeled as rectangles [M.Boban 2011]
• For every instance and pair of nodes a straight line is drawn from antenna position of each TX vehicle to the antenna position of each RX vehicle
• Los, Olos, NLos
• Power Loss
• RCR 10

11. Evaluation setting (1/3)
Performance evaluation
Evaluation setting (1/3)
Enhanced Spring Clustering
Impact of RCR, competitor: Sp-Cl VECON’12] 11 11

12. Evaluation setting (2/3)
Performance evaluation
Evaluation setting (2/3)
Measured quantities
average number of clusters (number of CHs)
average cluster changes / vehicle
Average cluster lifetime
More dense network  decrease in RCR  often transition events 12 12

13. Evaluation setting (3/3)
Performance evaluation
Evaluation setting (3/3)
All nodes are equipped with GPS receivers &
On Board Units (OBU). 13 13

14. Impact of OLOS in average number of clusters
Vehicles as obstacles have a significant impact on the formation of clusters
Medium contention is overestimated when OLOS is neglected 14 14

15. OLOS influence average cluster changes / vehicle
Performance evaluation 15

16. Average cluster lifetime under OLOS
Performance evaluation
Average cluster lifetime under OLOS 16

17. Number of Trucks (tall vehicles) influence reliable communication range
Performance evaluation 17

18. En-Sp performance with dynamic RCR
Performance evaluation
En-Sp performance with dynamic RCR
Simulation parameters:
Tall Vehicles 15%
mean density
CR [ ] m
Tall vehicles play significant role in En-Sp.
Tall vehicles  bigger RCR 18

19. En-Sp performance with static CR
Performance evaluation
Height of nodes doesn't affect performance of En-Sp when OLOS is neglected
Same patterns for
Mean cluster transitions and
Average number of clusters 19

20. Summary Performance issues in Vanets Reliable communication range
Conclusions
Summary
Performance issues in Vanets
Reliable communication range
Enhanced Spring Clustering:
Favors tall vehicles to become clusterheads (due to the bigger reliable communication range)
Increases cluster lifetime
Special role of vehicles can be used for other characteristics of vehicles to enhance the clustering performance 20 20

21. Thank you! 21