Implementation and analysis of multiple criteria decision routing algorithm for wireless sensor network

International Journal of Electronics and Communication Engineering Research and Development
(IJECERD), ISSN 2248-9525(Print), ISSN- 2248-9533 (Online) Volume 4, Number 2, April-June (2014)
28
IMPLEMENTATION AND ANALYSIS OF MULTIPLE CRITERIA
DECISION ROUTING ALGORITHM FOR WIRELESS SENSOR
NETWORK
[1]
Shashank S. Bhagwat, [2]
Mrs. Triveni C.L, [3]
P C Srikanth
[1]
Department of ECE, Malnad College of Engineering Hassan, India
[2]
Department of ECE, Assistant Professor, Malnad College of Engineering, Hassan, India
[3]
Professor & Head, Department of ECE, Malnad College of Engineering, Hassan, India
ABSTRACT
Energy consumption is the key design criterion for routing data in wireless sensor
network. However, some applications of wireless sensor network like disaster management,
battlefield control etc. demand fast data delivery. In this paper, a data forwarding technique is
proposed and its performance is compared with flooding algorithm. Here, the source nodes or
intermediate nodes select a next node to forward the data to the destination based on different
criteria. The process repeats until data reach the destination. In Multiple Criteria Decision
Routing algorithm next node to forward the data is selected based on both its distance to the
sink node and the remaining energy of the node. A comparative study of the performance of
these techniques has been carried out and results are presented in this paper. In the simulation
results show that Multiple Criteria Decision Routing algorithm performance is better than
flooding algorithm.
Keywords: Multiple Criteria Decision Routing (MCDR), Wireless Sensor Networks (WSN).
I. INTRODUCTION
With the advancement of wireless communication technologies, Wireless Sensor
Networks (WSNs) have become useful for many applications. In particular, the monitoring
applications covering large geographical areas are much benefited by the use of WSNs.
However, in large-scale WSNs, data may need to follow a long multi-hop communication path
and each intermediate node has to receive and transmit the packets. The residual energy of the
intermediate nodes reduces due to such communication activities. Moreover, overall energy
IJECERD
© PRJ
PUBLICATION
International Journal of Electronics and Communication
Engineering Research and Development
(IJECERD)
ISSN 2248– 9525 (Print)
ISSN 2248 –9533 (Online)
Volume 4, Number 2, April- June (2014), pp. 28-35
© PRJ Publication, http://www.prjpublication.com/IJECERD.asp

29
consumption for packet routing depends on the number of hops in the path. Therefore, it is
important that the network must be designed in such a way that the average number of hops for
routing the data from source to sink remains as minimum as possible.
For many applications, timeliness of data delivery to the sink is also one of the essential
requirements. An example of such applications is Disaster management. Although data
transmission is fast when flooding routing protocol is used, energy consumption due to this
protocol is high.
In this paper a new routing algorithm for fast and energy efficient data delivery across
WSN. In the MCDR routing algorithm the next node to forward the data is selected based on its
remaining energy and its distance to the sink node. This technique ensures fast and energy
efficient data delivery. The performance of MCDR algorithm is compared with flooding
algorithm.
II. RELATED WORK
Extensive research work has been carried out on data routing techniques. In [1], the
authors proposed Multiple Sink Dynamic Destination Geographic Routing (MSDDGR). It is
based on greedy forwarding scheme. When a packet needs to be sent, the sender selects the
nearest sink as the current destination. Also if the intermediate node sees another sink is nearer
to it, then the current destination node is changed and the new sink node is selected as the
destination.
The authors in [2] address the problem of efficient routing the data from multiple
sources to multiple sinks. The authors first define a mathematical model to derive an optimal
solution. In the second phase, it is assumed that the initial state of the system is such that a tree
exists for each sink connecting it to all the relevant sources. The path from source to sink is
changed periodically adapting the different sink-rooted trees. The adaptation consists of
selecting a different neighbor as the parent towards a given sink.
In [3], the authors propose a protocol called MRMS (Multipath Routing in large scale
sensor networks with Multiple Sink nodes) which incorporates multiple sink nodes. In MRMS,
a primary path is created with minimum path cost. It also saves the other paths from different
sinks. Thus, when the primary path is not reachable or if the residual energy of the sensors
along the path falls below a certain threshold, another path is selected.
A scalable multipath routing approach called Neighbor Sink Nexus (NSN) routing
algorithm is presented in [4]. It is based on Set Cover problem. This paper claims that set cover
method gives balanced and better performance in terms of energy efficiency and reduced
latency for multiple sink WSNs.
Malakooti et al. in [5] propose a Distributed Composite Multiple Criteria Routing
approach. In this work two mechanisms, such as global multiple criteria routing and distributed
multiple criteria routing are discussed. The multiple criteria like energy, delay and bit error rate
are applied to the Distance Vector routing protocol to find the best possible path from any
source to destination. Li et al. in [6] propose a data-centric multi-criteria routing algorithm
called MCR. Here criteria like energy remaining at the sensor nodes, power consumption
model, and group membership of each node are considered. Tabatabaei in [7] also uses the
multiple criteria approach for mobile ad hoc networks with AODV routing protocol.

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III. MUTIPLE CRITERIA DECISION ROUTING ALGORITHM
In this section MCDR data routing algorithm designing is proposed. Before proceeding
into the algorithm some assumptions common for all the three mentioned routing techniques
are made. The assumptions are as follows.
i. All the nodes in the network should know their position in that partition (X and Y
coordinates).
ii. All the nodes in the network should also know the position of the sink node in that
network.
The working of the MCDR algorithm is as follows.
a. mcdr algorithm has two criteria, energy and distance to the sink.
b. The Transmission Range For Each The Nodes In The Network Must Be Given. All The
Nodes Are Present In The Transmission Range Of Source Node Is Determined source
node broadcasts a request packet to all the nodes that are present in the transmission
range. request packet includes the source id and sink node id and euclidian distance of
the source node to the sink node. request packet structure shown in fig1.
Fig 1: Request Packet Structure
c. On receiving the request packet the nodes calculate their Euclidian distance to the sink
node. Since every node in the partition know the position of the sink node and its
position Euclidian distance can be calculated using the formula.
(1)
where x1 = x-coordinate of the sink node.
y1 = y-coordinate of the sink node.
x1 = x-coordinate of the sink node.
y1 = y-coordinate of the sink node.
d. Every node transmits the reply packet to the source node. The reply packet includes the
id of the node, its distance with the sink node and the remaining energy of the node.
Reply packet is shown in fig 2.
Fig 2: Reply Packet Structure
e. The utility factor is calculated for each node using the distance and the remaining
energy in the reply packet.
source
id
destination
id
dist
source id node id destination id new_dist rem_energy

31
f. To calculate the utility factor weights are assigned to each alternative. If an alternative
has more importance then it will be assigned with higher weightage. In the algorithm
equal weight of 0.5 is assigned to both the alternatives.
g. The distance value and the remaining energy must be normalized between 1 and 0. The
normalization procedure is as follows. For energy the normalization is done using the
formula
(2)
Normalization of the distance is done using the formula
(3)
a. The Utility Factor is calculated according to the equation
where n = number of alternatives i.e., total number of nodes that comes under the transmission
range of the source node.
m = number of criteria.
ZK(Oi) = normalized value of the criteria.
WK = weights assigned to the criteria.
b. Utility factor is calculated for each node in the transmission range of the source node.
The source node or the intermediate node selects the next node to transmit the data
with maximum utility factor.
c. The above procedure is continued until the destination (sink) node is reached.
IV. FLOODING
In flooding [6], the source node floods all events to every node in the network.
Whenever a sensor receives a data message, it keeps a copy of the message and forwards the
message to every one of its neighboring sensors and the cycle repeats. It is an
easy-to-implement routing scheme, and it is suitable for various network types, node
distributions and environments. The main advantage of flooding is the increased reliability
provided by this routing method. Since the message will be sent to at least once to every host it
is almost guaranteed to reach its destination. But the unlimited broadcasting the packets in the
flooding scheme will cause the broadcast storm.
V. SIMULATION
In the simulation 100 nodes are distributed over 100*100 area in random manner.
Transmission range is 40 units. The simulation parameter are listed in table 1.

32
Table 1: Simulation Parameters
Parameter Name Parameter Value
No of Nodes 100
Environment Factor 0.5
Range 40 units
Energy Amplicification 0.5mJ
Energy Transmission 1mJ
Attenuation Factor 0.02
Figure 3 shows the routing in flooding algorithm. It is clear that flooding takes multiple
routes for data transmission and hence consumes more energy.
Figure 3: Routing using Flooding algorithm
Figure 4 shows the data rouintg in MCDR algorithm. Compared to figure 3 and figure 4
it is clear that MCDR performes better than flooding. Since MCDR routes the data to one of the
neighbor nodes unlike flooding, this consumes lesser energy and its faster.
Figure 4: Routing using MCDR algorithm
Data routing will be faster if number of hops for data transmission is lesser and in the
simulation it is shown that MCDR takes lesser number of hops than flooding and is shown in
the figure 5.

33
Figure 5: Number of hops for Flooding and MCDR
The performance of of the routing algorithm is better if it consumes the lesser energy. In
the simulation it is clear that MCDR consumes lesser energy than the flooding and is shown in
figure 6.
Figure 6: Energy Consumed of FLOOD and MCDR
In the simulation it is clear that MCDR takes lesser time to route the data than the
flooding algorithm. Hence MCDR is faster compared to the flooding algorithm.
Figure 7: Time Taken by Flooding and MCDR
Power consumption of MCDR algorithm is also lesser compared to flooding. Power
consumption of MCDR and flooding for 25 iterations is shown in figure 8.

34
Figure 8: Power Consumption of Flooding and MCDR
The lifetime of WSN will be more if the routing algorithm manages to keep more nodes
alive. In the figure 9 it is shown that MCDR manages to keep many nodes alive hence makes
the network more reliable.
Figure 9: Number of alive nodes using Flooding and MCDR
VI. CONCLUSION AND FUTURE SCOPE
It has been demonstrated that the proposed MCDR algorithm shows better
performances over flooding routing protocol and using multiple criteria to choose the next node
is the best choice in terms of increase in the life time of the network.
Future work will focus on including additional criteria, fine-tuning of the weight values
etc. The route hole problem will also be handled in future.
REFERENCES
[1] B. Elbhiri, S.EI. Fkihi, R. Saadane, D. Aboutajdine, Clustering in Wireless Sensor
Networks based on Near Optimal Bi-partitions, in: Procs. of the Next Generation
Internet (NGI), 2010.
[2] B. Malakooti, I. Thomas, A Distributed Composite Multiple Criteria Routing Using
Distance Vector, in: Procs. of the IEEE International Conference on Networking,
Sensing and Control, Ft. Lauderdale, FL, 2006.

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[3] D. Das, Z. Rehena, S. Roy, N. Mukherjee, Multiple-Sink Placement Strategies in
Wireless Sensor Network, in: Procs. of the Fifth International conference on
Communication Systems and Networks (COMSNETS 2013), Bangalore, India, 2013.
[4] E.I. Oyman, C. Ersoy, Multiple Sink Network Design Problem in Large Scale Wireless
Sensor Networks, in: Procs. of the International Conference on Communications (ICC),
Paris, France, 2004.
[5] I. Slama, B. Jouaber, D. Zeghlache, Energy Efficient Scheme for Large Scale Wireless
Sensor Networks with Multiple Sinks, in: Procs. of the IEEE Wireless Communications
and Networking Conference, WCNC, Las Vegas, USA, 2008.
[6] K.N. Chaaran, M. Younus, M.Y. Javed, NSN based Multi-Sink Minimum Delay Energy
Efficient Routing in Wireless Sensor Networks, European Journal of Scientific
Research, 41, 2010, 399-411.
[7] L. Cao, C. Xu, W. Shao, Multiple Sink Dynamic Destination Geographic Routing in
Wireless Sensor Networks, in: Procs. of the International Conference on Cyber-Enabled
Distributed Computing and Knowledge Discovery (CyberC), Huangshan, 2010.
[8] P. Ciciriello, L. Mottola, G.P. Picco, Efficient Routing from Multiple Sources to
Multiple Sinks in Wireless Sensor Networks, in: Procs. of the 4th European Conference
on Wireless Sensor Networks (EWSN), 2007.
[9] Q. Li, J. Beaver, A. Amer, P.K. Chrysanthis, Multi-Criteria Routing in Wireless
Sensor-Based Pervasive Environments, Journal of Pervasive Computing and
Communication, 1 (2005).
[10] S. Tabatabaei, Multiple Criteria Routing Algorithms to Increase Durability Path in
Mobile Ad hoc Networks, in: Procs. of the 4th
International Conference for Internet
Technology and Secured Transactions, ICITST, London, UK, 2009.
[11] W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy- Efficient
Communication Protocol for Wireless Micro Sensor Networks, in: Procs. of the 33rd
Hawaii International Conference on System Sciences, Hawaii, 2000.

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Implementation and analysis of multiple criteria decision routing algorithm for wireless sensor network