Download Routing Protocols to Save and Balance Energy for Wireless Sensor

Routing Protocols to Save and Balance
Energy for Wireless Sensor Network using
Fuzzy Set
1
MADHAVA C, 2SAROJADEVI H.
Dept. of CSE, Nitte Meenakshi Institute of Technology, Bangalore
Abstract
Wireless sensor networks are used in different
application like civil and military. Many routing
protocols have been proposed to find suitable to
transmit data.
Most of energy aware routing
protocols reduce energy consumption and often
provide energy balancing. These protocols optimize
or decrease the energy consumption of the network.
Energy manager protocols will balance the energy
consumption of network. The lifetime of WSN
depends on the battery of sensor node. In this paper
we propose fuzzy protocol to balance energy and use
low energy to find the best route.
Key words: Routing protocol, Wireless sensor
network, energy aware, fuzzy set.
1.
Introduction
A wireless sensor network (WSN) is a wireless
network consisting of spatially distributed
autonomous devices using sensors to cooperatively
monitor physical or environmental conditions, such
as temperature, sound, vibration, pressure, motion or
pollutants, at different locations. The development of
wireless sensor networks was originally motivated by
military applications such as battlefield surveillance.
However, wireless sensor networks are now used in
many industrial and civilian application areas,
including industrial process monitoring and control,
machine health monitoring, environment and habitat
monitoring,
healthcare
applications,
home
automation, and traffic control. WSN has gained its
importance in recent years. It has become most
important technology throughout the world. Wireless
sensor network consists of many sensor nodes where
each sensor node is small, of low cost and of limited
power. The role of sensor node is to sense and gather
information from the environment. They are also
used for transmitting the sensed data to the user.
Development of WSN was motivated by military
application.
Sensor nodes in WSN are tiny
components that consist of sensor, processors,
memory, and actuator. Major factor in sensor
network is energy consumption. Since each sensor
node has tiny batteries so it has limited processing
power, so services will be limited. This is major
issue in sensor network because every sensor node is
used for routing and forwarding the data. The
proposed fuzzy logic based routing protocol tries to
balance and save energy in network.
2.
Related works
In WSN there are many routing protocols which are
used to route the information among different sensor
nodes. These protocols use route information in
more energy efficient manner to increase the life time
of sensor nodes.
Low energy adaptive clustering hierarchy is a well
known hierarchical protocol. The network in LEACH
protocol is used to divide the network into different
clusters and choose cluster head among them. Since
LEACH is not used for large deployed network
because data is transmitted directly to cluster head.
Another cluster head election mechanism CHEF
using fuzzy logic is proposed that uses fuzzy variable
to find energy and local variable. Cluster heads are
more evenly distributed over the network in CHEF
than LEACH, so CHEF increases the network
lifetime. Energy aware routing protocol based fuzzy
logic is tuneable and soft algorithm. This cluster head
algorithm is considered more powerful when
compared to some other sensor node and has no
energy limitation. The fuzzy cluster algorithm uses
node’s residual energy to improve the lifetime of
wireless sensor network which distributes clusters
uniformly over network.
S. M. Abolhasani et.al. [2] came up with “A Learning
Automata Based Energy-aware Routing Protocol for
Sensor Networks” which uses learning automata to
find the paths in terms of balancing network traffic
load. This method performs well in terms of
balanced energy consumption of nodes and
consequently, lengthening network lifetime.
G. P. Hancke et.al. [3] proposed a “A Simple Energy
Efficient Protocol for Wireless Sensor Networks”
protocol to optimize network lifetime. SEER uses
flat network structure along with event-driven
reporting to reduce the number of message
transmissions. Routing is based on the distance from
the base station as well as remaining energy of
battery of the nodes path towards the base station.
SEER protocol minimizes the number of messages
that are sent through the network and thus reduces the
overall energy consumption.
E. Ahvar et.al. [4], introduced a “Balanced EnergyAware Routing Protocol for Wireless Sensor
Network”, which is an improvement of SEER routing
protocol. BEAR routing protocol considers energy
balancing and finding optimal distance. It finds a fair
tradeoff between energy balancing and optimal
distance by using learning automata concept.
Gupta et.al. [5] , proposed a mechanism to find the
cluster head called “Cluster head election mechanism
using fuzzy logic in wireless sensor networks”.
Cluster head node election method can reduce the
energy consumption and enhance the lifetime of the
network. A fuzzy logic approach to cluster-head
election is proposed based on three descriptorsenergy, concentration and centrality. Depending
upon network configuration, a substantial increase in
network lifetime can be accomplished by selecting
the nodes as cluster-heads using only the local
information.
3.
Implementation
Basics of fuzzy set are from the inception of fuzzy set
in 1965. It has advanced in different ways in many
areas. Fuzzy set has its applications in artificial
intelligence, computer science, medicine, logic,
management science, operations research. Fuzzy set
is a powerful tool for modeling uncertainty and for
processing vague or subjective information in
mathematical models. This mathematical model
development has advances of very high standard and
are still upcoming today. Since 1992 the theory of
neural nets and evolutionary programming are known
as computational intelligence.
The relationship
between these areas has naturally become close. The
main reason for development have been diverse and
its application to varied real problems. The notation
of fuzzy system is that truth values or membership
values that are indicated within range [0,1], with 0
and 1 representing absolute falseness and absolute
truth respectively and in between values are used for
representing the states that are within the range.
3.1 Fuzzy protocol routing
There are many inherent characteristics factors of
WSNs that have to be considered for design of
efficient routing. Some factors may include node
deployment, link heterogeneity, data reporting
method, energy consumption, scalability, data
aggregation, connectivity and quality of service.
Fuzzy set protocol is a reactive protocol which uses
lazy approach because the routes are discovered to
destination only on demand. Bandwidth consumption
in this protocol is less when compared to proactive
protocol, but has large delay in determining route.
Fuzzy set protocol is energy aware protocol. It has
three major steps:
1) Neighbor discovery
2) Forwarding data
3) Energy update
1) Neighbor discovery
The network has to be set up in the area where it has
to be operated once this setup has been next step is to
broadcast the message. The sink or base station
floods the network with broadcast message. Each
node after receiving the initial packet it makes an
entry to neighbor table including neighbor id, energy
level and hop count to it neighbor table. When base
station sends anther packet it checks the neighbor
table for node that transmitted the message. If not it
adds an entry to neighbor table. The node then
increases the hop count stored in the message and
stores this hop count as its own. It then retransmits
the broadcast message but change source address to
its address. It also changes the energy level field to
its energy level and then retransmits the broadcast
message. Every node retransmits the broadcast
message only once to all its neighbors in the network.
When base station flooded the initial broadcast
message every node in network knows its energy
level and hop count of its neighbors. The sink node
periodically keeps transmitting the message to
network, so that nodes add new neighbors that joined
the network to the neighbor table and remove the
node that have failed to be active member of the
network.
Let α be obtained from the following formula:
2) Forwarding data
Now, we define following decision maker equation:
Routing process is started when the node observes
the event. In node bases routing the decision is based
on the hop count and remaining energy. The most
important task is how to select the next hop. The
node searches its neighbor table for all neighbors
with small hop count than itself, if any such neighbor
then it is selected as destination for the message. If
two neighbors with same hop count is found then
decision is made upon the remaining energy of the
node. In the proposed protocol fuzzy protocol, that
uses the fuzzy set technique to solve problem of next
hop. It consists of two fuzzy set A and B.
A is fuzzy set of all neighbor energy level.
A={e1, e2,…..,en}
A has a membership function, mA(ei) which can be
defined as below,
mA(ei )  ei ,  1  i  n
n
 mA(e )
i

i 1
n
Then A  ei mA(ei   
(2)
Where α is energy threshold and Aα is used to remove
the neighbors with unacceptable energy level.
B is the fuzzy set of all neighbors hop counts with
membership function mB(hi), and the decision maker
equation is as below.
mB(hi )  1  hi
MaxHop,1  i  n
(3)
From this we find the neighbor with maximum
amount of C is selected as next hop.
Where,
MaxHop  ( x 2 )  ( y 2 ) / R
mA (ei )  mB(hi )
c(i)  
0
(4)
ei  
, where 1  i  n (5)
ei  
3) Energy update
Nodes may be used by more than one neighbor for
routing and therefore the energy value stored in the
neighbor tables of both of the node’s neighbors’ will
not be completely accurate. They update the energy
level of sender node in their neighbor table by
piggybacking technique. Nodes might be used by
more than one neighbor for routing in this case the
energy value stored in the neighbor table of both
nodes neighbor will be completely accurate by
overhearing technique.
4.
Experimental results.
(1)
Where  is a control parameter to limit energy
factor in [0,1] interval, ei is energy level of (i)th
neighbor.
For simulation work we consider two features for the
proposed protocol – 1) Fuzzy protocol with dynamic
maxhop known as dynamic fuzzy 2)Fuzzy protocol
with constant Maxhop known as static fuzzy.
Computation is done using NS2 simulator.
Simulation of the protocol started with a broadcast
message. Simulations are performed to evaluate the
network lifetime achieved by each protocol.
Figure 3: Packet drop
Figure 1: Packet delivery ratio
5.
Conclusion
The routing in this protocol is based on Fuzzy logic
for energy saving and energy balancing in WSN.
Here we use fuzzy set techniques in order to achieve
energy balancing in wireless sensor network. We
have simulated through ns2. We used two different
methods static and dynamic methods to compute the
energy balancing and energy saving. From the above
results we can say that the dynamic methods has
better performance than static method. This is based
on the observation that the packet delivery ratio and
packet drop are better for dynamic method, while the
energy consumed is comparable to that of static
method.
Figure 2: Energy consumed
REFERENCES
[1] Ashwani Kumar, “A Survery on routing protocols for wireless
sensor networks”,
International Journal of Advances in
Engineering Research(IJAER), Vol.No.I, Issue No.2, February
2011.
[2]. S. M. Abolhasani, M. R, Meybod, M. Esnaashari, "LABER: A
Learning Automata Based Energy-aware Routing Protocol for
Sensor Networks", Proceedings of the Third Information and
Knowledge Technology conference, Nov. 27-29, 2007.
[3]. G.P. Hancke, C.J. Leuschner, “SEER: A Simple Energy
Efficient Routing Protocol for Wireless Sensor Networks” South
African Computer Journal (SACJ), No.39, 2007.
[4]. E.Ahvar, M.Fathy, “BEAR: A Balanced Energy-Aware
Routing Protocol for Wireless Sensor Networks” Wireless Sensor
Network Journal, vol. 2, pp. 768–776. October 2010.
[5]. Gupta, I, Riordan, D, Sampalli, S. “Cluster-head election
using fuzzy logic for wireless sensor networks” In Proceedings of
the 3rd Annual Communication Networks and Services Research
Conference, Nova Scotia, Canada, 16-18 May 2005; pp. 255-260.
[6]. J. Chen, Y. Hsu and I. Chang, “Adaptive Routing Protocol for
Reliable Sensor Network Applications”, International Journal on
Smart Sensing and Intelligent Systems, Vol. 2, No. 4,
December 2009.
[7]. M. Youssef, M. Younis and K. Arisha. “A constrained
shortest-path energy-aware routing algorithm for wireless sensor
networks”, In Proceedings of IEEE Wireless Communications and
Networking Conference, vol. 2, pp. 794–799. 17–21 March 2002.
[8]. H.J. Zimmermann, "Fuzzy Set Theory and its Applications",
third ed. Kluwer Academic Publishers, Boston, MA. 1996.
[9]. F. Block and C. Baum, “An energy-efficient routing protocol
for wireless sensor networks with battery level uncertainty”, In
Proceedings of IEEE Military Communications Conference, vol. 1,
pp. 489–494, 7–10 October 2002.
[10]. C. Schurgers and M. Srivastava, “Energy efficient routing in
wireless sensor networks”, In Proceedings of IEEE Military
Communications Conference, vol. 1, pp. 357–361. 28–31 October
2001.