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Toward Strongly Connected
Clustering Structure in
Vehicular Ad hoc Networks
Zaydoun Y. Rawshdeh, Syed Masud Mahmud
Electrical and Computer Engineering Department
Wayne State University, Detroit, MI, USA
Presented by:
Sanaz Khakpour
Master of Computer Science Student
5/24/2017
1
Objectives
• Use clustering techniques in order to decrease the dynamic
topology of VANETs as much as possible.
• Cluster the nodes with most similar mobility pattern using
direction, location, and speed.
• Partitioning the network to minimum number of clusters.
• Using a multi-metric election technique to choose the best
cluster head.
• Increase cluster stability considering changes in the network
topology which have direct effect on stability.
5/24/2017
2
Identifying Candidate Cluster Member
• Degree of speed difference is a key feature to build stable
clusters.
• The position information (sent in periodic messages) of vehicles
is being used to build neighbourhood relationship (rneighbour).
• Nodal degree is the total number of r-neighbours of a node.
• Neighbour nodes moving in the same direction are supposed
to be candidate cluster members (CCM).
• Neighbours are classified to SN (r-distance, same direction,
close speed) and UN.
• All SN which do not belong to other clusters are CCM.
5/24/2017
3
Identifying Candidate Cluster Member
•
The speed of vehicles is assumed to be random variable (Normal
distribution 𝜇 and variance σ2 ). Probability density function (pdf):
𝑓𝑣 𝑣 =
•
1
𝜎 2𝜋
The speed difference between vehicles (∆V) follows normal
distribution as follow:
𝑓∆𝑣 ∆𝑣 =
•
𝑒
−(𝑣−𝜇)2
2𝜎2
1
𝜎∆𝑣 2𝜋
𝑒
−(∆𝑣−𝜇∆𝑣)2
2𝜎2
The probability that speed difference between two vehicles is in
the threshold (∆𝑉𝑡ℎ ):
𝑓∆𝑣 (-∆𝑣𝑡ℎ < ∆𝑣 < ∆𝑣𝑡ℎ )=
1
𝜎∆𝑣 2𝜋
∆𝑣𝑡ℎ −(∆𝑣−𝜇∆𝑣)
2𝜎2
𝑒
−∆𝑣𝑡ℎ
2
• To avoid high variation in the number of SN, threshold is
assumed to be a function of deviation, such as ∆𝑣𝑡ℎ = 𝛽𝜎.
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4
Protocol Structure
• Control channel: is being used to send periodic messages and
gain information about neighbours. (Transmission range R).
• Service channel: is used to create cluster and send intracluster messages and cluster management. (Transmission
range r < R).
• Because R=4r, vehicles can obtain complete information
about their neighbours (can be beyond cluster boundaries)
• Any vehicle can understand if its speed is less than all its nonclustered neighbours in R distance range. That vehicle is
supposed to start cluster formation.
5/24/2017
5
Cluster Radius
• DSRC (Dedicated Short-Range Communications) is a multichannel interface with various transmission ranges.
• Neighbourhood definition depends on the used channels.
• Vehicles u and v are neighbours in control channel’s
perspective. But u and w are neighbours from the perspective
of both channels.
5/24/2017
6
Cluster Formation
• Each vehicle keeps a list of 2-r neighbours at time t (Γ(t)).
• Γ(t) is divided into Γ(t)_G and Γ(t)_L which are vehicles with
greater and lower speeds respectively.
• The vehicle with lowest speed among its neighbours starts
cluster formation. It is called cluster originating vehicle (COV).
• COV sends its ID to all Γ(t)_G as temporary cluster ID. All non
clustered members set the cluster ID.
• Vehicles calculate their suitability to be a CH and announce it
if their value is higher than previously received values.
Suitability value is compared with only r-neighbour members
of Γ(t)_G of COV.
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7
Cluster Rules
• Vehicles that can’t connect to the cluster stay non-clustered
(default state) and start cluster formation process again.
• A node joins cluster if its relative speed to CH is in the
threshold.
• The members should stay in r-distance range. Otherwise, they
will lose their membership.
• Two clusters can merge if:
 The distance between CHs are less than r.
 The difference between average speed and both CH’s
speed is in a threshold.
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8
Cluster Head Selection
• Suitability function is used to verify eligibility of a node to be
CH.
•
nodes with closer distance to their neighbours and closer
relative speed to average speed of neighbours are supposed
to have higher connectivity degree.
• CCM of COV is Γ(t)_G including {𝑛1 , 𝑛2 , … , 𝑛𝑘 }, connectivity
degree (d) of node 𝑛i is calculated as follow:
𝑘
𝑑𝑖 =
{𝑑𝑖𝑠(𝑛i
𝑝𝑜𝑠
, 𝑛j 𝑝𝑜𝑠 ) < 𝑟}
𝑗=1,𝑗≠𝑖
𝑛i
𝑝𝑜𝑠
, 𝑛j 𝑝𝑜𝑠 are current position of nodes i and j
𝑑𝑖𝑠(𝑛i
𝑝𝑜𝑠
, 𝑛j 𝑝𝑜𝑠 ) is distance between nodes i and j
5/24/2017
9
Cluster Head Selection
• Then normalized mean distance of a node 𝑛1 to its
d1 neighbours is (𝜇𝑝 is mean position and 𝜎𝑝 is standard
deviation):
𝑃𝑛𝑜𝑟𝑚 =
𝑛i
𝑝𝑜𝑠
−𝜇𝑝
𝜎𝑝
• Value of 𝑃𝑛𝑜𝑟𝑚 indicates the distance of node from centre of
its neighbours.
• The suitability of node to be CH is expressed as follow:
S=d*𝑒 −𝛼𝑤
w= 𝑃𝑛𝑜𝑟𝑚 + 𝑉𝑛𝑜𝑟𝑚 and 0< 𝛼 ≤ 1
5/24/2017
10
Simulation Results
• Vehicles enter a multi-lane highway and move for 10 km.
• Vehicles can only change lane if there is no obstacle,
otherwise they will slow down and stay behind the slower
vehicle in front of them.
• Cluster radius (r) is 200m and control channel range (R=4r) is
800 m.
𝑙𝑖𝑓𝑒
• Cluster lifetime (Ci
)is being evaluated which is directly
dependant on CH lifetime:
Ci,mean 𝑙𝑖𝑓𝑒 =
1
𝐿
𝑙𝑖𝑓𝑒
𝐿
C
𝑖=1 i
,
L: total number of clusters
Ci,mean 𝑙𝑖𝑓𝑒 : mean cluster lifetime
5/24/2017
11
Simulation Results
5/24/2017
12
Questions
• What parameters are used for calculating mobility
metric?
• What are Γ(t)_G and Γ(t)_L in cluster formation
process?
• What is he paper’s most important objective?
5/24/2017
13