An efficient routing approach for aggregated data transmission along with performance improvement in wireless sensor networks

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Special Issue: 07 | May-2014, Available @ http://www.ijret.org 168
AN EFFICIENT ROUTING APPROACH FOR AGGREGATED DATA
TRANSMISSION ALONG WITH PERFORMANCE IMPROVEMENT IN
WIRELESS SENSOR NETWORKS
Subhashini S1
, Parani T K2
1
Student, Electronics and Communication Department, Dhanalakshmi Srinivasan College of Engineering, TamilNadu,
India
2
Assistant Professor, Electronics and Communication Department, Dhanalakshmi Srinivasan College of Engineering,
TamilNadu, India
Abstract
Wireless Sensor Networks consist of large number of sensors which are deployed close to the proximity which is to be monitored.
Sensors are energy-constrained devices, so it is very important to design a routing protocol for transmitting the aggregated data to
the sink node effectively. In this paper, a novel approach called EDRINA (Enhanced Data Routing for In-Network Aggregation) is
proposed. EDRINA forwards data using spanning tree in the cluster, where cluster head is elected using forward aware technique,
towards the destination node. It has features like maximized data aggregation rate, low cost, low overhead, thus improves the network
lifetime by reducing energy consumption. EDRINA is compared with Data Routing for In-Network Aggregation (DRINA), Shortest
Path Tree (SPT) and Information Fusion based Role Assignment (InFRA) algorithms using Network Simulator and is proved to be
outperforming in all the evaluated scenarios.
Keywords: Data Aggregation, Routing Protocol, Wireless Sensor Network
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1. INTRODUCTION
Wireless Sensor Networks (WSN) consist of large number of
sensors which are deployed in the environment to monitor
various phenomenon’s like pressure, temperature, sound,
motion, or vibration in different locations. WSNs are widely
used in many applications like monitoring critical
infrastructure, home land security, environmental monitoring,
communication, military and in many other critical
applications[2].Sensor nodes in the network are energy-limited
as they are battery operated and the energy consumption is
generally associated with the data gathering and all other
operations performed by the nodes. Among all the activities
performed in the network, communication is the most
expensive activity in terms of energy consumption [1].
Data-driven networks generate large amount of data [1], which
are to be transmitted efficiently to the sink node. Transmission
of large amount of data consumes more energy which
decreases the network lifetime. In order to reduce the energy
consumption, the data aggregation technique is performed,
which in turn increases the network lifetime [6]. For routing
the aggregated data, an efficient routing approach is required
as the lose of aggregate data packet cause an immense impact
on data inference at the monitoring center.
A novel approach called EDRINA (Enhanced Data Routing
For In-Network Aggregation) is proposed here. It is a cluster
based approach where the cluster head acts as the aggregation
point and they transmit the packets only in the forward manner
in the network. Spanning tree is formed in the cluster for
forwarding the data. Thus it maximizes the data aggregation
rate, reduces the tree cost, and reduces the overhead. This
results in reducing the energy consumption for the
communication cause the increased network lifetime.
2. RELATED WORKS
Various algorithms have been proposed to provide data
aggregation during the routing in WSNs. Some of them are
tree-based algorithms, cluster-based algorithms while others
are simply structure-less algorithms [2].
2.1 Shortest Path Tree (SPT) Algorithm
It is a tree-based routing approach. It usually depends on
hierarchical organization of the nodes. It uses a very simple
strategy to construct the tree structure. Each source sends its
information to the sink along the shortest path between the
two [4]. Where these paths overlap for different sources, they
are combined to form the aggregation tree. It has static routes
[1].

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2.2 Information Fusion based Role Assignment
(InFRA) Algorithm
It builds a cluster for each event including only those nodes
that were able to detect it. Then, cluster-heads merge the data
within the cluster and send the result toward the sink node[3].
The InFRA algorithm aims at building the shortest path tree
that maximizes information fusion. Thus, once clusters are
formed, cluster-heads choose the shortest path to the sink node
that also maximizes information fusion by using the
aggregated coordinators distance [3]. Main drawbacks of
InFRA algorithm are very high overhead rate , low scalability
and very high cost[1].
2.3 Low Energy Adaptive Clustering Hierarchy
(LEACH) Protocol.
LEACH protocol is one of the most famous WSN hierarchical
routing algorithms. In LEACH, the nodes organize themselves
into local cluster, the protocol is divided into a setup phase
when the clusters are organized and a steady-state phase when
data are transferred from the nodes to the cluster head and on
to the sink [8]. LEACH-based algorithms assume that the sink
can be reached by any node in only one hop, which limits the
size of the network for which such protocols used [5].
2.4 Data Routing For In-Network Aggregation
(DRINA) Approach
It is a cluster-based approach. The Cluster head (CH) acts as
the aggregation point. It aims at maximizing the overlapping
routes for data transfer and reducing the communication cost.
The drawbacks of DRINA are: the routes are static and the
overhead is high [1].
3. EDRINA: ENHANCED DATA ROUTING FOR
IN-NETWORK AGGREGATION
EDRINA is a cluster based approach, which mainly aims at
improving the performance in the network while maximizing
the data aggregation in the network. Here, CH acts as the
aggregation point. It makes use of Periodic Simple
Aggregation as its timing strategy for data aggregation. It
requires each node to wait for a predefined period of time
while aggregating all received data packet and, then, forward a
single packet with the result of the aggregation [2].
For routing the data to the sink node from the source nodes
clusters of sensors are formed in the network. In each cluster a
node is elected as cluster head (CH), which acts as the
aggregation point. It receives data from all other nodes in the
cluster for aggregation and forwards it to the destination via
intermediate nodes which also includes CH of other clusters.
Here, CH is elected on the basis of distance from the sink node
i.e., the node closer to the sink will be elected.
Nodes can vary transmission power according to the distance
to its receiver[8]. Initially the sink node broadcast to all the
nodes in the network. The message broadcasted by the sink
node will have the ID field containing the identifier of the
node, DIST field which has the distance in hops from the sink
node as shown in the Fig 1. Based on the DIST field value the
node closer to the sink in each cluster can be obtained. Thus
the CH will perform only forward data transmission towards
the sink, which reduces the energy consumption in each node
and improves the speed of transmission. This results in
reduced delay in the aggregated data transmission.
Fig 1 Flowchart for the initial message broadcasting for
distance from sink estimation

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Spanning tree is constructed in the network for forwarding the
data. The flow chart for the data transmission and tree
formation is given in the Fig.2. In the tree, node with smallest
identifier i.e., CH acts as root node. It exchanges the message
containing the identifier of the node field, its root and the
distance from the root in hops. The source node from which it
receives the message is considered as the parent node.
Identifier of the node is used to break the ties in the network.
This algorithm makes use of shortest path for forwarding the
data.
Timer is made use of for transmitting the message in a
particular preset time. It helps for the maintenance of tree in
the network. Whenever a message is transmitted, the timer P is
started. It will get reset when there occurs any change in
neighboring node. This approach helps to update the node
itself and to transmit the message to the root node of the tree.
Assume rn be the identifier of the root node for the node n. dn
be the shortest distance from the node n to rn, mn be the
message send by the node n which consists of n, rn, dn values,
pn be the parent node ID for the node n and trecv,n be the time
node n receive the message from the parent node as given in
Fig 2. As the shortest path is used for data transmission, the
energy usage is reduced and prolongs the network lifetime.
The spanning tree used in the routing structure in cluster helps
to improve the performance of the network. Performance of
the proposed approach is compared with other known
solutions in the following section
4. PERFORMANCE EVALUATION
EDRINA algorithm is evaluated and its performance is
compared with other three known solutions: DRINA, SPT and
InFRA algorithms. Performance metrics such as Efficiency,
Overhead, Treecost are obtained by simulating the algorithm
using Network Simulator version 2.34, by varying the number
of nodes in the network as well as the number of events sensed
by the nodes. Following are the various parameters evaluated
for the algorithms:
Data Packet Delivery Rate: This metric defines the quality of
tree built by the algorithm. It denotes the number of packets
reach the sink node. The lower the rate, higher will be the
aggregation rate.
Control Packet Overhead: It denotes the number of control
message used to set up the routing tree along with the cluster
formation.
Efficiency: Packets per processed data. It denotes the rate
between the number of packets transmitted to the number of
packets received by the sink.
Routing Tree Cost:It denotes the number of edges in the
routing tree structure built by the algorithm gives the treecost.
4.1 Simulation Parameters
Table 1. Simulation Parameters
Simulation Parameter Value
Simulator NS v2.34
Topology Size 1200m x 1200m
Number of Nodes 500
Communication Radius 80m
# of Events 5
Simulation Duration 4 hrs
Event Duration 4 hrs
Fig 2. Flowchart for building and data transmission in the
spanning tree.

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5. RESULTS
Each of the discussed performance metrics of the EDRINA
algorithm are evaluated under various situations in the
network for determining its efficiency. The algorithms
performance is compared with the known protocols DRINA,
SPT and InFRA under various criteria to out show its
capability in various scenarios. For evaluating the
performance of EDRINA in different scenarios the number of
nodes in the network is varied from 100 to 500. Similarly, the
number of events are varied from 1 to 5 for simulating the
algorithm. The nodes sense all the events simultaneously in
the network.
5.1 Impact of Network Size
For determining the impact of network size on the algorithms,
the numbers of nodes are varied in the network. This helps to
evaluate the algorithms scalability.
Fig 3 shows the efficiency of the EDRINA algorithm
compared with other 3 known algorithms :DRINA, SPT and
InFRA. Its clear that the number of packets per processed data
is less for EDRINA compared to other algorithms proves that
its efficiency is higher than that of DRINA and others.As the
data aggregation rate of EDRINA is higher, the delay
associated with the data transmission will get reduced. So, the
aggregated data packets are transmitted with very less delay in
the network deployed with EDRINA as routing approach than
DRINA algorithm.
Fig 3 Efficiency: Packets per processed data vs number of
nodes.
The graph shown in the Fig 4 gives the treecost of the
algorithms. Number of edges required for EDRINA is very
less when compared to SPT Whereas for DRINA it is medium
value. Thus the routing tree for the EDRINA algorithm is
proved as very efficient.
Fig 4 Treecost: Edges Vs Number of Nodes
Fig 5 shows the overhead for the EDRINA algorithm
compared with other known solutions by varying the number
of nodes in the network. It is a known fact that Overhead for
DRINA is very large compared to others[1], whereas the
overhead for the EDRINA is less, thus shows it requires less
control message to built the routing structure than other
algorithms. Thus it is proved that, EDRINA’s performance is
efficient even when the number of nodes are varied in the
network.
Fig 5 Overhead: Packets vs Number of Nodes
5.2 Impact of Number of Events
The performances of the algorithms are evaluated by varying
the number of events sensed by the nodes in the network by
keeping the number of nodes as constant. Numbers of events
are varied from 1 to 5 for evaluation.

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Fig 6 Efficiency: Packets per processed data vs Number of
events.
The Fig 6 shows the graph indicating the efficiency of the
EDRINA algorithm. The number of packets transmitted for
the EDRINA is very less compared to that of SPT. The
DRINA has less than that of Infra and SPT. Even when the
events are increased the number of aggregated data packet
transmission is less compared to that of others. This proves
that EDRINA has an efficient routing structure with high
aggregation rate compared to others. Thus its proved that the
efficiency of the EDRINA is much better than the other
known solutions.
Fig 7 Treecost: Edges vs Number of Events
Treecost for the proposed algorithm is given in the Fig 7.
When the number of events are increased, the number of edges
are increased in the structure. The number of edges required
for the EDRINA is very less compared to that of DRINA, SPT
and InFRA algorithms. When the number of edges required is
less, the treecost for the algorithm will also get reduced.
EDRINA has very lower treecost.
Fig 8 Overhead: Packets vs Number of Events
The graph in Fig 8 shows that the number of control packets
required for the algorithm increases with increase in the
number of events in the network. Proposed approach EDRINA
requires less control message for the routing structure, as there
occurs no flooding in the networks, the packets transmitted
also very less. Thus, the packet overhead is very less for
EDRINA and is maximum for DRINA. Decrease in the
overhead improves the performance of the EDRINA algorithm
and makes it very suitable for the routing of aggregated data
packets in sensor networks.
Fig 9 Delay: Time vs Number of Nodes

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The graph shown in the fig 9 denotes the delay associated with
the data transmission in the network with DRINA and
EDRINA approaches. From the graph its clear that the delay
for EDRINA is less compared with the known solution. Thus
it proves that the data transmission is faster in the network
deployed with the proposed approach.
6. CONCLUSIONS
Wireless Sensor networks are widely used for monitoring
various phenomenon in different applications. The proposed
EDRINA algorithm is used for routing the aggregated data
packets in WSNs. It is a cluster-based approach and makes use
of spanning tree for routing the packets. It uses the technique
of forward-aware routing for data transmission in the cluster.
The EDRINA algorithm is compared with the other known
solutions: DRINA, SPT and InFRA algorithms for the
performance evaluation by varying the number of nodes and
number of events sensed in the network. EDRINA approach
significantly reduces the number of data transmissions so as to
decrease the overhead. Simulation results shows that EDRINA
outperforms the other known solutions in all the evaluated
scenarios. Thus, its proved that EDRINA has higher
aggregation rate, efficiency low overhead and delay. This
decreases the energy consumption in the nodes lead to
increased network lifetime. Its application can also be
extended to the Mobile Wireless Sensor Networks. Data
transmission has to be performed in a reliable way, EDRINA
algorithm can be extended in such a way that it provides
reliability in aggregated data transmission in the WSNs.
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BIOGRAPHIES
Subhashini S is currently persuing her Masters in
Engineering from Dhanalakshmi Srinivasan
College of Engineering, Coimbatore. She is
interested in WSNs, MANET.
Parani T K is currently working as an Assistant
Professor in Dhanalakshmi Srinivasan College of
Engineering, Coimbatore. Her area of interests are
Wireless Communication and WSNs.

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