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This paper evaluates and ranks the suitability of routing algorithms for bipartite wireless sensor network topology. The network considered in this paper, consists of an irregular combination of fixed and mobile nodes, which leads to construction of a bipartite graph among them. A wireless sensor network is usually constrained by the energy limitations and processing capabilities. We therefore, consider the important metrics for analysis namely, carried load, energy consumption and the average delay incurred. We present the possibilities of employing the routing algorithms subject to the quality of service required by the wireless sensor networks applications
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International Journal of Electrical and Computer Engineering (IJECE)
Vol. 5, No. 6, December 2015, pp. 1417~1423
ISSN: 2088-8708 1417
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Performance Comparison of Routing Protocols in Bipartite
Wireless Sensor Network
Devashish Gosain, Itu Snigdh
Department of Computer Science, Birla Institute of Technology, Ranchi, India
Article Info ABSTRACT
Article history:
Received Apr 12, 2015
Revised Jul 20, 2015
Accepted Aug 16, 2015
This paper evaluates and ranks the suitability of routing algorithms for
bipartite wireless sensor network topology. The network considered in this
paper, consists of an irregular combination of fixed and mobile nodes, which
leads to construction of a bipartite graph among them. A wireless sensor
network is usually constrained by the energy limitations and processing
capabilities. We therefore, consider the important metrics for analysis
namely, carried load, energy consumption and the average delay incurred.
We present the possibilities of employing the routing algorithms subject to
the quality of service required by the wireless sensor networks applications.
Keyword:
Average Delay
Bipartite
Carried Load
Energy Consumed
Copyright © 2015 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Devashish Gosain,
Department of Computer Science,
Birla Institute of Technology,
Ranchi, India
Email: gosain.devashish@gmail.com
1. INTRODUCTION
WSN’s are now all pervasive in world. From home automation system to critical boiler monitoring,
from X-Box to military surveillance, they are ubiquitous. Recently, considerable amounts of research efforts
have enabled the actual implementation and placement of sensor networks tailored to the unique
requirements of certain sensing and monitoring applications [1].
An established well-functioning sensor network has a foundation, built on, two factors, a good
communication protocol and a robust network topology. Extensive review of existing communication
protocols can be found in [2], [3] and for Sensor placement (or network topology) [4] can be referred.
Objective of any sensor topology is to increase the coverage with minimizing the cost. To increase
the coverage various schemes have been proposed, which includes bifurcated network of fixed and mobile
nodes, like grid deployment method [5], where the region is divided into grids. Number of deployed static
nodes, obstacles and boundaries collectively decides the weight of each grid. The grid with least weight is the
destination of a mobile sensor. In [6] Voronoi polygon is exploited to find the number of coverage holes
(with their positions) using a coverage enhancing algorithm. Similar to this, in [7] authors propose an
algorithm which uses Delaunay triangulation to determine holes with the help of mobile and static sensor
nodes. Since variegated network of such kind has many practical applications; we focus on the topology
which is partially static and partially dynamic in our research. The wireless sensor networks that constitute
mobile and fixed sensors usually compromise between cost and coverage. Also, in order to achieve high
coverage, mobile sensors may require to move from dense areas to sparse areas. The background strength of
our analysis lies in incorporating bipartite graphs into such kind of topology. There has been a lot of research
by employing such graphs in target tracking applications. The network is incorporated as a set Si consist of i
ISSN: 2088-8708
IJECE Vol. 5, No. 6, December 2015 : 1417 – 1423
1418
sensors and set Tj consist of j targets; with the objective to assign sensors to targets optimally (maximizing
utility and/or minimizing cost), subject to the imposed constraints. Authors view this as a bipartite graph,
where an edge e(Si, Tj ) corresponds to a pairing of sensor Si with target Tj with the objective of assigning
sensors to targets optimally (maximizing utility and/or minimizing cost), subject to the imposed constraints
[8]-[10]. Our work is different from the previous work, because here we are partitioning sensors into two
disjoint sets of mobile and stationary sensors.
2. MOTIVATION
The motivation behind the idea is that, sometimes it becomes impossible to manually deploy sensors
in sites like rough mountain terrain, dense forests, cave, battlefields, and areas affected by poisonous gases.
The solution is scattering the sensors randomly, with obvious drawback of not having the desired placement
and coverage. In the recent times, researchers have encouraged on mixed sensor networks, in which the
stationary nodes and mobile nodes work in coherence to perform placement task. Such placements give more
coverage and robustness with reduced number of nodes.
Our article analyses this topology construction in context to a bipartite graph. Essentially a bipartite
graph, also called a bigraph, is a set of graph vertices decomposed into two disjoint sets such that no two
graph vertices within the same set are adjacent [11]. Our bipartite sensor network involves partitioning
sensors into two disjoint sets of mobile and stationary sensors. In this article, we analyze the performance of
bipartite network under three classes of routing algorithms, namely DSR (on demand) [12], OLSR (distance
vector based , static) [13], and FISHEYE (link state based) [14] on different kinds of bipartite sensor
networks that has not been addressed in previous literatures till date.
3. SYSTEM MODEL
We constructed a bipartite graph B(V, E), where V = S M, where S denotes the set of static nodes
and M denotes the set of mobile nodes. We have taken the distance as a cost metric in the construction of the
bipartite graph.
3.1. Assumptions
1) Sensors are location aware either obtained from Global Positioning System (GPS) or through
location discovery algorithms.
2) It is assumed that mobile sensors are Full Functional Device and Stationary Sensors are reduced
functional devices.
3) The mobile/dynamic sensors can easily move and can reach the desired location efficiently and
accurately using the mobility algorithm for dynamic sensors.
3.2. Mobility Algorithm
1) For each mobile node
2) For each static sensor node
3) Calculate Euclidian distance from itself
4) Update node table entry
5) End For
6) From node table select the node with min. distance
7) Move towards the node selected in step 5
8) Latch with node selected in step 5 and to all other static nodes which are in its communication
range
9) Delete node entries from its node table (of latched nodes from step 7)
10) Repeat step 6 to 9 until its node table gets empty
11) End For
12) Repeat steps 1 to 11 multiple times
3.3. Steps of Construction
1) Add all the movable nodes into set M.
2) Add all the stationary nodes into set S.
3) ∀ ∈and ∀ ∈ if mobile node m can reach static node s, (the distance between m and s is
less than maximum prescribed distance and the remaining energy of m is above a certain threshold ET) then
IJECE ISSN: 2088-8708
Performance Comparison of Routing Protocols in Bipartite Wireless Sensor Network (Devashish Gosain)
1419
add an edge e(m,s) into the bipartite graph; the weight of the edge e(m,s) is represented as w(m,s), denotes
the distance between mobile node m and fixed node s, otherwise, w(m,s) = NULL.
3.4. Model Generated
Figure 1. An initial deployment of B3,2 sensors in a
particular field Figure 2. Final deployment of B3,2 sensors
Figure 1 depicts two classes of sensors; static and mobile where, S1, S2 and S3 represent static
sensors (none of them is in range of each other, communication is not possible). M1 and M2 represent mobile
sensors. The dashed arrows represent the trajectories of M1 and M2 (mobile sensors) at a particular instance
of time. (Note: both the sensors are completely free to move around inside the periphery, these arrows
represent, onlyone particular possibility of their direction).
Figure 2 depicts the same network but aase when the mobile node moves in the communication
range of a particular static sensor node thereby initiating data transfer. We see that M1 has moved along his
trajectory and is in the transmitting range of both S1 and S2 (represented by solid arrows). Also, M2 has
moved along his trajectory and is in the transmitting range of S3 (represented by solid arrows).In this
scenario, S1, S2 and S3 are sensing their neighborhoods. M1 and M2 comes in proximity with Stationary
sensors and collect the data from them. Later on, they will be moving towards sink and will transfer the data
(sensed by themselves and collected from static sensors). Same process is iterated multiple times.
3.5. Snapshot of Actual Simulation of B3,2
Figure 3 shows that initially mobile sensors 4 and 5 are not in range of any static sensor 1, 2 or 3.
The red flags are random waypoints which indicate the next location of mobile sensors.
Figure 3. Initial deployment setup
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IJECE Vol. 5, No. 6, December 2015 : 1417 – 1423
1420
From Figure 4 we observe that finally, mobile nodes have travelled and now they are in range of
static nodes. Node 4 (mobile) is communicating with nodes 1 and 2 (static). Similarly Node 5 (mobile) is
communicating with node 3 (static)
Figure 4. After movement of mobile nodes
3.6. Notations Used
Assembly of S1, S2 and M1 is named as B2,1,acronym of Bipartite Graph consisting two set of nodes
static (S1 and S2) and mobile (M1).
1) Assembly of S3 and M2 is named as B1,1,acronym of Bipartite Graph consisting two set of
nodes static (S2) and mobile (M2).
2) Together these assemblies are known as B3,2,acronym of Bipartite Graph consisting two set of
nodes static (S1, S2 and S3) and mobile (M1 and M2).
3) In general, Bm,nrepresents a Bipartite graph of two set of nodes, one consisting of m fixed nodes
and other consisting of n mobile nodes.
4. SIMULATION RESULT
Construction of the various bipartite network scenarios is implemented in Qualnet under the set of
given experiments with following parameters.
1) Bm,1graphs which consist of m static nodes and only 1 moving node.
2) Bm,2 graphs which consist of m static nodes and 2 moving node.
3) Bm,3graphs which consist of m static nodes and only 3 moving node.
Table 1. Parameter Specifications
PARAMETERS VALUES
Area 1500 x 1500 m
Data Rate 2 Mbps
Radio Type 802.11b
Packet Reception Model PHY802.11b
Battery Model Residual Life Estimator
Energy Model Mica Motes
MAC Propagation Delay 1 s
Application Constant Bit Rate
The main interest lies in observing how the routing protocol performs with increasing number of
mobile nodes as they will require extra power source for motion.The three important parameters of WSN,
Carried Load, Energy and Delay have been observed for DSR, OLSR and FISHEYE. The graphs are
analysed for the results obtained after simulation.
IJECE ISSN: 2088-8708
Performance Comparison of Routing Protocols in Bipartite Wireless Sensor Network (Devashish Gosain)
1421
4.1. Carried Load
From Figure 5(a), 5(b) and 5(c it is evident that carried load in the network is minimum for DSR
followed by OLSR and FISHEYE. Performance of DSR can be attributed to its ad-hoc nature. It is an on-
demand protocol designed to minimize the bandwidth consumed by control packets in ad hoc wireless
networks by eliminating the periodic table-update messages required in the table-driven approach (like OLSR
and FISHEYE) [15].
B11 B21 B31 B41
0
200
400
600
800
1000
1200
1400
1600
1800
CARRIED LOAD (bits/sec)
NODES
DSR
OLSR
FISHEYE
B32 B42 B52 B62 B82
850
900
950
1000
1050
1100
1150
1200
1250
1300
1350
1400
1450
1500
1550
1600
1650
CARRIED LOAD (bits/sec)
NODES
DSR
OLSR
FISHEYE
Figure 5(a). Bm
,
1 graph Figure 5(b). Bm
,
2 graph
B63B73B83B93
950
1000
1050
1100
1150
1200
1250
1300
1350
1400
1450
1500
CARRIED LOAD (bits/sec)
NODES
DSR
OLSR
FISHEYE
Figure 5(c). Bm,3 graph
4.2. Energy Consumed
B11 B21 B31 B41
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
ENERGY (mWh)
NODES
DSR
OLSR
FISHEYE
B32 B42 B52 B62 B82
0.010
0.015
0.020
0.025
0.030
0.035
ENERGY (mWh)
NODES
DSR
OLSR
FISHEYE
Figure 6(a). Bm
,
1 graph Figure 6(b). Bm
,
2 graph
B63 B73 B83 B93
0.012
0.014
0.016
0.018
0.020
0.022
0.024
0.026
ENERGY (mWh)
NODES
DSR
OLSR
FISHEYE
Figure 6(c). Bm,3 graph
ISSN: 2088-8708
IJECE Vol. 5, No. 6, December 2015 : 1417 – 1423
1422
On observing Figure 6(a), 6(b) and 6(c) it is clear that DSR has minimum energy requirement
whereas FISHEYE has maximum. Carried load of a sensor node is directly proportional to its energy
consumed. From previous findings it is established that for DSR routing scheme carried load is minimum,
which implies energy consumption will also be minimum for the same. Since, FISHEYE and OLSR are
proactive protocols, significant amount of energy will be required in transmitting and receiving control
packets to maintain the link state routing table by each node, this leads to their high energy requirement.
4.3. Average Delay
Figures 7(a), 7(b) and 7(c) depicts that DSR routing scheme produces long delays in network. This
can be attributed to the fact that, in link state routing algorithms (OLSR and FISHEYE), before transmission
of data, neighbor tables of all the nodes are updated at the beginning only. Next hop selection takes trivial
amount of time. But, in DSR every time the data packet is received by the node, it is forwarded to the next
node, if a route to the destination is known by the present node. Else, route discovery mechanism is initiated
by that node. This consumes considerable amount of time. This factor can be attributed to DSR’s poor
performance withrespectto the average delay parameter.
B11 B21 B31 B41
0.0010
0.0015
0.0020
0.0025
0.0030
0.0035
0.0040
NODES
DSR
OLSR
FISHEYE
AVERAGE DELAY (seconds)
B32 B42 B52 B62 B82
0.0015
0.0020
0.0025
0.0030
0.0035
0.0040
0.0045
0.0050
0.0055
0.0060
0.0065
AVERAGE DELAY (seconds)
NODES
DSR
OLSR
FISHEYE
Figure 7(a). Bm
,
3 graph Figure 7(b). Bm
,
3 graph
B63 B73 B83 B93
0.0015
0.0020
0.0025
0.0030
0.0035
0.0040
0.0045
AVERAGE DELAY (seconds)
NODES
DSR
OLSR
FISHEYE
Figure 7(c). Bm,3 graph
Table 2. Rank Table
PERFORMANCE
PARAMETERS RANK 1 RANK 2 RANK 3
Carried Load (bits/sec) DSR OLSR FISHEYE
Energy Consumed (mWh) DSR OLSR FISHEYE
Average Delay (sec) FISHEYE OLSR DSR
5. CONCLUSION
Energy and delay are two paraphernalia’s for any WSN. If the bi-partite network is established in
difficult-to-access terrains, where human intervention is infeasible, to prolong the network lifetime, DSR is
the only option because of least energy requirement. However, in surveillance applications and disaster
management system, where delivery time is of foremost importance, FISHEYE/OLSR can be employed but
certainly DSR does not prove to be efficient. In situations where WSN is restricted to be operated on low
bandwidth DSR routing scheme is a better option and must be employed for enforcing least carried load
IJECE ISSN: 2088-8708
Performance Comparison of Routing Protocols in Bipartite Wireless Sensor Network (Devashish Gosain)
1423
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