Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network
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The document presents a study on energy-efficient clustering algorithms for wireless sensor networks (WSNs), focusing on the challenges posed by base station (BS) failures. It evaluates the performance of algorithms like HEED and EECML based on key metrics such as power consumption, cluster stability, and fault tolerance. The conclusion suggests that EECML is more suitable for maintaining network adaptability and reducing power consumption during BS replacements in critical applications.
![IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 07, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 258
Selection of Energy Efficient Clustering Algorithm upon Cluster Head
Failure in Wireless Sensor Network
Manglesh Khandelwal1
Gajendra Singh Chandel2
Kailash Patidar3
1
M.Tech. Student 2
Head of the Department 3
Assistant Professor
1,2,3
Department of Computer Science & Engineering
1,2,3
Sri Satya Sai Institute Of Science and Technology Sehore M.P.
Abstract— Wireless Sensor Network (WSN) applications
have increased in recent times in fields such as
environmental sensing, area monitoring, air pollution
monitoring, forest res detection, machine health monitoring,
and landslide detection. In such applications, there is a high
need of secure communication among sensor nodes. There
are different techniques to secure network data
transmissions, but due to power constraints of WSN, group
key based mechanism is the most preferred one. Hence, to
implement scalable energy efficient secure group
communication, the best approach would be hierarchical
based like Clustering. In most of the WSN designs based on
clustering, Base Station (BS) is the central point of contact
to the outside world and in case of its failure; it may lead to
total disconnection in the communication. Critical
applications like these cannot afford to have BS failure as it
is a gateway from sensor networks to the outside world. In
order to provide better fault tolerant immediate action, a
new BS at some other physical location will have to take the
charge. This may lead to a total change in the hierarchical
network topology, which in turn leads to re-clustering the
entire network and in turn formation of new security keys.
Therefore, there is a need to find a suitable algorithm which
clusters sensor nodes in such a way that when a BS fails and
a new BS takes the charge, new group key gets established
with minimum computation and less energy consumption.
Key words: Wireless sensor network, Cluster, Base Station,
HEED, EECML
I. INTRODUCTION
A. Wireless Sensor Networks
Wireless sensor network consists of large number of small,
low power, low cost sensor nodes with limited memory,
computational, and communication resources and a Base
Station. These nodes continuously monitor environmental
conditions and collect detailed information about the
physical environment in which they are installed, and then
transmits the collected data to the BS. BS is a gateway from
sensor networks to the outside world
B. Clustering
WSN [1, 2] consists of a large number of sensor nodes,
moreover these sensor nodes run on non-rechargeable
batteries. So to serve the objective of fault-tolerance, load
balancing and network connectivity, grouping of nodes is
required. Clustering [3] is a process of dividing sensor
nodes into groups on the basis of various parameters, and
selecting a group leader from each group. The groups are
called clusters and group leaders are called Cluster Heads
(CHs) of the clusters. Parameters for forming the clusters
include distance between cluster head and its member, intra-
cluster communication cost, residual energy of sensor nodes,
location of node with respect to BS etc.
Fig 1.1: Sensor information forwarding without
clustering and aggregation.
Figure 1.2 and Figure 1.3 shows the clustering
sensor network scenario before and after failure of BS
respectively, it is clear that whenever there is a change in
location of BS there will be some or more change in clusters
and topology of the sensor network, this also results in more
efforts in terms of energy consumption and time for
establishment of new group key.
Fig. 1.2: Clustering hierarchy before failure of Base Station
Fig. 1.3: New clustering hierarchy after failure of Base
Station
We have to find a suitable algorithm which clusters
sensor nodes in such a way that when a BS fails and anew
BS takes the charge, new group key gets established with
minimum computation and less energy consumption i.e. our
objective is to come up with the most suitable clustering
algorithm that requires minimum changes in clusters and
topology in such situations and makes WSN efficiently
adaptable.
II. PROPOSED APPROACH
A. Evaluation parameter
There are many metrics which would determine strength of
clustering algorithm for WSNs. After the detailed study, it
can be inferred that below mentioned six metrics decide the
extent of adaptability in the clustering algorithm in case of
failure of Base Station. These metrics are selected and are
defined as follows:
1) Power Consumption of Sensor Nodes
Power Consumption [1] for all systems, process and
communication protocols in sensor nodes and sensor
networks should be minimum as sensor nodes run on
batteries and recharging them is very difficult since large](https://image.slidesharecdn.com/ijsrdv2i7107-150918115241-lva1-app6891/85/Selection-of-Energy-Efficient-Clustering-Algorithm-upon-Cluster-Head-Failure-in-Wireless-Sensor-Network-1-320.jpg)
![Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network
(IJSRD/Vol. 2/Issue 07/2014/060)
All rights reserved by www.ijsrd.com 259
number of nodes are generally not attended after the
deployment. The clustering algorithm which minimizes
energy expense will in effect increase the lifetime of the
sensor nodes and hence of the entire network. Power
consumption also depends on the communication cost in the
network. Nodes in clustered Wireless Sensor Network [1, 2]
communicate via CHs. If the clusters are not properly
formed then the communication cost might increase.
Therefore, clustering technique should be properly selected.
2) Cluster Stability
Clustering [3] divides WSN into groups of sensor nodes.
Once clusters are formed, node membership changing from
one cluster to another cluster generally may not happen, but
CH in a cluster may change. In order to balance the load in
the cluster. This will indirectly help to reduce the energy
expense towards the re-keying process of group key
management activity in Wireless Sensor Network.
3) Fault tolerance and Re-clustering
Fault tolerance [2] is the ability to sustain network
functionalities without any interruption due to node failures.
Even if a node fails, network should be able to establish new
paths for communication between remaining nodes. When a
new sensor node gets added in the network, clustering
algorithm should be able to adapt the change in the network
with minimum energy-consumption and computation. Also
Re-clustering should require minimum efforts and
reconfigure the clusters in minimum time.
4) Cluster Overlapping
While forming clusters [3], one node should fall under only
one cluster. But there are some clustering algorithms [7, 8,
9] which clusters one sensor node in more than one cluster
due to which network overhead increases. Overlapping
nodes will increase the communication cost for the network
since that overlapped node will try to communicate using
multiple CHs. Thus, overlapping should be less in a
Wireless Sensor Network.
5) Scalability
A Wireless Sensor Network [1, 2] should perform its
functionality correctly even when the number of nodes or
workload in the network increases. Therefore Wire-less
Sensor Network must be scalable and adaptive to changes in
network topology.
6) Node Cluster Information
When clusters are created, each cluster member must have
information about its corresponding CH and each CH must
have the information about its member nodes in order to
establish and maintain proper secure group key management
in the cluster.
B. Further Approach
From literature, we selected many clustering algorithms in
order to achieve our goal. These algorithms were studied
against above mentioned parameters and the abstract results
which were obtained after the study. It is clear that EECML
and HEED are more appropriate choices for a clustering
algorithm in order to have adaptability in WSN. Since we
have to select the most suitable one, and these results only
give us abstract information’s. So, EECML and HEED are
analyzed further by implementing them on TinyOS, their
performance is evaluated on various aspects like Power
consumption, cluster stability, cluster overlapping,
scalability and fault tolerance. The algorithm which gives
better performance will be selected as the most suitable
algorithm.
Firstly in both algorithms, clusters are formed,
hierarchy among the nodes is created and once the clustering
process is completed, BS is made to fail. Then in order to
keep the network in running state (as required by critical
applications) another BS will take the charge and as
discussed earlier whole process of clustering is repeated and
new clusters, new hierarchy and new topology will be
established.
In this whole process a track on power
consumption by sensor nodes is kept. Total power
consumption of all nodes before failure of BS is recorded
for both the algorithms. New BS can be any random node
located in any random location. So, we consider different
positions of new BS by repeating the process and then
average of power consumed by all the nodes after failure of
BS in all the cases is
recorded. For calculating cluster stability, membership of all
the nodes for clusters is recorded before and after failure of
BS when clustering process is completed. These recorded
readings are compared and checked to see how many nodes
change their membership.
III. IMPLEMENTATION AND RESULTS
A. Implementation in TinyOS
From table 3.1, it is clear that amongst all clustering
algorithms surveyed, HEED and EECML are the more
appropriate choices for an energy-efficient clustering
algorithm which makes wireless sensor network adaptable
upon failure of Base Station. So, we need to evaluate their
performance in detail against all discussed metrics. There is
no cluster overlapping in both the algorithms and in both
algorithms Cluster Head contains information of its cluster
members and cluster members have information about its
CH. So we need to evaluate the performance of HEED and
EECML for Cluster Stability, Power Consumption of sensor
nodes, fault tolerance and scalability.
These algorithms are implemented in TinyOS, an
open-source development environment and a platform to
build WSN applications in easiest way. These algorithms
once implemented are simulated on TOSSIM, a discrete
event simulator provided by TinyOS.
1) Challenges in Implementation
The primary challenge is scarcity of energy that in effect
drives protocol design. In order to deal with this challenge
proper management of topology is required so that most of
the sensor nodes remain in operating state as long as
possible. Sensor nodes are deployed randomly in the field
and can be distributed arbitrarily on the ground, so some
nodes may on paths which is accessed most of the times to
communicate to the Base Station, this results in radial
depletion of their energy while other node's energy can be
left unmonitored. This effect is significantly reduced in
HEED as clustering is done periodically on the basis of
residual energy i.e. nodes with higher remaining energy are
elected to perform more loaded task of CHs and nodes with
lower remaining energy perform task of sensing. While in
EECML this effect is taken care by keeping size of clusters
closer to BS smaller as compared to clusters away from BS.](https://image.slidesharecdn.com/ijsrdv2i7107-150918115241-lva1-app6891/85/Selection-of-Energy-Efficient-Clustering-Algorithm-upon-Cluster-Head-Failure-in-Wireless-Sensor-Network-2-320.jpg)
![Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network
(IJSRD/Vol. 2/Issue 07/2014/060)
All rights reserved by www.ijsrd.com 260
Different sizes gives us an assurance that the closer CHs
have enough energy to transmit the data it receives from the
CHs farther from BS. Also as current CH is kept fixed in a
cluster; frequency of updating cluster-head is reduced and
thus minimizes energy consumption. Clustering enables
nodes to communicate with smaller power range at intra-
cluster level for more energy savings.
Another challenge is the estimation of remaining
energy of battery after some operation. It can be done by
inspecting analog-to-digital converter (ADC) for battery
voltage. Since ADC results have coarse granularity, this
approach may not be useful. So we use a simplistic approach
called credit-point system (CREP) where all sources of
energy consumption are considered. We are not using an
accurate CREP system instead only a rough estimate of
remaining energy is computed for all sensor nodes using an
integrated method independent of hardware.
2) Estimation of Energy Dissipation
We simulate the algorithms considering that they are
running on Mica2 mote. So all the values required in the
implementation are taken as per Mica2 mote values and
most of the information is taken from [4, 5]. Assuming
battery of sensor node has Vb average voltage and Ab Amp-
hr capacity. The maximum residual energy of each node,
Emax is computed as follows:
Emax = Vb Ab 3:6 103
Joule
If I is the current drawn while receiving or
transmitting a packet and tb be the bit transmission time then
the energy consumed in transmitting/receiving one packet of
size k is de ned as:
E = Vb I tb k 8
In a sensor network, energy dissipation of a sensor
node is mainly due to following reasons:
Processing
Send/Receive
B. Results
Experiments are conducted with 10,25,50 and 75 number of
sensor nodes with the above taken parameters. Both
EECML and HEED algorithms are evaluated against the
selected metrics in chapter 3 in each of the experiments and
their results are obtained as follows:
1) Power Consumption of sensor nodes
We can see the power consumed by all the sensor nodes to
form a clustering hierarchy in the network for both HEED
and EECML algorithms. These are the values obtained in
both algorithms as soon as clusters are established in the
net-work. Graph in figure 3.1 gives us the comparative
results for both the algorithms.
After this state, the current Base Station is made to
fail by stopping it, and another takes the charge. As soon as
this happens clustering protocol (HEED or EECML) starts
forming their clusters again with respect to new location of
BS. New BS can come at any random location, so we have
taken three different positions by simulating the algorithm
three times and then total power consumption of network is
calculated. Figure 3.2 shows the average of power
consumed by wireless sensor networks for both HEED and
EECML algorithms in all three different positions of new
BS. Graph in figure 3.2 shows comparative power
consumption of all sensor nodes in WSN. It is clear from
graphs 3.1 and 3.2that power consumption in HEED is
much more than that of EECML.
2) Cluster stability
Cluster stability is the extent to which change in
membership of a node from one cluster to another cluster
doesn't happen when one BS fails and another takes the
charge. We evaluate this metric by recording the clusters
and its members after formation of clusters in both the cases
i.e. before and after the failure of BS (considering three
different positions of new BS). Figure 3.3 shows the
average(of all three case) number of nodes who don't change
their cluster membership i.e. they doesn't move from one
cluster to another cluster. Graph in figure 3.3 shows the
cluster stability (in percentage) of both algorithms in able
EECML as compared to HEED.
Fig. 3.1: Power Consumption of all sensor nodes in HEED
and EECML after formation of clusters but
Before failure BASE STATION
Fig. 3.2: Power Consumption of all sensor nodes in HEED
and EECML after Failure of Base Station (average of
different positions of new BS)
Fig. 3.3: Stability of clusters in HEED and EECML
IV. CONCLUSION AND FUTURE WORK
A. Conclusion
A survey on different clustering algorithms has been done
for selecting the best one in order for the Wireless Sensor
Network to become adaptable. The main purpose is,](https://image.slidesharecdn.com/ijsrdv2i7107-150918115241-lva1-app6891/85/Selection-of-Energy-Efficient-Clustering-Algorithm-upon-Cluster-Head-Failure-in-Wireless-Sensor-Network-3-320.jpg)
![Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network
(IJSRD/Vol. 2/Issue 07/2014/060)
All rights reserved by www.ijsrd.com 261
whenever a Base Station fails, and a new Base Station takes
the charge, re-clustering has to be done, but new clusters
formed should not be completely different so that later when
security algorithm acts on the updated cluster, the overhead
reduces to minimum. Therefore, after the detailed study, it
can be inferred that mentioned six metrics decide the extent
of adaptability in the clustering algorithm. Specially, it has
been observed that the more the cluster stability and less the
cluster overlapping, the more will be the network reliability.
Also power consumption has become a significant factor for
improving network lifetime. Even though energy depletion
of CH in EECML is faster as compared to that in HEED, the
total power consumption of sensor network in EECML is
much less as com-pared to HEED. Therefore, considering
the overall network, the above factors and results obtained,
it can be concluded that EECML is the appropriate choice of
clustering algorithms to achieve high adaptability in
Wireless Sensor Network upon failure of BS.
B. Future Work
From this research it has been concluded that EECML is the
most appropriate clustering algorithm for a WSN to have
adaptability in WSN upon failure of Base Station. Power
consumption of sensor nodes in EECML algorithm can
further be improved as in EECML when member wants to
send packet to the Base Station it first send to its CH which
is in reverse direction of BS, so there is reverse forwarding
of packet in clusters in order to send to the Base Station.
Therefore, while selecting the clusters head in EECML,
instead of selecting the node closer to the boundary of the
cluster and next cluster, select the one which closer to the
boundary of the same cluster and previous cluster. So that
cluster members need not have to spend their energy in
reverse forwarding of packets, but sending in the direction
of BS. In this way total energy consumption of member
nodes will be reduced
Since EECML algorithm is required by critical
applications those who cannot afford BS failure and requires
a secure communication among sensor nodes which can be
provided by group key mechanism. So, this EECML
algorithm should be implemented with a group key
establishment algorithm [3, 4] and then the performance of
the algorithm should be analyzed in various events like
adding of a node inside network, removal of a node from the
network. That is how group key in a group of a network
changes whenever any node joins the group of sensor node,
or any node leaves the group or when the BS fails in the
network.
REFERENCE
[1] Xiang Min, Shi Wei-Ren, Jiang Chang-Jiang, Zhang
Ying, Energy Efficient Clustering Algorithm for
Maximizing Lifetime of Wireless Sensor Net-works,
International Journal of Electronics and
communications (AE) 64 pages 289298, 2010.
[2] Sangho Yi, Junyoung Heo, Jiman Hong, PEACH:
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Protocol for Wireless Sensor Networks, Science
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2852, 2007.
[3] Yan Jin, Ling Wang, Yoohwan Kim, Xiaozong Yang,
EEMC: An Energy-E cient Multi-level Clustering
Algorithm for Large-Scale Wireless Sensor Net-works,
Science Direct, Computer Networks 52, pages 542-
562, 2008.
[4] O.Younis, Sonia Fahmy, Energy-efficient Routing and
Data aggregation in sensor networks : An
Experimental Study, Department of Computer
Sciences, Purdue University, West Lafayette, 47907,
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[5] V. Shnayder, M.Hempstead, C.G.Werner, M. Welsh,
Simulating the Power Consumption of Large Scale
Sensor Network Applications, ACM conference on
Embedded Networked Sensor Systems (ACM SenSys),
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[6] Guo B, Li Z, United voting dynamic cluster routing
algorithm based on residual-energy in wireless sensor
networks, Journal of Electronics & Information
Technology 29(12), pages 3006-3010, 2007.
[7] Yuan H-y, Yang S-q, Li X-l et al, Time-controlled
routing algorithm for sensor networks, Journal of
System Simulation 20(5), pages 11631166, 2008.
[8] Fatma Bouabdallah, Nizar Bouabdallah, Raouf
Boutaba, Reliable and energy efficient cooperative
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Communication-36, ELSEVIER, pages 520-532 ,2013
[9] Francisco J. Fernandez-Luque, David Perez,and Felix
Martinez, An energy efficient middleware for an ad-
hoc AAL wireless sensor network, Ad-hoc network -11
ELSEVIER page 907-925.](https://image.slidesharecdn.com/ijsrdv2i7107-150918115241-lva1-app6891/85/Selection-of-Energy-Efficient-Clustering-Algorithm-upon-Cluster-Head-Failure-in-Wireless-Sensor-Network-4-320.jpg)
Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network
- 1. IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 07, 2014 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 258 Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network Manglesh Khandelwal1 Gajendra Singh Chandel2 Kailash Patidar3 1 M.Tech. Student 2 Head of the Department 3 Assistant Professor 1,2,3 Department of Computer Science & Engineering 1,2,3 Sri Satya Sai Institute Of Science and Technology Sehore M.P. Abstract— Wireless Sensor Network (WSN) applications have increased in recent times in fields such as environmental sensing, area monitoring, air pollution monitoring, forest res detection, machine health monitoring, and landslide detection. In such applications, there is a high need of secure communication among sensor nodes. There are different techniques to secure network data transmissions, but due to power constraints of WSN, group key based mechanism is the most preferred one. Hence, to implement scalable energy efficient secure group communication, the best approach would be hierarchical based like Clustering. In most of the WSN designs based on clustering, Base Station (BS) is the central point of contact to the outside world and in case of its failure; it may lead to total disconnection in the communication. Critical applications like these cannot afford to have BS failure as it is a gateway from sensor networks to the outside world. In order to provide better fault tolerant immediate action, a new BS at some other physical location will have to take the charge. This may lead to a total change in the hierarchical network topology, which in turn leads to re-clustering the entire network and in turn formation of new security keys. Therefore, there is a need to find a suitable algorithm which clusters sensor nodes in such a way that when a BS fails and a new BS takes the charge, new group key gets established with minimum computation and less energy consumption. Key words: Wireless sensor network, Cluster, Base Station, HEED, EECML I. INTRODUCTION A. Wireless Sensor Networks Wireless sensor network consists of large number of small, low power, low cost sensor nodes with limited memory, computational, and communication resources and a Base Station. These nodes continuously monitor environmental conditions and collect detailed information about the physical environment in which they are installed, and then transmits the collected data to the BS. BS is a gateway from sensor networks to the outside world B. Clustering WSN [1, 2] consists of a large number of sensor nodes, moreover these sensor nodes run on non-rechargeable batteries. So to serve the objective of fault-tolerance, load balancing and network connectivity, grouping of nodes is required. Clustering [3] is a process of dividing sensor nodes into groups on the basis of various parameters, and selecting a group leader from each group. The groups are called clusters and group leaders are called Cluster Heads (CHs) of the clusters. Parameters for forming the clusters include distance between cluster head and its member, intra- cluster communication cost, residual energy of sensor nodes, location of node with respect to BS etc. Fig 1.1: Sensor information forwarding without clustering and aggregation. Figure 1.2 and Figure 1.3 shows the clustering sensor network scenario before and after failure of BS respectively, it is clear that whenever there is a change in location of BS there will be some or more change in clusters and topology of the sensor network, this also results in more efforts in terms of energy consumption and time for establishment of new group key. Fig. 1.2: Clustering hierarchy before failure of Base Station Fig. 1.3: New clustering hierarchy after failure of Base Station We have to find a suitable algorithm which clusters sensor nodes in such a way that when a BS fails and anew BS takes the charge, new group key gets established with minimum computation and less energy consumption i.e. our objective is to come up with the most suitable clustering algorithm that requires minimum changes in clusters and topology in such situations and makes WSN efficiently adaptable. II. PROPOSED APPROACH A. Evaluation parameter There are many metrics which would determine strength of clustering algorithm for WSNs. After the detailed study, it can be inferred that below mentioned six metrics decide the extent of adaptability in the clustering algorithm in case of failure of Base Station. These metrics are selected and are defined as follows: 1) Power Consumption of Sensor Nodes Power Consumption [1] for all systems, process and communication protocols in sensor nodes and sensor networks should be minimum as sensor nodes run on batteries and recharging them is very difficult since large
- 2. Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network (IJSRD/Vol. 2/Issue 07/2014/060) All rights reserved by www.ijsrd.com 259 number of nodes are generally not attended after the deployment. The clustering algorithm which minimizes energy expense will in effect increase the lifetime of the sensor nodes and hence of the entire network. Power consumption also depends on the communication cost in the network. Nodes in clustered Wireless Sensor Network [1, 2] communicate via CHs. If the clusters are not properly formed then the communication cost might increase. Therefore, clustering technique should be properly selected. 2) Cluster Stability Clustering [3] divides WSN into groups of sensor nodes. Once clusters are formed, node membership changing from one cluster to another cluster generally may not happen, but CH in a cluster may change. In order to balance the load in the cluster. This will indirectly help to reduce the energy expense towards the re-keying process of group key management activity in Wireless Sensor Network. 3) Fault tolerance and Re-clustering Fault tolerance [2] is the ability to sustain network functionalities without any interruption due to node failures. Even if a node fails, network should be able to establish new paths for communication between remaining nodes. When a new sensor node gets added in the network, clustering algorithm should be able to adapt the change in the network with minimum energy-consumption and computation. Also Re-clustering should require minimum efforts and reconfigure the clusters in minimum time. 4) Cluster Overlapping While forming clusters [3], one node should fall under only one cluster. But there are some clustering algorithms [7, 8, 9] which clusters one sensor node in more than one cluster due to which network overhead increases. Overlapping nodes will increase the communication cost for the network since that overlapped node will try to communicate using multiple CHs. Thus, overlapping should be less in a Wireless Sensor Network. 5) Scalability A Wireless Sensor Network [1, 2] should perform its functionality correctly even when the number of nodes or workload in the network increases. Therefore Wire-less Sensor Network must be scalable and adaptive to changes in network topology. 6) Node Cluster Information When clusters are created, each cluster member must have information about its corresponding CH and each CH must have the information about its member nodes in order to establish and maintain proper secure group key management in the cluster. B. Further Approach From literature, we selected many clustering algorithms in order to achieve our goal. These algorithms were studied against above mentioned parameters and the abstract results which were obtained after the study. It is clear that EECML and HEED are more appropriate choices for a clustering algorithm in order to have adaptability in WSN. Since we have to select the most suitable one, and these results only give us abstract information’s. So, EECML and HEED are analyzed further by implementing them on TinyOS, their performance is evaluated on various aspects like Power consumption, cluster stability, cluster overlapping, scalability and fault tolerance. The algorithm which gives better performance will be selected as the most suitable algorithm. Firstly in both algorithms, clusters are formed, hierarchy among the nodes is created and once the clustering process is completed, BS is made to fail. Then in order to keep the network in running state (as required by critical applications) another BS will take the charge and as discussed earlier whole process of clustering is repeated and new clusters, new hierarchy and new topology will be established. In this whole process a track on power consumption by sensor nodes is kept. Total power consumption of all nodes before failure of BS is recorded for both the algorithms. New BS can be any random node located in any random location. So, we consider different positions of new BS by repeating the process and then average of power consumed by all the nodes after failure of BS in all the cases is recorded. For calculating cluster stability, membership of all the nodes for clusters is recorded before and after failure of BS when clustering process is completed. These recorded readings are compared and checked to see how many nodes change their membership. III. IMPLEMENTATION AND RESULTS A. Implementation in TinyOS From table 3.1, it is clear that amongst all clustering algorithms surveyed, HEED and EECML are the more appropriate choices for an energy-efficient clustering algorithm which makes wireless sensor network adaptable upon failure of Base Station. So, we need to evaluate their performance in detail against all discussed metrics. There is no cluster overlapping in both the algorithms and in both algorithms Cluster Head contains information of its cluster members and cluster members have information about its CH. So we need to evaluate the performance of HEED and EECML for Cluster Stability, Power Consumption of sensor nodes, fault tolerance and scalability. These algorithms are implemented in TinyOS, an open-source development environment and a platform to build WSN applications in easiest way. These algorithms once implemented are simulated on TOSSIM, a discrete event simulator provided by TinyOS. 1) Challenges in Implementation The primary challenge is scarcity of energy that in effect drives protocol design. In order to deal with this challenge proper management of topology is required so that most of the sensor nodes remain in operating state as long as possible. Sensor nodes are deployed randomly in the field and can be distributed arbitrarily on the ground, so some nodes may on paths which is accessed most of the times to communicate to the Base Station, this results in radial depletion of their energy while other node's energy can be left unmonitored. This effect is significantly reduced in HEED as clustering is done periodically on the basis of residual energy i.e. nodes with higher remaining energy are elected to perform more loaded task of CHs and nodes with lower remaining energy perform task of sensing. While in EECML this effect is taken care by keeping size of clusters closer to BS smaller as compared to clusters away from BS.
- 3. Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network (IJSRD/Vol. 2/Issue 07/2014/060) All rights reserved by www.ijsrd.com 260 Different sizes gives us an assurance that the closer CHs have enough energy to transmit the data it receives from the CHs farther from BS. Also as current CH is kept fixed in a cluster; frequency of updating cluster-head is reduced and thus minimizes energy consumption. Clustering enables nodes to communicate with smaller power range at intra- cluster level for more energy savings. Another challenge is the estimation of remaining energy of battery after some operation. It can be done by inspecting analog-to-digital converter (ADC) for battery voltage. Since ADC results have coarse granularity, this approach may not be useful. So we use a simplistic approach called credit-point system (CREP) where all sources of energy consumption are considered. We are not using an accurate CREP system instead only a rough estimate of remaining energy is computed for all sensor nodes using an integrated method independent of hardware. 2) Estimation of Energy Dissipation We simulate the algorithms considering that they are running on Mica2 mote. So all the values required in the implementation are taken as per Mica2 mote values and most of the information is taken from [4, 5]. Assuming battery of sensor node has Vb average voltage and Ab Amp- hr capacity. The maximum residual energy of each node, Emax is computed as follows: Emax = Vb Ab 3:6 103 Joule If I is the current drawn while receiving or transmitting a packet and tb be the bit transmission time then the energy consumed in transmitting/receiving one packet of size k is de ned as: E = Vb I tb k 8 In a sensor network, energy dissipation of a sensor node is mainly due to following reasons: Processing Send/Receive B. Results Experiments are conducted with 10,25,50 and 75 number of sensor nodes with the above taken parameters. Both EECML and HEED algorithms are evaluated against the selected metrics in chapter 3 in each of the experiments and their results are obtained as follows: 1) Power Consumption of sensor nodes We can see the power consumed by all the sensor nodes to form a clustering hierarchy in the network for both HEED and EECML algorithms. These are the values obtained in both algorithms as soon as clusters are established in the net-work. Graph in figure 3.1 gives us the comparative results for both the algorithms. After this state, the current Base Station is made to fail by stopping it, and another takes the charge. As soon as this happens clustering protocol (HEED or EECML) starts forming their clusters again with respect to new location of BS. New BS can come at any random location, so we have taken three different positions by simulating the algorithm three times and then total power consumption of network is calculated. Figure 3.2 shows the average of power consumed by wireless sensor networks for both HEED and EECML algorithms in all three different positions of new BS. Graph in figure 3.2 shows comparative power consumption of all sensor nodes in WSN. It is clear from graphs 3.1 and 3.2that power consumption in HEED is much more than that of EECML. 2) Cluster stability Cluster stability is the extent to which change in membership of a node from one cluster to another cluster doesn't happen when one BS fails and another takes the charge. We evaluate this metric by recording the clusters and its members after formation of clusters in both the cases i.e. before and after the failure of BS (considering three different positions of new BS). Figure 3.3 shows the average(of all three case) number of nodes who don't change their cluster membership i.e. they doesn't move from one cluster to another cluster. Graph in figure 3.3 shows the cluster stability (in percentage) of both algorithms in able EECML as compared to HEED. Fig. 3.1: Power Consumption of all sensor nodes in HEED and EECML after formation of clusters but Before failure BASE STATION Fig. 3.2: Power Consumption of all sensor nodes in HEED and EECML after Failure of Base Station (average of different positions of new BS) Fig. 3.3: Stability of clusters in HEED and EECML IV. CONCLUSION AND FUTURE WORK A. Conclusion A survey on different clustering algorithms has been done for selecting the best one in order for the Wireless Sensor Network to become adaptable. The main purpose is,
- 4. Selection of Energy Efficient Clustering Algorithm upon Cluster Head Failure in Wireless Sensor Network (IJSRD/Vol. 2/Issue 07/2014/060) All rights reserved by www.ijsrd.com 261 whenever a Base Station fails, and a new Base Station takes the charge, re-clustering has to be done, but new clusters formed should not be completely different so that later when security algorithm acts on the updated cluster, the overhead reduces to minimum. Therefore, after the detailed study, it can be inferred that mentioned six metrics decide the extent of adaptability in the clustering algorithm. Specially, it has been observed that the more the cluster stability and less the cluster overlapping, the more will be the network reliability. Also power consumption has become a significant factor for improving network lifetime. Even though energy depletion of CH in EECML is faster as compared to that in HEED, the total power consumption of sensor network in EECML is much less as com-pared to HEED. Therefore, considering the overall network, the above factors and results obtained, it can be concluded that EECML is the appropriate choice of clustering algorithms to achieve high adaptability in Wireless Sensor Network upon failure of BS. B. Future Work From this research it has been concluded that EECML is the most appropriate clustering algorithm for a WSN to have adaptability in WSN upon failure of Base Station. Power consumption of sensor nodes in EECML algorithm can further be improved as in EECML when member wants to send packet to the Base Station it first send to its CH which is in reverse direction of BS, so there is reverse forwarding of packet in clusters in order to send to the Base Station. Therefore, while selecting the clusters head in EECML, instead of selecting the node closer to the boundary of the cluster and next cluster, select the one which closer to the boundary of the same cluster and previous cluster. So that cluster members need not have to spend their energy in reverse forwarding of packets, but sending in the direction of BS. In this way total energy consumption of member nodes will be reduced Since EECML algorithm is required by critical applications those who cannot afford BS failure and requires a secure communication among sensor nodes which can be provided by group key mechanism. So, this EECML algorithm should be implemented with a group key establishment algorithm [3, 4] and then the performance of the algorithm should be analyzed in various events like adding of a node inside network, removal of a node from the network. That is how group key in a group of a network changes whenever any node joins the group of sensor node, or any node leaves the group or when the BS fails in the network. REFERENCE [1] Xiang Min, Shi Wei-Ren, Jiang Chang-Jiang, Zhang Ying, Energy Efficient Clustering Algorithm for Maximizing Lifetime of Wireless Sensor Net-works, International Journal of Electronics and communications (AE) 64 pages 289298, 2010. [2] Sangho Yi, Junyoung Heo, Jiman Hong, PEACH: Power-e cient and Adaptive Clustering Hierarchy Protocol for Wireless Sensor Networks, Science Direct, Computer communications 30, pages 2842- 2852, 2007. [3] Yan Jin, Ling Wang, Yoohwan Kim, Xiaozong Yang, EEMC: An Energy-E cient Multi-level Clustering Algorithm for Large-Scale Wireless Sensor Net-works, Science Direct, Computer Networks 52, pages 542- 562, 2008. [4] O.Younis, Sonia Fahmy, Energy-efficient Routing and Data aggregation in sensor networks : An Experimental Study, Department of Computer Sciences, Purdue University, West Lafayette, 47907, 2004 [5] V. Shnayder, M.Hempstead, C.G.Werner, M. Welsh, Simulating the Power Consumption of Large Scale Sensor Network Applications, ACM conference on Embedded Networked Sensor Systems (ACM SenSys), 2004. [6] Guo B, Li Z, United voting dynamic cluster routing algorithm based on residual-energy in wireless sensor networks, Journal of Electronics & Information Technology 29(12), pages 3006-3010, 2007. [7] Yuan H-y, Yang S-q, Li X-l et al, Time-controlled routing algorithm for sensor networks, Journal of System Simulation 20(5), pages 11631166, 2008. [8] Fatma Bouabdallah, Nizar Bouabdallah, Raouf Boutaba, Reliable and energy efficient cooperative detection in wireless sensor Network, computer Communication-36, ELSEVIER, pages 520-532 ,2013 [9] Francisco J. Fernandez-Luque, David Perez,and Felix Martinez, An energy efficient middleware for an ad- hoc AAL wireless sensor network, Ad-hoc network -11 ELSEVIER page 907-925.