DESIGN OF ENERGY EFFICIENT ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORK (WSN) USING PASCAL GRAPH

Rupak Bhattacharyya et al. (Eds) : ACER 2013,
pp. 175–189, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3217
DESIGN OF ENERGY EFFICIENT ROUTING
ALGORITHM FOR WIRELESS SENSOR
NETWORK (WSN) USING PASCAL GRAPH
Deepali Panwar1
and Subhrendu Guha Neogi2
1
Department of Computer Science and Engineering, Sir Padampat Singhania
University, Udaipur, Rajasthan, India
1
deepali.panwar@spsu.ac.in
2
subhrendu.neogi@spsu.ac.in
ABSTRACT
Development of energy efficient Wireless Sensor Network (WSN) routing protocol is nowadays
main area of interest amongst researchers. This research is an effort in designing energy
efficient Wireless Sensor Network (WSN) routing protocol under certain parameters
consideration. Research report discusses various existing WSN routing protocols and propose a
new WSN energy efficient routing protocol. Results show a significant improvement in life cycle
of the nodes and enhancement in energy efficiency of WSN. In this paper, an attempt has been
made to design a wireless sensor network involving the extraction of Pascal Graph features.
The standard task involves designing a suitable topology using Pascal Graph. As per the
definition of interconnection network it is equivalent that a suitable graph can represent the
different computer network topologies very efficiently. Different characteristics of Pascal Graph
Topology has been discovered and used in network topology design. Since Pascal Graph gives
better result in terms of finding the dependable and reliable nodes in topology, it has been
considered for network analysis. Moreover, we propose a methodology that involves the Pascal
Graph Topology for wireless sensor network which can analyse and represent the network and
help in routing.
KEYWORDS
WSN, Energy efficient, WSN routing protocol, Pascal Matrix and Pascal Graph
1. INTRODUCTION
Wireless Sensor Networks (WSN) is an emerging technology that could revolutionize the way
wireless network access is provided. The interconnection of different wireless devices using
wireless links exhibits great potential in addressing different connectivity issue. Wireless Sensor
Network (WSN) is a wireless network consisting of small nodes with sensing, computation, and
wireless communications capabilities [1]. Each sensor collects data from the monitored area (such
as temperature, sound, vibration, pressure, motion or pollutants) and then it routes data back to
the base station [2-3]. Data transmission is usually a multi- hop, from node to node toward the
base station. To realize this vision, it is imperative to provide efficient resource management.
Resource management encompasses a number of different issues, including routing. As wireless
sensor networks consist of hundreds to thousands of low-power multi functioning sensor nodes,
operating in an unattended environment, with limited computational and sensing capabilities,
sensor nodes are equipped with small, often irreplaceable batteries with limited power capacity.

176 Computer Science & Information Technology (CS & IT)
WSN consist of hundreds or thousands of small, cheap, battery-driven, spread-out nodes bearing
a wireless modem to accomplish a monitoring or control task jointly. Although a profusion of
routing mechanisms has been proposed for other wireless and mobile networks, the unique
characteristics of WSN suggest that WSN demand a specific solution. To have a clear and precise
focus on future research in WSN routing, the characteristics of WSN that have a strong impact on
routing must be identified. Then a set of criteria is defined against which the existing routing
protocols from ad hoc, sensor, and WSN can be evaluated and performance metrics identified.
This will serve as the basis for deriving the key design features for routing in wireless sensor
networks. An important concern is the network lifetime: as nodes run out of power, the
connectivity decreases and the network can finally be partitioned and become dysfunctional [2-3].
Routing in WSNs is a very challenging problem due to the inherent characteristics which
differentiate such networks from other wireless networks such as ad hoc networks and cellular
networks [2-4]. In recent years, many algorithms have been proposed for the routing issue in
WSNs. The minimum energy routing problem has been addressed [5-6]. The minimum total
energy routing approaches in these papers are to minimize the total consumed energy. However,
if all traffic is routed through the minimum energy path to the destination, the nodes along that
path will run out of batteries quickly rending other nodes useless due to the network partition
even if they do have available energy. Instead of trying to minimize the total consumed energy on
the path, the objective is to maintain the connected network as long as possible. If sensor nodes
consume energy more equitably, they continue to provide connectivity for longer, and the
network lifetime increases [5-10]. A number of potential solutions have been proposed, including
full end-to-end wireless access and ad-hoc networks. Industry has divergent views on some
parameters based upon their experience of designing wireless sensor network which includes
network components, properties of sensor nodes, degree of mobility, traffic pattern, energy
efficiency of sensor nodes, routing between sensor nodes. The Wireless Sensor Network has been
designed using different data structures such as graph, tree, linked list and other techniques like
neuro-fuzzy, swarm intelligence etc. A sufficient representation has been so far made to design
different computer network topologies using appropriate graph models. Graphs have huge
contributions towards the performance improvement factor of a network. Some major
contributors are de-Bruijn, Hypercube, Mesh and Pascal. As per the definition of interconnection
network it is equivalent that a suitable graph can represent the physical and logical layout very
efficiently. However, deploying these networks requires the installation of a large amount of
wireless sensor devices. The initial investment costs for deployment and the difficulty of
deployment in some environment settings have prevented the widespread realization of such
access networks. As sensor networks have specific requirements on energy saving, data-oriented
communication, and inter-connection between non-IP and IP, therefore sensor network dedicated
routing protocols may be required, for energy efficient routing scheme. In WSN there are the
routing protocols that minimize the used energy, extending subsequently the life span of the WSN
[8-9, 11-12]. Energy awareness is an essential in routing protocol design issue. Depending on the
network structure, routing in WSNs can be divided into:
a) Flat-based routing
b) Hierarchical-based routing
c) Location-based routing
Depending on the protocol operation, routing in WSNs can be divided into:
a) Multipath-based routing
b) Query-based routing
c) Negotiation-based routing
d) QoS-based routing
e) Coherent based routing

Computer Science & Information Technology (CS & IT) 177
Against this backdrop, the present study first attempts to make a comparative analysis of different
graph topologies on the identified features of wireless sensor network and propose an energy
efficient routing algorithm in wireless sensor network designed using Pascal graph topology. In
this paper, we revisit this main idea is to design a suitable algorithm to create topology in wireless
sensor network using Pascal Graph. The article is organized as follows. Section 2 briefly reviews
different techniques and Section 3 deals with a precise description of our methodology. Section 4
is devoted to design issues and section 5 emphasizes on discussion of the study. Finally, Section 6
concludes the paper and gives suggestions for future work.
2. LITERATURE REVIEW
Architectural design issues and their implications pertaining to the Wireless Sensor Network have
been observed by host of scholars. The issues related to network dynamics depends on three main
components in a sensor network. These are the sensor nodes, sink and monitored events. Aside
from the very few setups that utilize mobile sensors [13], most of the network architectures
assume that sensor nodes are stationary .On the other hand, supporting the mobility of sinks or
cluster-heads (gateways) is sometimes deemed necessary [14]. Routing messages from or to
moving nodes is more challenging since route stability becomes an important optimization factor,
in addition to energy, bandwidth etc. The sensed event can be either dynamic or static depending
on the application [15]. Another consideration is the topological deployment of nodes. This is
application dependent and affects the performance of the routing protocol. The deployment is
either deterministic or self-organizing. In deterministic situations, the sensors are manually placed
and data is routed through pre-determined paths. However in self-organizing systems, the sensor
nodes are scattered randomly creating an infrastructure in an ad hoc manner [16-19]. During the
creation of an infrastructure, the process of setting up the routes is greatly influenced by energy
considerations. Since the transmission power of a wireless radio is proportional to distance
squared or even higher order in the presence of obstacles, multi-hop routing will consume less
energy than direct communication. However, multi-hop routing introduces significant overhead
for topology management and medium access control. Direct routing would perform well enough
if all the nodes were very close to the sink [17]. Most of the time sensors are scattered randomly
over an area of interest and multi-hop routing becomes unavoidable. Depending on the
application of the sensor network, the data delivery model to the sink can be continuous, event-
driven, query-driven and hybrid [15]. In the continuous delivery model, each sensor sends data
periodically. In event-driven and query-driven models, the transmission of data is triggered when
an event occurs or a query is generated by the sink. Some networks apply a hybrid model using a
combination of continuous, event-driven and query-driven data delivery. The routing protocol is
highly influenced by the data delivery model, especially with regard to the minimization of
energy consumption and route stability. For instance, it has been concluded in [20] that for a
habitat monitoring application where data is continuously transmitted to the sink, a hierarchical
routing protocol is the most efficient alternative. This is due to the fact that such an application
generates significant redundant data that can be aggregated on route to the sink, thus reducing
traffic and saving energy. In a sensor network, different functionalities can be associated with the
sensor nodes. In earlier works shown in [21-23], all sensor nodes are assumed to be homogenous,
having equal capacity in terms of computation, communication and power. However, depending
on the application a node can be dedicated to a particular special function such as relaying,
sensing and aggregation since engaging the three functionalities at the same time on a node might
quickly drain the energy of that node. Some of the hierarchical protocols proposed in the
literature designate a cluster-head different from the normal sensors. While some networks have
picked cluster-heads from the deployed sensors [17-19] in other applications a cluster-head is
more powerful than the sensor nodes in terms of energy, bandwidth and memory [13, 18]. In such
cases, the burden of transmission to the sink and aggregation is handled by the cluster-head. Since
sensor nodes might generate significant redundant data, similar packets from multiple nodes can

be aggregated so that the number of transmissions would be reduced. Data aggregation is the
combination of data from different sources by using functions such as suppression (eliminating
duplicates), min, max and average [24]. Some of these functions can be performed either partially
or fully in each sensor node, by allowing sensor nodes to conduct in-network data reduction [22,
25-26]. Recognizing that computation would be less energy consuming than communication [17],
substantial energy savings can be obtained through data aggregation. This technique has been
used to achieve energy efficiency and traffic optimization in a number of routing protocols [22,
24-28]. In some network architectures, all aggregation functions are assigned to more powerful
and specialized nodes [13]. Data aggregation is also feasible through signal processing
techniques. In that case, it is referred as data fusion where a node is capable of producing a more
accurate signal by reducing the noise and using some techniques such as beam forming to
combine the signals [17]. The growing interest in wireless sensor networks and the continual
emergence of new architectural techniques inspired some previous efforts for surveying the
characteristics, applications and communication protocols for such a technical area[15,
23].Taxonomy of the different architectural attributes of sensor networks is developed using
different models [15]. Flooding and gossiping [29] are two classical mechanisms to relay data in
sensor networks without the need for any routing algorithms and topology maintenance. Although
flooding is very easy to implement, it has several drawbacks [24]. Such drawbacks include
implosion caused by duplicated messages sent to same node, overlap when two nodes sensing the
same region send similar packets to the same neighbour and resource blindness by consuming
large amount of energy without consideration for the energy constraints [29]. Gossiping avoids
the problem of implosion by just selecting a random node to send the packet rather than
broadcasting. However, this cause delays in propagation of data through the nodes. A growing
body of literature suggests fundamental problems of topological design in computer technology
[30-34]. These problems have been resolved in form of classes of Pascal graph with extreme
topological parameters that correspond to the parameters of speed of information transmission,
productivity, and security with limitations in costs [35]. In the phase of topological design, the
computer network is represented in graph [36-37] form whose vertices correspond to the nodes of
information processing, and edges correspond to the communication lines. The ontogeny of
Pascal Graph (PG) was Pascal Matrix (PM) that in turn was generated meticulously from Pascal’s
triangle [37]. Scientists have been putting a lot of efforts in ameliorating computer network
properties [38]. Wide varieties of graph model worked as resource to their brainstorming
contribution in this field [39]. Pascal Graph (PG) [37] is one of those resources and played a
significant role as soon as its exploration was initiated almost two decades back. As a
consequence, several similar graph models emerged in this area with laudable potentials to be
used as computer network topologies [37]. For reliability or security considerations, some
networks may be required to provide more than one distinct path for each node pair, there by
resulting in a minimum degree of connectivity between the nodes [39].The maximum
connectivity is desirable since it corresponds to not only the maximum fault tolerance of the
network but also the maximum number of internally disjoint paths between any two distinct
vertices. However, connectivity number can be at most equal to the degree of the network graph
[40-42]. Routing in WSNs is a very challenging problem due to the inherent characteristics which
differentiate such networks from other wireless networks such as ad hoc networks and cellular
networks [11-12]. In recent years, many algorithms have been proposed for the routing issue in
WSNs. The minimum energy routing problem has been addressed in the literature review of
several scholarly articles [11-12, 43]. The routing protocols that minimize the used energy,
extending subsequently the life span of the WSN [7]. The minimum total energy routing
approaches in these papers are to minimize the total consumed energy. However, if all traffic is
routed through the minimum energy path to the destination, the nodes along that path will run out
of batteries quickly rending other nodes useless due to the network partition even if they do have
available energy. Instead of trying to minimize the total consumed energy on the path, the
objective is to maintain the connected network as long as possible. If sensor nodes consume

energy more equitably, they continue to provide connectivity for longer, and the network lifetime
increases [11-13].
3. METHODOLOGY
The proposed methodology involved in this study entails following 2 phases:
3.1. Phase I - Deep investigation
For achieving reliable results and excellent efficiency of the problem solution, further in depth
literature review has been done. The literature review is done in section 2.
3.2. Phase II - Designing & Development
In this phase, solution of the identified problem has been developed for onward analysis,
efficiency and reliability measures during modelling and simulation. The proposed solution has
been reconsidered based on the analysis of the results of simulations for further improvement of
the solution. Section 4 illustrates the design principles and discussed in section 5.
4. DESIGNING WITH PASCAL GRAPH
4.1. Pascal Graph and method for designing topology
An undirected graph of n vertices corresponding to PM (n) as an adjacency matrix is called Pascal
Graph (n), where n is the order of the Pascal graph [37]. The following graph has been drawn
from the Pascal Matrix. The following are the example of Pascal Graph PG (3) and PG (5)
derived from Pascal Matrix PM (3) and PM (5) respectively. An (n x n) symmetric binary matrix
is called the Pascal Matrix PM(n) of order n if its main diagonal entries are all 0’s and its lower
(and therefore the upper also) consists of the first (n-1) rows of Pascal Triangle modulo 2. Where
PMi,j denotes the element of ith
row and jth
column of the Pascal Matrix [37]. The topology design
depends on the properties of Pascal Graph described in the section 4.2. We carry out the usual
text data transformation that consists in extracting graphs of valued associations between
attributes from collections of separate documents. Let us formally review this process that
transforms individual data into relational data to which we can refer as network data. The Pascal
Graph Topology (PGT) can provide a reliable node and shortest path with minimum of one hop
count and maximum of two hops using the first node. The design paradigm is such that the first
node behaves as the most dependable node but the failure of such node also creates problems in
the topology.

The following example shows the Pascal Matrix of PM (3) and PM (5) and the corresponding
Pascal Graph in figure 1.
Figure 1. Pascal Graph with 3 and 5 nodes
Suppose the graph G has n vertices and m edges and the graph G = (V;E) denote the ad-hoc
network before running the topology control algorithm, with V being the set of ad-hoc nodes,
and E representing the set of communication links. The n×m incidence matrix of G, denoted by
IG has one row per vertex and one column per edge. The column corresponding to edge {i, j} of
IG is zero except for the i-th and j-th entries, which are (Eij)0.5
and − (Eij)0.5
respectively, where Eij
is the corresponding edge weight. Note that there is some ambiguity in this definition, since the
positions of the positive and negative entries seem arbitrary. However this ambiguity will not be
important to us. The graph representation offers the advantage that it allows for a much more
expressive document encoding than the more standard bag of words/phrases approach, and
consequently gives improved classification accuracy. Document sets are represented as graph sets
to which a weighted graph mining algorithm is applied to extract frequent sub-graphs, which are
then further processed to produce feature vectors (one per document) for classification. Weighted
sub-graph mining is used to ensure classification effectiveness and computational efficiency; only
the most significant sub-graphs are extracted. The approach is validated and evaluated using
several popular classification algorithms together with a real world textual data set. The results
demonstrate that the approach can outperform existing text classification algorithms on some
dataset. When the size of dataset increased, further processing on extracted frequent features is
essential. In a weighted graph G = (V;E) every edge (u; v) is attributed a weight ωuv. When
referring to a weighted graph we assume that the weights are symmetric: ωuv = ωvu. The nodes of
a Euclidean graph are assumed to be located in a Euclidean plane. Furthermore the edge weight of
an edge (u; v) is defined to be ωuv = |uv|, where |uv| is the Euclidean distance between the nodes u
and v. Note that the definition of Euclidean graphs does not contain a statement on the existence
of certain edges. There is a link (u; v) in E if and only if the two nodes u and v can communicate
directly. Running the topology control algorithm will yield a sparse sub-graph Gtc = (V;Etc) of G,
where Etc is the set of remaining links. The resulting topology Gtc should be symmetric, that is,
node u is a neighbour of node v if and only if node v is a neighbour of node u. Although a
minimum spanning tree (MST) is a sparse connected sub-graph, it is often not considered a good
topology, since close-by nodes in the original graph G might end up being far away in Gtc (G
being a ring, for instance). For any two nodes u and v, if the optimal path between u and v in G
has cost c, then the optimal path between u and v in Gtc has cost f(c). If f(c) is bounded from
above by a linear function in c, the graph Gtc is called a spanner. Researchers have studied a
selection of cost metrics, the most popular being i) Euclidean distance and ii) various energy

metrics. The cost of a link in model i) is the Euclidean distance of the link, in model ii) the
distance is raised to a predefined power. The remaining graph Gtc should be sparse, that is, the
number of links should be in the order of the number of nodes. It follows that from the
similarity analysis point of view, the intersection graph GO(K,E) of H defines the co-word
graph having as many edges as there are non-null values in a similarity matrix.
4.2. Properties of Pascal Graph and generation method
There are certain pragmatic properties that make Pascal graph a better choice for a computer
network topology over many others [40]. Some of those properties are given below:
PG(n) is a sub-graph of PG(n+1) ∀ n ≥ 1 [37].
All Pascal Graph PG(i) for i ≤ 1 ≤ 7 are planner; all Pascal Graph of higher order are
non-planner [44].
Ιn the Pascal Graph, Vertex V1 is adjacent to all other vertices and Vertex Vi is adjacent
to Vi+1 for i ≥ 1 [40].
In the Pascal Graph, two even numbered vertices are never connected and adjacent to
each other.
More than one node except V1 in Pascal Graph has special properties in terms of
connectivity.
If k=2n +1, n is a positive integer and then Vk is adjacent to all Vi [44].
All Pascal Graph of order ≥ 3 are 2-connected [37].
There are at least two edge disjoint path of length ≤ 2 between any two distinct vertices
in PG(n), 3 ≤ n [44].
If Vi is adjacent to Vj, where j is even and |i-j|>1, then i is odd and Vi is adjacent to Vj-1
[44].
Let det(PM(n)) refer to the determinant of the Pascal matrix of order n. Then,
Det(PM(n)) is even, for all n ≥ 3 and det(PM(n)) = 0, for all even n ≥ 4 [44].
Ιn the Pascal Graph, V1 is considered to be most dependable and reliable node, the
properties of Vk have few similar properties of V1 for k=2n +1 where n is a positive
integer.
The following steps will generate a Pascal Graph:
Step 1: Enter p number of vertices to form a graph G = (V;E).
Step 2: Initialized LT[p,0] = 1;
Step 3: From the lower left triangle [LT(p,p)] by adding the number directly above and to the left
with the number directly above and to the right to find the new value. If either the number to the
right or left is not present, substitute a zero to its place [44].
Step 4: From the upper right triangle [UT(p,p)] using the same manner the generic Pascal Matrix
is formed.
Step 5: Convert the lower left and upper right triangle into binary values by using modulo
approach.
Step 6: From the final adjacency matrix [PM(p,p)] of with LT(p,p) and UT(p,p).
Step 7: Stop.
If we consider a Pascal graph containing n nodes i.e. PG(n) then by default vi ( i ≥ 1) is adjacent
to all other nodes [37]. The exploration does not conclude with it as the nodes have different
properties which need to be explored further. We have found and recommended other node
similar to V1 in the PG(n) and termed it as Dependable Node of Pascal Graph (DNP); the special
node, other than node V1, of Pascal graph PG(n) with same degree like v1 [44]. The node V1 is

called most dependable and reliable node in terms of routing and path optimization in the graph.
To satiate our claim and to rationalize the whole thing we have used very simplistic approach [40,
41].
The explanation with some suitable example is given below.
Case 1: m = 2(log m)
Case 2: m π 2[log m]
Case N: m = 2[log m]+1
To establish the new property, we are supposed to generate the index i of DNP.
For Case 1: i = 2(log m) − 1 +1, where i φ 1
For Case 2: i = 2[log m] +1 where i φ 1
For Case N: i = 2[log m] − 1 +1 and i = 2[log m] +1, where i φ 1
The formation of Pascal graph depends on DNP and its properties. The graph has mesh network
but minimum number of connectivity as per requirements. There are atleast two paths available
between nodes and V1 can provide connectivity with minimum hop count between any nodes. The
similar property can be found with the nodes have same functionality with V1 as per the formation
of graph.
The design of Pascal Graph from Pascal Matrix showed the existence of Dependable nodes of
Pascal Graph (DNP) along with V1 where there is a full connectivity with other nodes. The graphs
are not planner in the case of PG(n) with N>7 but the connectivity is not as complex as we have
more than two dependable nodes in some cases. The Table 1 gives the list of DNP in case of
Pascal Graph where n > 7.
Table 1. List of DNP
PG(n) i DNP Degree
8 5 V5 7
9 5, 9 V5, V9 8
10 9 V9 9
11 9 V9 10
12 9 V9 11
13 9 V9 12
14 9 V9 13
15 9 V9 14
16 9 V9 15
5. DISCUSSION
There are many other studies related to design using graph. Geibel and Wysotzki proposed a
method to derive induced sub-graphs of graph data and to use the induced sub-graphs as attributes
on decision tree approaches [45-46]. Their method can be used to find frequent induced sub-
graphs in the set of graph data. However, the upper limit number of the vertices to be included in
the sub-graph must be initially specified to avoid the exponential explosion of the computational
time, and thus the search is not complete. Liquiere and Sallantin proposed a method to completely
search homomorphically equivalent sub-graphs which are the least general over a given set of

graphs and do not include any identical triplet of the labels of two vertices and the edge direction
between the vertices within each sub-graph [47].
Figure 2. Mesh-Pascal(a,b,n)-Graph
From the Figure 2, we can find that it is forming a well-connected graph compared to other
graphs. Pascal graph is best among them in terms of diameter but it is worse in terms of
maximum degree of a node and total number of edges. On the other hand, ring and mesh perform
well in terms of maximum degree of a node and total number of edges, but they both perform
poorly in terms of diameter of graph. The proposed graph gives a standard measurement in terms
of degree, diameter and total number of edges. The only graph which comes close to it is
hypercube graph. But the hypergraph fails measurably compared to the proposed graph model
when we consider total number of edges, reliability of the network and expansion-contraction of
the network. If we expand the hypercube from the dimension q to q+1, it would reconfigure 2q
number of nodes (which increases exponentially). But the proposed model improved by adding
nodes to PGN (Pascal Graph Network), so we have to reconfigure a few member of nodes to get a
better network as in Mesh-Pascal Graph(ab,n), multiple path exists between two nodes and the
network can be more reliable and fault tolerant for that reason.
The proposed model is good enough with comparison to other WSN network model since it gives
minimal degree and diameter but maximum reliability and expansion. The entire simulation tested
by using a very well-known simulator NS2 where different Pascal Graph topology has been
designed. The three topologies shown in figures 3,4,5 below; where we have tested with 2 nodes,
3 nodes and 4 nodes for the testing of Pascal Graph property. We have also tested with more
number of nodes as per the topology shown in Figure 1. The different network protocols has been
designed and implemented in NS2 simulator. NS2 includes a tool for viewing the simulation
results, called NAM; NAM is an animation tool for viewing network simulation traces and real
world packet data. The first step to use NAM is to produce trace file containing topology
information for nodes and links which generated by NS Tcl/Tk script. During the simulation, we
have designed topology configuration and layout information for Pascal Graph, tracing events
using NS and visualize with NAM. Based on the simulation various graphs has been plotted to
demonstrate the performance of Pascal Graph. We have tried to show the results of the simulated
routing and sensor node energy. Using the same topology we have compared Pascal graph
network with existing WSN network model using NS2. The graph shows the performance of
PGN and the routing is better in comparison with other WSN network since the first node is
effective for minimum cost routing in terms of different factors of energy efficient node
optimized routing mechanism. PGN always tries to find shortest path using first node which is
also called most dependable node. This is one of the major drawbacks of PGN. So the energy

consumption by the nodes which has higher connectivity is more and they are dependable nodes
as well. So we considered the energy efficiency of dependable nodes and the performance also
being demonstrated in time frame and it is one of the major issues in PGN that the energy
consumption is higher in case of DNP and this can be one of the major factors of routing as well.
The following figures illustrate the PGN and performance of the network designed in NS2
simulator.
Figure 3. Pascal Graph with two nodes
Figure 4. Pascal Graph with three nodes

Figure 5. Pascal Graph with four nodes
Figure 6. The bandwidth cost for data
Figure 7. The number of route discovery

Figure 8. WSN node life and energy drain with respect to time after increasing hops
6. CONCLUSION AND FUTURE SCOPE OF STUDY
In this paper we have proposed new WSN routing algorithm by using Pascal Graph. Many new
routing and MAC layer protocols and different techniques have been proposed for WSN network
and most of them trying to resolve the resource constrained for unattended wireless sensor
environment, The majority of all the protocols mainly Concentrate on energy efficiency of sensor
nodes, however sensor application have very important role specially in critical applications like
the defence and health where the accuracy and guaranteed data transfer timely is an important
issue. In the same way with some more specific sensor applications where the data type is mainly
image and movie is supposed to be transfer, we required more accuracy and guarantee for timely
data transfer. Hence transfer of data in such cases mainly requires QoS aware routing network
management in order to ensure efficient usage of the sensor nodes. We already start working on a
proposed and approved project from Deanship of scientific research, KFU, related to the QoS of
WSN as it has a very key role for enhancing WSN efficiency and even its life, with that project
we will focus on operational and architectural challenges of handling QoS routing traffic in
sensor network and will propose a new mechanism for QoS based routing protocol to further
enhance WSN life. On other hand, as we know that wireless sensor network (WSN) becomes
more attractive area for the researchers now day, due to the wide range of its application areas
and due to the distributed and deployment nature of these networks in remote areas, these
networks are vulnerable to numerous security threats that can adversely affect their proper
functioning. This problem is more critical if the network is deployed for some mission-critical
applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-
life deployment scenarios.
ACKNOWLEDGEMENTS
Authors would like to thank their colleagues and students of Sir Padampat Singhania
University for their valuable help and support.

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AUTHORS
Ms. Deepali Panwar is currently a M.Tech student in the department of Computer Science
and Engineering of Sir Padampat Singhania University. She has received B.Tech with
honours degree from Rajasthan Technical University in the year 2010. She has teaching
experience of one year from Govt. polytechnic college, kota. Her research interest lies in
Wireless Sensor Networks.
Mr. Subhrendu Guha Neogi is currently working as Assistant Professor in the depa
rtment of Computer Science and Engineering of Sir Padampat Singhania University. He
has been awarded Masters in Engineering from West Bengal University of Technology.
He has more than 10 years of experience in teaching. He served many reputed
educational Institutes in India in various positions.

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