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CHAPTER 2
PROPOSED CLUSTERING METHOD
2.1 INTRODUCTION
Vehicles are grouped based on the location of clusterhead. Road
side units located at certain predefined places like junctions, traffic signals,
hospitals, restaurants, congested places, shopping malls, city exit points and
toll gates act as static clusterheads. Vehicles that are within the range of static
clusterhead become its members and information is shared between them in a
full duplex manner. All static clusterheads are attached to the central base
station for regulating traffic and instructing them to take decision about path
for vehicles during peak hours. During high mobility conditions, the selected
optimal or shortest path for data propagation might not lead to successful
communication (Rezaei et al 2009). The proposed hierarchical clustering will
address this problem of link failure and dynamic mobility to a great extent.
2.2 CLUSTERHEAD SELECTION METHOD
The propagation of vital data in packets from a source node to
destination node without any loss is important in determining the efficiency of
the overall composition of our proposed system. The location of clusterhead
decides the formation of cluster, based on its transmission range respective to
the other cluster members. Two types of clusterheads are incorporated for
data dissemination, static and dynamic clusterheads. Predefined road side
units act as static clusterheads; they can be located at road junctions, traffic
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signals, hospitals, restaurants, congested places, shopping malls, city exit
points and toll gates. Vehicles are represented as cluster members when they
are within the range of static clusterhead and information is shared between
them in a full duplex manner. The selection of dynamic clusterhead is based
on the vehicles, which travel around the city or the vehicle, which travel over
longer distance like buses, having predefined path and time chart to handle
the high mobility situations. In order to maximize the efficiency of data
dissemination, both the static and dynamic clusterheads are used as part of our
hierarchical clustering method. There can be one or many number of static or
dynamic clusterheads forming a part of global clustering phenomenon and
thereby improvising the performance of data dissemination, reducing the loss
of data and maximizing the regional coverage.
During high mobility situations the number of vehicles
participating in a VANET will be less and thereby making timely information
dissemination very difficult. Static clusterheads are able to receive
observations and aggregates from passing-by vehicles. They also send
beacons and thereby hand over their knowledge to other vehicles. The benefit
of static clusterheads, however, can also be achieved by connecting them via
dynamic clusterhead to form a backbone network irrespective of the
expanding network, allowing them to exchange information through a large
covered area. This can bring up-to-date knowledge to distant network regions
in very short time. A very limited number of static clusterheads are sufficient
for substantial benefits and when dynamic clusterheads are collaborating to a
parent static cluster head, the number of vehicle participation is improved
without much data losses.
2.3 CLUSTERING METHOD
Clustering in VANET provides a framework for management and
reduces the overhead of route attainment. Many clustering techniques had
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been proposed in the literature, but only few of them had considered the status
of network from the aspect of stability. However, the nature of dynamic
changing topology in VANETs introduces difficulties in end-to-end route
finding. Hierarchical clustering is a technique that can dynamically change
the state of clustering in VANET according to different mobility conditions.
More specifically, only stable clusters are formed around static and dynamic
clusterheads. When a new cluster is formed, the member nodes have to inform
the dynamic cluster head of their 2-hop neighbor so that the cluster head can
maintain the topological information of the cluster. Since a cluster is a
proactive domain, the cluster head is informed of the topological changes in
the cluster. Two clusters that collide have to compete according to their
number of stops and direction towards nearby static CH becomes a new CH.
The losing one must dismiss the cluster.
Vehicles are grouped based on the proposed hierarchical clustering
three tier architecture, where all vehicles are at level-0. The chosen dynamic
clusterheads (buses) are at level-1. Static clusterheads that are located at the
predefined places are at level-2. This clustering procedure is performed
recursively until the desired numbers of clusters have been constructed and
each node becomes a member of any one of the clusters. All the updated
level-0 or level-1 vehicle information is propagated into the nearby static
clusterhead whenever they cross. During the cluster formation, all the nodes
that are within the range of static clusterhead will join the nearest cluster upon
receiving a hello message.
2.4 STATIC AND DYNAMIC CLUSTERING
As per the flood-based algorithm, initial cluster construction starts
when a new node enters into the range of static cluster or if dynamic
clusterhead starts to propagate the data. Hello messages are received from
neighbour nodes within the transmission range of clusterhead. The source
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node calculates the neighbour node distance and registers the immediate
neighbour nodes along with the 2-hop neighbour information. Instead of
flooding the data to all neighbours, source would flood the data to the selected
nodes based on the direction towards the destination. This avoids cluster
congestion to a great extent. When static clusterhead, dynamic clusterhead
and cluster members are considered top most priority is given to static
clusterhead; routing and data propagation are led by the static clusterhead. In
the absence of static and dynamic clusterheads the node that has more number
of connectivity and majority direction of travel than other nodes, becomes the
clusterhead.
If the new node does not become a member of any cluster or if it is
not within the reachable area of the clusterhead, it will form the temporary
virtual cluster and this first non-cluster node will become the clusterhead for
that region. Further, all non-cluster nodes become the members of that virtual
cluster. Normally the dynamic clusterheads are designed to propagate data
and record the topology variation at different periods of time. After the cluster
construction is initiated, the topology of network will vary, as nodes are
moving with various speeds. Continuous mobility of nodes followed by its
topology changes leads to reclustering. Among the existing mobility models,
random waypoint and RPGM models (Karnadi et al 2007) are common in
urban scenario. The more the dynamicity of the node the more the topology
and its descendent links are disturbed. The nodes, which move very
frequently, are never used in data propagation. Hence frequent mobility will
lead to unbalanced overload conditions and overhead in sharing information
of topology changes. Clusterhead should update appropriate table information
frequently.
For fast-moving vehicles, the location updates in neighbours might
become obsolete by the time they reach the correspondent node. To get the
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exact position information of a vehicle, large routing overhead is incured.
A cooperative three-tier framework based routing in such a dynamic
environment that proves to be very efficient is proposed.
2.5 HIERARCHICAL CLUSTERING
The hierarchical clustering combines the features of static and
dynamic clustering together. Static clusters are formed around the static
clusterheads located at the road signals, street corners and congested places.
Howe ver, buses are chosen as dynamic clusterheads for having
predefined path and time chart to handle the high mobility situations.
Hierarchical clustering creates a layered environment that poses some of the
main challenges in adhoc networks. Top layer consists of static clusterhead,
middle layer consists of dynamic clusterhead and bottom layer consists of
ordinary vehicles. Because of highly dynamic vehicles, network topology also
changes. This in turn affects the performance of the network and also invokes
protocol mechanisms to react to such situations. Mobility awareness deals
with sudden changes in topology by responding against malfunctions in
routing (Basagni et al 1999). Some of mobility metrics are considered for
cluster construction in order to form a stable cluster structure thereby
decreasing its influence on cluster topology. Vehicle mobility behaviour
determines the architecture of the cluster (McDonald and Znati 1999).
Vehicles are grouped in two different ways; group vehicles which are in the
communication ranges of dynamic sources or group vehicles, which are in the
ranges of static sources mounted at traffic signals and road junctions. By
doing so, the reaffiliation and reclustering rate can be naturally decreased.
Hierarchical clustering architecture as shown in Figure 2.1 is a
three-tier architecture where all vehicles are at level-0. Those vehicles that are
within the transmission range of dynamic clusterhead become members of
that cluster. The chosen dynamic clusterheads (buses) are at level-1. Static
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clusterheads that are located at the predefined places are at level-2. Based on
the location of level-0 cluster members, static clusterhead is chosen. This
clustering procedure is performed recursively until the desired numbers of
cluster have been constructed and all the nodes become the member of any
one cluster. The formation of three-tier cluster architecture, involves the
recursive operation of clusterhead selection and topology updates. All the
updated level-0 or level-1 vehicle information is propagated into the nearby
static clusterhead whenever they cross.
Figure 2.1 Hierarchical clustering architecture
2.6 PROPOSED HIERARCHICAL CLUSTERING ALGORITHM
Dynamic clustering attempts to partition the number of nodes into
multi-hop clusters based on the following parameters (VID,LID,s,VLT)
defined in CCA Algorithm. The (VID,LID,s,VLT) criteria indicate that every
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vehicle node in a cluster has it own unique ID (VID) and Location_ID (LID)
representing the road in a particular area of the city it belongs. The symbol ‘s’
indicates the speed of the vehicle and VLT indicates the vehicle’s lifetime in a
particular cluster, regardless of the hop distance between them. The purpose is
to support robust and efficient routing, and adaptively adjust its dominant
routing scheme depending on the manner of network mobility.
As a dynamic clustering scheme, the existing parameterized
clustering scheme requires no periodic reclustering. As soon as a vehicle
enters the clustering zone its unique VID is registered into the clusterhead and
becomes a member of that cluster. Any unclustered vehicle joins a cluster by
sending out req message. Mobility also affects the size of the cluster. Low
mobility increases the size of the cluster compared to high mobility, where
increase in the number of clusters is observed. A vehicle can join a cluster if it
has a valid VID and its speed is also an important criterion. If any new vehicle
other than ambulance or rescue vehicle enters the cluster with the speed more
than an average speed it is not necessary to update it everywhere. If a vehicle
does not receive a response message after a certain period of time, it will
create a new cluster and it will become the head for this cluster.
The proposed hierarchical clustering algorithm is location based,
and it performs clustering operations on both random waypoint and RPGM.
Some of the existing routing algorithms, based on clustering uses proactive
algorithm within the clusters and reactive algorithm between the clusters
(McDonald and Znati 1999, Yu-Chee et al 2007). In the proposed solution,
CH election is a transparent one for both types of clusterheads-dynamic and
static clusterhead. City buses and selected call taxis act as the dynamic
clusterhead, and mount static clusterheads at the chosen places where
dynamic mobility is high. Each CH node acts as master for all the members
and every event is recoded and updated continuously. CH is responsible for
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controlling the data propagation inside and between the clusters. Information
storage overload problems are avoided in VANET because of its abundant
storage facility (Bao et al 2009). The CCA can reduce the burden of CH with
relatively moderate speed and more connectivity. Delay of routing is reduced
by immediate distribution and reclustering. The routing process is separated
into intra-cluster routing and inter-cluster routing.
During intra-cluster routing, the source node would check if the
destination node is in the table of the neighbour. If present, then direct
communication would proceed. Otherwise CH would list out the neighbour
nodes containing destination node entries and start to flood the data to those
nodes. In inter-cluster route, when the CH receives the route request, it would
check the destination node in its cluster table. If yes, then the route between
the destination node and neighbour cluster would be established. Otherwise,
the route request would be forwarded to other CHs. Here mobility impacts the
clustering in two different ways. On one hand, an ordinary node may move
fast, far away from its clusterhead requiring it to affiliate to another cluster.
On the contrary it may move fast within the region causing congestion.
Random mobility propagates the information throughout the cluster
unconditionally but the prediction of the location of the nodes to be updated is
a time consuming process (Kogias et al 2009). Group mobility occurs when
the vehicles wait at the signals or when there is traffic congestion. All the
events regarding the traffic information are updated during the waiting period
at the junction places. Clusters of very small size are combined together to
form a new cluster and also process for identifying clusterhead for this new
cluster is triggered.
2.6.1 Hierarchical Cluster Based Routing
The performance of VANET routing protocols tends to decrease as
the mobility of nodes increases (Wisitpongphan et al 2007, Jerbi et al 2009,
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Wenjing et al 2009) resulting in decrease in cluster stability. It is desirable to
investigate how to utilize such rich connectivity and physical heterogeneity to
improve the stability of the cluster. Non-overlapping infrastructure and the
data propagation in the form of 2-hop manner is used. Each cluster should
retain the cluster information even over longer period of time. Based on
information maintained in each clusterhead, inter-cluster routes are
dynamically discovered through flooding followed by multicasting. It has the
features of both on-demand and table driven existence of bi-directional links,
which aid in both intra-cluster and inter-cluster routing.
Static and dynamic clusterhead coordinates the data transmission
within the cluster to other clusters. During inter-cluster routing, the
clusterhead should disseminate short-lived Time-to-Live (TTL) topology
information proactively even when there is no data to be sent. This should
happen frequently at particular intervals. By doing this the nodes confirm the
availability, current status of the vehicle, major changes like clusterhead
changes and link failures. Such proactive mechanism leads to increase in
overhead.
To reduce the overhead, reactive approach can be used in which the
route is searched only when there is data to be sent. Compared to other
approaches, in a reactive approach there is no initial route discovery delay,
which is undesirable in many circumstances. Instead of that initial flooding
and direction based 2-hop multicasting give better results. The propagation of
control packets is limited within each cluster by 2-hop transmission. The
transmission range of the antennas in the vehicles those to form a physical
communication hierarchy loop. Such a hierarchy enables direct information
exchange and it potentially increases routing performance. It is assumed that
there are four types of nodes, (i) static clusterhead, (ii) dynamic clusterhead,
(iii) gateway nodes and (iv) ordinary nodes in the network.
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2.6.2 Cluster Formation and Maintenance
The top layer, which contains the static clusterhead, establishes
communication over multi-hop paths. Each clusterhead periodically
broadcasts hello messages containing the cluster_id, and its location
information. Initially there is no dynamic clusterhead and it is set to 0 and the
topology is empty. During the cluster formation, all the nodes that are within
the range of static clusterhead will join the nearest cluster upon receiving a
hello message. When a node receives a new hello message from a different
cluster, based on the distance of the clusterhead, the node can continue or
leave the current cluster and join the new cluster. By checking the cluster_id
in the cluster and the distance, each hello message is relayed towards the
boundary of the cluster.
The details of the algorithm for cluster formation and maintenance
are described in CCA algorithm. The flowchart of CCA algorithm is shown in
Figure 2.2. Cluster parameters such as CJReq, CJRep, Vehicle_id, total
number of vehicles, Neighbour Vehicle_id, Vehicle counter and the number of
static and dynamic clusterheads are initialized. If there is no static CH at a
particular location, the dynamic CH(bus) is chosen as a new CH. Once the
CH has been selected, CJReq message is broadcast to initiate the cluster
formation. After the receival of CJRep cluster is formed around the static or
dynamic CH. Each Vehicle_id is registered in the CH routing table and from
this the number of vehicles in each cluster can be found out. If any one of the
vehicles in a scenario does not receive any CJReq message, it is announced as
temporary CH and it starts to propagate CJReq message to form a cluster
around it. If the temporary CH receives any CJReq from any nearby static or
dynamic CH, it will become a member of that cluster otherwise a node with
more number of stops and direction towards the neighbour becomes
a new CH.
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Figure 2.2 Flowchart of cluster construction algorithm
=Zero
No
Initialize cluster parameters
Determine the number of static and dynamic CH
Broadcast CJReq messages and
Initiate cluster formation
Calculate waiting time of CJRep messagesfrom each vehicle within its region
Waiting time
> threshold
Yes
Number of
static CHs
Choose the dynamic
CH (buses) with slow
speed and more number
of stops
>=1
Vehicle received
CJReq message
Announce it as a temp_CH
and form a cluster around it
No
Continue it as a single node
CH
Cluster formed around the static
clusterhead
Each vechile_id is registeredin the CH routing table
Yes
Calculate the number of clusters
Compute the CH and
number of vehicles in each
cluster
temp_CH broadcast CJReq
message to its neighbours
Calculate waiting time of CJRep message
from each neighbour vehicle
Waiting time >
threshold time
A virtual cluster is created
around temp_CH and itsneighbours
No
No
Neighbours are memberof another cluster?
Discard the CJReq
message
Yes
Yes
Any CJReq
from nearby
static or
dynamic CH
Virtual cluster becomes a
member of the nearby static
or
dynamic cluster
The node with the more
number of stops and
direction towards nearby
static CH becomes anew CH
Yes
No
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Pseudocode of Cluster Construction Algorithm is shown in
Figure 2.3. Cluster parameters such as CJReq, CJRep, Vehicle_id, total
number of vehicles, Neighbour Vehicle_id, Vehicle counter and the number of
static and dynamic clusterheads are initialized. Static clusterhead will
distribute CJReq to all vehicles within its range and store the number of
vehicles in the CH vehicle counter. Due to high mobility conditions many
relay nodes enter and leave between source and destination. Existing nodes
within the cluster discard CJReq to avoid congestion. If any one of the
neighbours receives CJReq, it will send the CJRep reply to the source.
Otherwise CJReq messages are distributed through the intermediate nodes
and the vehicle counter stores the number of vehicles involved during the data
propagation.
Cluster Construction Algorithm (CCA)
Parameters
CJReq (SVID,IMVID,DVID,VC,s,VLT) - Cluster Join Request
CJRep (DVID,IMVID,SVID,VC) - Cluster Join Reply
CVID - Current Vehicle_ID
DCH - Dynamic Clusterhead
DVID - Destination Vehicle_ID
IMVID - Intermediate Vehicle_ID
LID - Location ID
MV - Master Vehicle (buses)
NV - Number of Vehicles
NVID - Neighbour Vehicle_ID
SCH - Static Clusterhead
SP - Shortest Path
SVID - Source Vehicle_ID
VC - Vehicle counter
VD - Vehicle Distance
VID - Vehicle_ID
At Static Clusterhead
SCH having valid SVID distribute CJReq with LID to
all vehicles within its range
Figure 2.3 (Continued)
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Find all Immediate Neighbours(NVID) speed ‘s’ and
VLT
Find VC = # (NV) &
Register & Forward(CJReq) to NV
for each intermediate node
{
for each CJReq received
{
if new NVID == old NVID
drop(CJReq) to avoid repetition
if NVID == DVID
then DV send CJRep to source
else
{
a. CJReq to IMVID add(CVID)
b. Find VC = # (NV)
c. CJReq VC += VC
}
}
}
DCH forms cluster around its transmission range
a. If any new vehicle enters into range (DCH)
register its VLT into DCH routing table
member (DCH) <- new(vehicle)
b. If any new MV enters into the range (DCH)
{
if speed of old (DCH) < new(MV)
no change
endif
if speed of old (DCH) > new(MV)
then new(DCH) <- new (MV)
endif
Figure 2.3 (Continued)
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if speed of old(DCH) = new(MV)
vehicles with more LID entries becomes a
new (DCH)
endif
endif
}
At source
{
till(timestamp < threshold time)
{
link = SORT(CJRep IMVID)
for each link
{
find SP = Min (CJRep VC) and LID
send data to destination vehicle through SP
}
}
}
At Destination
{
for each CJReq received, send (CJRep) to source
{
if (speed > thresholdspeed)
update only VID in Clusterhead
else
sort(CJRep IMVID)
update details of vehicle to all nodes
}
}
SORT(CJRep IMVID)
{
for each IMVID in CJRep
{
calculate VD
SORT(IMVID) in ascending order of VD
calculate SP and number of vehicles
store all VID in an array of each clusterhead
}
}
Figure 2.3 Pseudocode of Cluster Construction Algorithm (CCA)
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Dynamic clusters are formed around the transmission range of
dynamic clusterhead. If any new dynamic clusterhead (bus) enters into the
dynamic cluster, clusterhead election process starts. The vehicle having more
number of stops and minimal speed becomes a new CH. The source can find
the shortest path to the destination by the distance and the number of
intermediate nodes from the CJRep message. All the events are updated
immediately in each clusterhead
2.6.3 Topology Discovery
In high mobility conditions, only the gateway nodes are involved in
the discovery of topology. All changes in topology should be immediately
propagated without delay. In regular interval period each node can sense the
presence of its neighbours by propagating hello messages. By flooding the
hello messages and using the reply from neighbours, clusterhead can choose
the neighbours based on the nodes having the updated destination information
and that are close to the destination. If a vehicle reaches the boundary of that
cluster and tries to enter the neighbour cluster it should initiate the topology
discovery process for that cluster to find out its initial scenario and update its
topology information. The node, which has the longest distance from the
clusterhead and the direction of travel towards the destination, becomes the
gateway node of that cluster. Those nodes which are at the border should
exchange the cluster update messages with the neighbour clusters’ gateway
nodes too. All the updates received by the border nodes from different
clusters are combined and sent to other nodes for further processing by the
clusterhead. Each node should enable their availability by registering their
node_id to its clusterhead. Flooding the topology updates in a reactive manner
reduces the traffic overhead to a greater extent.
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2.6.4 Packet Buffering
The packets are stored temporarily in dynamic clusterhead and
permanently in static clusterhead. Each route is computed from the routing
table entries associated with a lifetime in each node. Each clusterhead will
forward the packet to the neighbour clusterhead through relay nodes or
gateway nodes and reach the clusterhead of the destination cluster. The route
will be removed from the table entries when it expires. For destination node,
which moves frequently and is far away from source node, the route for the
destination cannot be found.
2.6.5 Route Recovery
In VANET, clustering and mobility are the basic parameters, which
determine the efficiency of a routing algorithm. This is because when a
vehicle is moving, it can be placed beyond the coverage range of its
neighbours. This will result in link failure. A novel routing algorithm has to
be quick: vehicles that belong to the failed route must be informed and a valid
replacement route must be discovered for further processing (Noureddine et al
2006). The proposed routing algorithm adopts the common approach for the
detection of route break used in most existing protocols. A link_error packet
is sent back to the source to notify the path break. The source initiates the
reroute process if it still has data to send. However, this approach will lead to
increase end-to-end delay and overhead. In order to recover routes efficiently
a more efficient method is used based on the lifetime of the dynamic
clusterhead at the destination node cluster. In this method the destination node
broadcasts backward beacons to the source. Once, the source of data receives
backward beacons, it chooses the adequate route with respect to the path with
minimal interference. However, due to frequent mobility and topology
changes, the backward beacons may not arrive at the source within a
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predefined time. The source of data may trigger a new route discovery by
broadcasting a new route request.
Route recovery process can be initiated without any delay if any
data loss occurs due to route failure. If the route failure is detected within the
cluster, route discovery process is initiated by the clusterhead through the
relay nodes. The relay nodes repair the broken path with minimum overhead.
If this method fails, the pair of nodes where the link has been disconnected,
should flood their status to their neighbouring nodes and find their alternate
way of connection and reinitiate a route discovery.
2.6.6 Routing in Clusters using 2-hop Information
Most research work on VANET clustering is focused on single-hop
clustering. Due to the limited area covered by each cluster and mobility
variations, the single-hop clustering needs frequent updates on routing
information (Vidhyapriya and Vanathi 2007). Recent researchers have shown
enormous interest in 2-hop routing (Uichin et al 2009). 2-hop routing will
play a central role in future enhanced VANET environments because it
improves network routing, avoiding overhead impact as node membership
increases. As a special case, a dense VANET that includes a large number of
vehicles located in a small area at a random period of time is considered.
Dense VANET deployment environments are not only of growing
commercial interest but also they can significantly simplify the design and
implementation of specific protocols and support solutions. In particular, the
2-hop routing, together with the comparative evaluation of novel heuristics
improves its effectiveness that addresses the formation of non-overlapping
clusters. The CHs assign all nodes within the 2-hop neighbourhood to the CH
and assign the role of relay to the nodes placed within the 2-hop
neighbourhood. The proposed algorithm respects the following constraints:
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1. Each node is identified by its 2-hop neighbour,
2. Every node must belong to at least one cluster under static
clusterhead or dynamic clusterhead,
3. Each cluster should contain minimum one clusterhead and
4. All the neighbour nodes should receive the initial flooding
information that later on they process with multicast operation
based on the destination location.
2.6.7 Link Path Maintenance
Data packets are able to continuously relay as long as connectivity
between source and destination remains in place. The packet delivery may fail
due to network partition, which is caused by the mobility of vehicles (Xiaoxia
et al 2008). Connectivity information between source, intermediate nodes,
gateways and destination mainly relies on periodical control messages. For
the path maintenance, link_flag of control messages is used to indicate the
connectivity status. Once it is fixed, the source of the link should flood the
status of the link to its nearby neighbours and execute the rerouting process.
The link_flag might be zero in two conditions: (i) when a vehicle moves to its
destination, which is far away; (ii) when the destination becomes unreachable
due to intermediate node movement. If a vehicle does not receive any control
message, either the static or dynamic source would initiate path setup phase to
designate other links and delete old links.
2.7 DATA FORWARDING
Depending on the density of the cluster, source forwards a data
packet towards the immediate neighbours. Source will wait for the
acknowledgement from the neighbours with valid cluster_id, to avoid the
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duplicate data forwarding. If the number of responding group members
exceeds predetermined threshold, a temporary queue is constructed for
collecting the reply messages from the cluster until the flooding is over. If a
requesting member does not get enough replies, data packet is propagated
along a separate path using the shortest path algorithm. A newly built cluster
is confirmed by a control message. If a temporary cluster is constructed, a
node requesting cluster creation broadcasts data packets during cluster
construction period. All nodes within a cluster, rebroadcast the packet if TTL
value is valid. All vehicles that were already forwarded with data packet of
same sequence number ignore packet during flooding. Accordingly, data
forwarding path rooted at sender does not have reversed link toward sender.
The parameter max_cluster_count can be used to avoid broadcast storm
problem by restricting the number of group members building their cluster.
Sometimes cluster members are used to forward the packet between two
clusters. In such cases, cluster members just rebroadcast receiving data
packets. When an intermediate cluster member receives the information from
a node at boundary, it attempts to identify the surrounding cluster members by
sending special control_queue packets.
When a group member does not belong to any cluster for some
period of time, it will announce itself as a clusterhead and start to create a
cluster around it by the data propagation (Gau et al 2002). Such being the
case, instant reliable delivery and efficient resource should be provided to the
new clusterhead. The redundancy can be allowed to some extent, if some link
failure occurs or the destination ignores the data packet. Data delivery failure
will be noticed when a destination cluster does not exist. In such a case, the
member relays data packet again if a node does not request to stop packet
relaying. Data packets can be continuously delivered to group members
through flooding without computation of routing table. It is intended for
networks with unpredictable topological changes and highly dynamic nodes.
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However, it uses adaptive mechanisms for restricting flooding in the static
network.
Packets are transmitted between the vehicles within the cluster
during intra-cluster transmission. The routing updates are done by the
clusterhead to its cluster member periodically and the members will update
their routing table after they receive the control packet. Routing tables are
updated by the corresponding clusterhead and gateway nodes (Gunter et al
2007). Packets are processed based on the priority of packets and the longest
path from different clusters. The timeout route items are removed from the
routing table. The overhead during updation process can be allowed. In order
to avoid unnecessary bandwidth utilization and to avoid overhead, the
clusterhead and gateways act as the backbone of routing and all the routing
activities are reflected only on this set of nodes. In dynamic mobility
conditions it is very difficult to maintain the stable link structure and the
topology also varies continuously that the clusterhead might change as well.
Hence the backbone architecture is eliminated altogether and a fully
distributed approach has to be convened.
During inter-cluster communication, two separate channels are used
between clusters: i) control channel; and ii) data channel. The routing
algorithm proposes proactive method for intra-cluster transmission and
reactive method for inter-cluster transmission. Each member of the cluster can
transmit/receive emergency messages and data packets to and from the
cluster-head during flooding. After receiving and processing each CH assigns
channel to transmit and receive different kinds of signals to the corresponding
cluster members. During inter-cluster routing the gateway to be used to
forward packets should be mentioned explicitly. Localization of certain nodes
and addressing packets will forward them to their destination. Each CH
collects the messages from its own cluster members and its nearby clusters
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and then consolidates information based on its priority. The CH uses the
control channel for beacon and hello messages. The data channel is used to
forward the data packets to the neighbouring cluster-heads. The vehicles from
different clusters will try to share the common channels to transmit/receive
the packets. Each relay station can randomly pick one of its neighbours,
which is closer to the destination and forward the message towards that node.
Therefore each packet should contain the information about destination,
location, direction and a set of neighbours to be traversed along. The closest
node to the destination location broadcasts the message to all stations in its
radio coverage area (Wiegel et al 2007). The clusterhead construction
algorithm provides the location information of nodes.
Each intermediate node should be aware of the traffic conditions.
The destination will receive the data packets and traffic conditions from
intermediate nodes instead of source. Once the path of transmission has been
established the shortest path algorithm is executed to find the path with
minimal interference. It is removed whenever the next shortest path has been
found. This process is continued until data transmission is over. In each
cluster all the clusterhead assigns its transmission power equal to the power
required to reach the furthest nodes of the cluster. However, the stable
structure has been maintained because all the calculated shortest paths are
available in clusterheads.
2.8 HANDLING FLOODING OVERHEAD
Large clusters are split into smaller ones for better management
purpose and routing. Due to large number of vehicles and different mobility
scenarios it is essential to implement inter and intra-cluster routing to localize
and manage packet routing. Source node will check the routes inside the
cluster by referring its 2-hop neighbour routing table after which direct
communication will happen. Otherwise the next level of 2-hop nodes will be
36
searched for further process until the destination has been reached. If the
search process continues until the border of the cluster and could not find the
destination node in its cluster region, then it would request route to the nearby
CH. After receiving the route request, it would check if the destination node is
in the nearby cluster table or not. If it is in the table, data will be delivered to
the destination. Otherwise, the route request would be delivered to other CHs.
Source node must know the topology of each cluster through the
gateway nodes before performing flooding. Each clusterhead should know the
vehicle_id of all the vehicles in that cluster and the connectivity among the
cluster nodes and relay nodes for the corresponding cluster. The intra-cluster
connectivity contributes the most to the level-0 routing table size and inter-
cluster connectivity contributes to level-1 routing table size. To distribute the
routing table size relevant to each node, a recursive flooding procedure is
employed for each level in the hierarchal clustering. Dissemination of the
cluster topology is initiated by the level-1 clusterhead.
The topology information in level-0 cluster is flooded only to
members with level-1 hierarchy. The flooding is initiated by the level-1
clusterhead. With the hierarchical structure, each node can unambiguously
forward packets towards the destination direction, which will reduce the
overhead incurred by flooded routing even at high mobility conditions.
2.9 RECLUSTERING
Reclustering provides clustering stability, network robustness,
longer connectivity and message reliability. Distributed mechanism for
clusterhead election, first demands certain level of consistency to be reached
among the nodes. In Vehicular Adhoc Networks (VANET) all the vehicles
communicate in a peer-to-peer fashion, without the need for a network
infrastructure. Reclustering will insert a kind of modified infrastructure
functionality and produce stable hierarchical structures to be built over
37
dynamically changing clusterheads which perform multi-hop forwarding,
efficient bandwidth allocation and cluster management. Mobility of vehicles
and topological parameters could reduce the advantage of using clusters by
requiring frequent updates of the routing tables. A chosen clusterhead should
keep track of routing table changes and ensure link maintenance.
The existing reclustering algorithms are either dynamic or static
(Avresky and Natchev 2005, Elmusrati et al 2007). The Lowest-ID algorithm
is one of the most popular static algorithms where the clusterhead role is
assigned to the node with the lowest ID (Lian et al 2007). Highest-Degree
(HD) algorithm is one of the dynamic algorithms in which the node with the
largest number of 1-hop neighbours is selected as clusterhead. Each node
should be aware of node mobility pattern, link failures and link reconnections
are caused by the movement of the nodes (Stojmenovic et al 2002). An
efficient reclustering in the proposed hierarchical clustering scheme is
suitable for the infrastructure-less VANET environment. During the
reclustering procedure, the decision-making is based on local information,
i.e., the node priorities based on node degree for clusterhead selection (Yu
and Chong 2005). The stability of the algorithm relates to the communication
overhead that is imposed by the underlying reclustering scheme, which affects
the network throughput. Compared to other reclustering schemes CCA
achieves better connectivity and hence better end-to-end message reliability
but overhead is increased because of the number of clusterhead modifications
due to mobility. The parameters that clusterhead election takes into account in
its procedure are, (i) node degree i.e., the number of 2-hop neighbours that
each node has inside its radio range and (ii) distance traveled by the vehicle.
For example, if the traveling time is calculated CCA will select as
clusterheads the nodes with more number of stops, a choice more suitable for
sparse networks. If it weighs more on the connectivity degree, the nodes
having a large number of 2-hop neighbours will be selected, a choice that is
38
more suitable for dense networks. Reclustering consists of a setup phase; then
nodes are deployed in the geographical areas and their mobility, topology,
speed, direction, counters and routing tables are initialized. Then the
algorithm of clusterhead selection and the update of the routing tables are
performed.
Vehicles are dispersed across the geographical area according to a
specific deployment pattern. CCA allocates to all nodes a unique vehicle_id.
Each vehicle holds a counter containing the information about the number of
times that it has been elected as clusterhead and a counter for the node degree.
The routing table should contain the information about 2-hop neighbour list
and the control message with all the packets that the node receives. The
neighbour and 2-hop neighbour list for each node is built by reading the
vehicle_id, which is included in the reply hello messages. After some
threshold time if no neighbour is heard, then that node becomes clusterhead.
Each vehicle in the network contains the information about node degree and
its 2-hop information, which are included in the reply packets that each
neighbour broadcasts. Different types of packets are generated during
reclustering cluster_JOIN, cluster_LEAVE and the ACK message to
acknowledge both the JOIN and LEAVE requests of the nodes during mobility
as shown in Table 2.1.
Table 2.1 Message parameters
Message Parameters
cluster_JOIN (vehicle_id,CH_ID)
cluster_LEAVE (vehicle_id,CH_ID)
ACK (vehicle_id,CH_ID)
HELLO (vehicle_id,CH_ID,Source
vehicle_id,Destination
vehicle_id,counter)
39
CCA sets a maximum to the number of neighbours that a CH is
allowed to advertise by flooding. Gateway nodes allow the acceptable number
of entries into the cluster that is equal to the cluster’s size. Parameters like
size of cluster, transmission rate and connectivity degree should not be
distributed to all nodes. If the cluster member connectivity degree is more
than the threshold, the node should run the rerouting algorithm and maintain
the load balancing with the neighbour nodes. CCA maintains stability
between the clusterhead and the neighbours who send control messages for
the communication establishment. If certain level of stability exists, the
cluster member becomes a CH, and then the selection of new clusterhead
triggers the reclustering procedures and updates the new events in all routing
tables.
2.10 EFFECT OF RWP MOBILITY MODEL ON CLUSTER
COUNT
The behavior of RWP mobility model by applying proposed and
existing algorithms for various vehicle counts is as follows. Even though
RWP model is easy to simulate, it has some unrealistic assumptions about
node movements like sharp turns and sudden stop. Sharp turns occur
whenever a node changes its direction after traveling for a random amount of
time and sudden stops occur when the node decides to stop at a particular time
instant. During a direction change, the speed chosen by a node is totally
independent of the previous speed. In Lowest-ID algorithm, nodes with the
lowest ID number become the clusterhead. In RWP, initially there is more
competition about the election of clusterhead and only small amount of
clusters are created. Later on whenever the vehicle count increases more
number of nodes are involved in cluster creation process leading to increase in
number of clusters. In MOBIC, cluster creation considers the relative mobility
of nodes. A group may consist of clusters that have similar mobility
40
characteristics. Several groups can be merged into one group depending on
the mobility of each group. A node with higher relative mobility is more
prone to be unstable and therefore this node should not be elected as a
clusterhead. Among the group of vehicles the vehicle with lowest relative
speed becomes the clusterhead. At RWP mobility conditions its difficult to
group the vehicles with relative speed when less vehicles are available.
Whenever the vehicle count increases, we can group the vehicles with the
relative speed and hence more number of clusters is created.
In MCC, the node having maximum connection is chosen as a
clusterhead. The random distribution of less number of vehicles in a
simulation environment creates only less number of clusterheads initially. The
random behavior decreases the clusterhead’s lifetime in MCC is more than
Lowest-ID and MOBIC as the degree of connectivity changes very rapidly. In
DDCA, the idea is to dynamically partition the network into nonoverlapping
clusters of nodes consisting of one parent with zero or more children. To join
a cluster, a node is expected to survive for a period of time ‘t’ with a
probability of at least ‘ ’. Initially more number of clusters are created
dynamically even at random situation. However when vehicle count increases
the maximum number of hops between any pair of nodes in the same cluster
varies dynamically depending on the mobility characteristics of the nodes
leading to decrease in the number of clusters. In CCA, at random mobility
conditions initially more clusters are created based on the predefined static
and dynamic clusterhead. After dynamic clusterhead election there is a
decrease in cluster count. Once the election process is over, CCA forms
constant number of clusters throughout the simulation. Even when vehicle
count increases it will produce constant number of clusters, avoiding
overhead. The number of reaffiliation in CCA increases with increased
number of vehicles and it decreases with further increase of vehicles as the
41
nodes tends to stay inside the transmission range of clusterhead despite their
random motion.
2.11 EFFECT OF RPGM MOBILITY MODEL ON CLUSTER
COUNT
The behavior of RPGM mobility model by applying proposed and
existing algorithms for various numbers of vehicles is as follows. RPGM
cannot be used as a basis for cluster formation, but can be useful for
predictive group mobility management. In RPGM, each group has a logical
“center” and the center’s motion defines the entire group’s motion behavior
including location, velocity, acceleration etc. If a node with a Lowest_ ID
happens to be highly mobile, it will cause severe re-clustering when it moves
into the transmission range of other clusterheads. MOBIC forms 1-hop
clusters with the relative speed of vehicles resulting in more reclustering in
initial phase. Whenever vehicle count increases there is a competition among
them to become the clusterhead. Based on the nodes with relative mobility,
with increase in vehicle count the number of clusters decreases. MOBIC does
not perform as well as Lowest_ID for vehicle count increase, because clusters
break up frequently, and the neighbour set changes frequently at the time of
group formation. It outperforms as vehicle count increases and the groups are
fixed.
In MCC, the nodes that have more number of neighbours are
elected as clusterheads. Initially less number of clusters is created during the
cluster formation phase; once the clusterhead is selected the further movement
of nodes along with the clusterhead will increase the number of clusters even
when the vehicle count increases. MCC builds 1-hop clusters with the
participation of clusterheads. During group behavior at peak time, pause times
exist between the vehicles resulting in increase in cluster stability for MCC.
DDCA forms less number of clusters but there is always a need to keep more
42
cluster member information leading to more reaffiliations. It does not require
pause time for initial cluster formation, which results in decrease in the
number of clusters if vehicle count increases. In CCA, clusterhead update is
very limited and hence brings no ripple effect. When there is no cluster to
join, it forms a new cluster. Hence, stable cluster structure is achieved thus
reducing the clustering overhead. The average number of clusters in CCA
decreases with increase in number of vehicles as it simply results in a
different configuration irrespective of its mobility. Thus, the role of the
clusterhead in CCA is retained as long as until simulation ends and its lifetime
are higher than others.
2.12 SIMULATION AND RESULTS
The simulation experiments are conducted by using Ns-2 simulator
with the objective to evaluate the efficiency of reclustering in CCA against
existing well known algorithms; Lowest-ID (LID), Mobility Metric Based
Clustering (MOBIC), Maximum Connectivity Clustering (MCC) and
Distributed Dynamic Clustering Algorithm (DDCA). The accounts are taken
at different possible mobility and topology conditions in order to analyze the
behaviour of the algorithms under these conditions. To this end, the input
parameters that were studied in the simulation were network density, speed
and direction variations, initial node deployment pattern, the user mobility
pattern, the radio transmission range and the packet transmission ratio.
The Ns-2 simulation model simulates nodes moving in an open
plane (Marc Greis 1995). NSG-2 tool is used to generate different scenarios
for Ns-2 simulator (Wu 2007). Motion follows the RWP and PRGM model,
source and relay nodes chooses a destination uniformly at random in the
simulated region, chooses a velocity uniformly at random from a configurable
range, and then moves to that destination at the chosen velocity. The nodes
are initially placed randomly in a rectangular region. Routing protocols
43
AODV and DSDV are considered. Simulations are done for networks with
50, 100,200 and 500 vehicles with 802.11 radios, with a nominal 250-meter
range. Table 2.2 summarizes parameters that are considered for CCA
simulation.
Table 2.2 Simulation parameters for CCA
Parameter Value
Number of nodes 50-500
Routing protocol AODV and DSDV
Mobility Model Random Waypoint and RPGM
Data flow CBR
Data packet size 512 bytes
Node placement Random
Terrain Area 2000m 2000m
Simulation time 2000s
In the experiments the mobility was simulated with the random
waypoint model and Reference Point Group Mobility Model. The nodes were
simulated to travel with an average speed in the range between the low
pedestrian speed of 5 km/h and the high vehicular speed of 120 km/h.
Initialization of VANET is done by deploying the nodes both randomly and in
groups. In the random deployment model the node mobility follows the
exponential distribution. Transmission range can be varied upto 250m.
However, if the transmission range is less, there are many clusters with
minimum overlapping instead of covering the entire area by a single cluster
resulting in overlapping of clusters with large membership.
44
N um ber of Vehicles
50 100 150 200 250
Nu
mb
er
of
Clu
ste
rs
0
5
10
15
20
25
30
CCA
Lowest-ID
M OBIC
M CC
DDCA
Figure 2.4 Number of clusters created under RWP mobility model
Figure 2.4 compares the number of clusters that were created by the
proposed and existing algorithms for various numbers of vehicles. CCA is the
most efficient with respect to this metric creating considerably same number
of clusters when the radio transmission covered 40 meters and more than
Lowest-ID, MOBIC, MCC (Maximum Connectivity Clustering), DDCA
(Distributed Dynamic Clustering Algorithm) and CCA. Cluster construction
can be performed parallel in the whole network. In Lowest-ID algorithm,
nodes with the lowest ID become the clusterhead. Election of clusterhead
competition increases whenever the vehicle count increases. Lowest-ID need
pause time of nodes for initial cluster formation to guarantee the accurate
information exchange between the neighbourhood node but in random
waypoint model all the vehicles are dynamic ones. In MOBIC, among the
45
group of vehicles the lowest relative speed of the vehicle becomes the
clusterhead. In dynamic mobility situations vehicles are distributed with
different speed at different times, so the number of clusters also increases.
MOBIC outperforms Lowest-ID by forming larger size clusters for medium to
high values of transmission range. In MCC, the node having maximum
connection is chosen as a clusterhead. However, cluster overhead increases at
the time of election resulting in increase in the number of clusters moderately
if vehicle count increases. Random behaviour decreases the clusterhead life
for MCC than Lowest-ID since the degree of connectivity changes very
rapidly, thereby reducing its lifetime but in the case of Lowest-ID clusterhead
change is not as frequent as in MCC. MCC performs worse than the Lowest-
ID algorithm when the number of vehicles is increased.
DDCA forms large size clusters at low mobility conditions and
cause table updates whenever topology changes occurs resulting in overhead
increase. During initial cluster formation DDCA does not require pause time.
The overhead and pause time leads to generate lesser number of clusters
throughout the process. In CCA, predefined static and dynamic clusterhead
forms constant number of clusters throughout the simulation. Even when
vehicle count increases it will produce constant number of clusters leading to
lesser overhead. In CCA, average number of clusterhead remains the same
even when the number of vehicle increases. Thus, the clusterhead change rate
is moderate than the existing algorithms. The number of reaffiliation in CCA
increases with increased number of vehicles and it decreases with further
increase of vehicles as the nodes tends to stay inside the transmission range of
clusterhead despite of their random motion. In CCA, a non-overlapping
cluster structure can be achieved with the introduction of predefined static and
dynamic clusterhead. This can reduce the number of small unnecessary
clusters. In CCA clusterhead update very limited and hence brings no ripple
effect.
46
N um ber o f Veh icles
50 100 150 200 250
Nu
mb
er
of
Clu
ste
rs
5
10
15
20
25
30
CCA
Lowest-ID
M O BIC
M CC
DDCA
Figure 2.5 Number of clusters created under RPGM mobility model
Figure 2.5 shows the number of clusters that were created by the
existing and proposed clustering algorithms under RPGM mobility model for
various numbers of vehicles. In group mobility behaviour, if the number of
vehicles increases at the peak time then the selection of clusterhead becomes
a competition in Lowest-ID, leading to decrease in number of clusters.
MOBIC forms 1-hop clusters with the relative speed of vehicles results in
more reclustering during simulation starts. Whenever vehicles count increases
there is a competition occurring between them to become clusterhead, this
decreases the number of clusters. Although MOBIC does not perform as well
as Lowest-ID when vehicle count increases, because clusters are broken up
frequently, and the neighbour set changes frequently at the time of group
formation. It does outperform the later when vehicle count increases. In MCC,
the clusterhead is elected based on the nodes having more number of
47
neighbours. MCC builds 1-hop clusters with the participation of clusterheads.
MCC needs pause time of motion of nodes for initial cluster formation to
guarantee the accurate information exchange between the neighbourhood
nodes. During group behaviour at peak time, pause time exist between the
vehicles resulting in cluster stability increase for MCC. DDCA forms clusters
with large size and need to keep more cluster member information leads to
more reaffiliations. It does not require pause time for initial cluster formation
resulting in decrease in number of clusters if vehicle count increases
(Sharmila John and Blessing Rajsingh 2008). In CCA, a non-overlapping
cluster structure can be achieved with the introduction of predefined static and
dynamic clusterhead. This can reduce the number of a small unnecessary
cluster.
In CCA, clusterhead update is very limited and hence brings
minimal ripple effect. If there is no cluster to join, it forms a new cluster.
Hence, stable cluster structure is achieved and minimized clusterhead count
can reduce the clustering overhead. The number of reaffiliations is less when
compared to other algorithms as its cluster members are at 2-hops from their
static and dynamic clusterhead. The clusterheads are nodes tend to stay inside
the clusters for longer time. The average number of clusters in CCA decreases
with increase in number of vehicles as it simply results in a different
configuration irrespective of its mobility. Thus, the role of the clusterhead in
CCA is retained as long as the simulation ends and it has a longer lifetime
than others.
48
V ehic le speed (km /h)
20 40 60 80 100 120
Clu
ste
rhe
ad
ch
an
ge r
ate
0
1
2
3
4
5
6
7
CC A
Lowest-ID
M O B IC
M C C
DD C A
Figure 2.6 Clusterhead change rates at various vehicle speeds under
RWP mobility model
Figure 2.6 shows the clusterhead change rate at various vehicle
speeds under RWP mobility model. An experimental result shows the CH
change rates for Lowest-ID, MOBIC, MCC, DDCA and CCA when nodes
were placed randomly in the field. The average clusterhead lifetime is lower
in Lowest-ID, since the degree of connectivity changes rapidly for the
vehicles and its clusterhead change also occurs very frequently, thereby
reducing its average lifetime whenever the vehicle speed increases. The nodes
tend to stay inside its cluster for a longer time at low mobility situations,
thereby increasing clusterhead change rate. In MCC the connectivity of the
clusterhead changes more rapidly than Lowest-ID. Thus the number of
reclustering is higher than Lowest-ID. The stability of the clusters in MCC
increases only when the pause time of the nodes increase which is impossible
49
in real life scenarios. MOBIC considers the vehicles with low relative speed;
cluster count increases throughout the area whenever the speed of the vehicles
is less. Competition occurs between them during low mobility condition and
hence the clusterhead change rate also increases initially. MOBIC forms large
size clusters vehicle speed is high. DDCA does not require pause time on
motion. It creates more number of clusters at high mobility condition leading
to increase in clusterhead change rate from the beginning of the simulation
itself. CCA provides higher stability than other existing algorithms as it
outperforms in terms of clusterhead lifetime, reclustering and clusterhead
change metrics by the introduction of predefined static and dynamic
clusterhead. CCA may be more feasible for somewhat congested places,
where vehicles are highly connected.
Vehicle Speed (km /h)
20 40 60 80 100 120
Clu
ste
rhe
ad
ch
an
ge r
ate
1
2
3
4
5
6
7
CCA
Lowest-ID
MO BIC
MCC
DDCA
Figure 2.7 Clusterhead change rates at various vehicle speeds under
RPGM mobility model
50
Figure 2.7 shows the clusterhead change rate at various vehicle
speeds under RPGM mobility model. In Lowest-ID, initially at low mobility
conditions, because of continuous election of clusterhead more numbers of
clusters are formed resulting in increased clusterhead change rate. Compared
to Lowest-ID, MOBIC will generate more number of clusters at low mobility
conditions and hence the rate of change is higher. In group mobility all the
vehicles are migrate from one place another to another along with their
neighbours. Initially, at low mobility conditions there is more connectivity
between the vehicles and hence clusterhead change rate is also high in MCC.
In MCC, the change of rate decreases whenever the speed of vehicles
increases because the link between them is less compared to low mobility
conditions. DDCA does not require the static assumption at the time of initial
cluster formation, because it utilizes the mobility behaviour of nodes to decide
the relative speed or path available resulting in increase of clusterhead change
rate. In CCA, the constant number of static and dynamic clusterhead leads to
stable cluster formation resulting in clusterhead change rate.
Overhead occurring at various vehicle densities under RWP
mobility model is shown in Figure 2.8. In Lowest-ID, each node broadcasts
hello messages periodically to participate in the network. Every node of
cluster radius ‘R’ retransmits each hello message. Thus, the drawback of
Lowest-ID algorithm is that certain nodes processing power is decreased due
to serving as clusterhead for longer period of time. There is a maximum
number of temporary clusterhead elections at high-density conditions leading
to increase in overhead. In MOBIC, each node in the cluster formation phase
needs two more hello messages to calculate its mobility metric, which results
in longer cluster formation duration and hence increased overhead.
51
Vehicle density
100 200 300 400 500
Ove
rhead
(%
)
2
4
6
8
10
12
14
16
CCA
Lowest-ID
M OBIC
M CC
D DC A
Figure 2.8 Overheads at various vehicle densities under RWP mobility
model
In MCC, the neighbours of a clusterhead become members of that
cluster and can no longer participate in the election process. Since no
clusterheads are directly linked, only one clusterhead is allowed per cluster.
As the number of nodes in a cluster is increased, the throughput drops. As the
density of the cluster increases, reclustering is done and hence overhead
increases. In DDCA, any node that possesses a low relative speed than its
neighbours has the ability to become a CH. This operation requires a lot of
pair wise communication before the decision is made. This leads to increase
in overhead if the number of vehicles increases. In CCA, the election of slow
speed vehicle shares the information with other vehicles and static clusterhead
in random scenario. Reclustering process is reduced by the election of
predefined time chart vehicles which also decreases the hello messages count
even if vehicle count increases.
52
Vehicle density
100 200 300 400 500
Overh
ead
(%
)
2
4
6
8
10
12
14
16
18
CCA
Lowest-ID
MO BIC
MCC
DDCA
Figure 2.9 Overheads at various vehicle densities under RPGM
mobility model
Figure 2.9 shows overhead at various vehicle densities under
RPGM mobility model. In Lowest-ID, the node with lowest ID in its k-hop
neighbourhood becomes the clusterhead. In Lowest-ID algorithm certain
nodes are prone to power drainage due to serving as clusterheads for longer
periods of time. In RPGM model, reclustering in Lowest-ID indicates that a
clusterhead movement may still invoke the complete cluster structure re-
computation leading to overhead increase. In MOBIC, the frequent
reclustering results in lower throughput and longer delay. In MOBIC, all the
decisions are based on the group reference member, the frequent reclustering
and the corresponding clusterhead election results in increase in the cluster
maintenance overhead. In MCC, the node with maximum number of
neighbours is chosen as a clusterhead. Each node either becomes a
53
clusterhead or an ordinary node. This system has a greater rate of clusterhead
change but the throughput is low. Whenever the vehicle count increases the
reaffiliation count of nodes is high due to node movement and as a result, the
current clusterhead may not be re-elected.
At high-density conditions the reclustering in MCC may cause
large communication overhead in the network, when there is frequent CH
disconnects in the cluster architecture. In DDCA, group movement of nodes
in urban area creating and maintaining cluster structures within an adhoc
network comes with additional communication and computation costs.
Cluster related information is exchanged rapidly; causing higher bandwidth
consumption and reduced network performance leading to overhead increase.
However in CCA, all the nodes become a member of either static or dynamic
clusterhead. During high mobility, if any node does not become member of
cluster, it declares itself as a clusterhead and forms a cluster around its
transmission range until a predefined clusterhead comes within its range. This
will reduce the number of reclustering and minimize overhead.
In this chapter the performance of hierarchical clustering-Cluster
Construction Algorithm (CCA) is discussed in detail. Overheads and
clusterhead change rates are reduced by the combination of static and
dynamic clusterheads. Various simulations are carried out and results are
compared with the existing algorithms. It is observed that the CCA algorithm
performs well under different mobility conditions. In the next chapter the
importance of the selection of proper mobility model is discussed.