International Journal of Digital Communication and Networks (IJDCN)
Volume 2, Issue 3, March 2015
Adaptive Tree Based Energy Efficient Routing
In Mobile Sink on WSN
P.N.N.Priya Dharshini, S.Suvitha and G.Rekha

Abstract —Big Data is a broad term for massive data sets
having large, more various structure with the difficulties
of storing and analysing for further processes and results.
Energy efficient big data gathering in wireless sensor
networks is a challenging research area. The most
effective solution to overcome this challenge is to make
the use of Sink node’s mobility. It also have some
additional challenges like Sink node trajectory and
Cluster Formation. To avoid these challenges a new
mobile sink routing is proposed. This method is a
clustering based EM technique. In previous research
‘LEACH’ algorithm is used for clustering. But in order to
increase the efficiency ‘HEED’ algorithm is used in this
paper. So the Energy consumption is reduced and
increases the lifetime of the sensors.
Keywords: WSN, Clustering, HEED algorithm, and
Energy efficiency.
I. INTRODUCTION
Because of the tremendous growth of ICT, the Big
data has emerged as a hot topic [1]. Since gathering Big data
is a very difficult task. There are many existing approaches
such as data compression technology [2] that is capable of
shrinking the volume of the data. Though it is easy to
implement it requires large storage and high power. Further
more flow control and routing can choose the path which
consists of nodes having high remaining energy [3],[4]. But
these are not able to deal with the divided network problem.
In conventional research works, Data gathering
using the Mobile sink in WSN has been widely studied in
literature. Data MULE is one of the most prominent Mobile
sink schemes [5]. In this method the Mobile sink might fail to
collect information.
LEACH is one of the important clustering algorithm
[6]. It uses static sink node. Therefore it is not able to use this
algorithm. Some of the distributed clustering algorithm are
KOCA and K-CONID [7],[8], these algorithm cannot achieve
optimization. Then PEGASIS [9] and KAT mobility.
(K-means and TSP mobility) [10] are the centralized
clustering algorithms. These algorithms achieves uniform
energy consumption. But the algorithm does not achieve
Manuscript received March, 2015
P.N.N.Priya Dharshini, UG Student, KLN college of Information
Technology –Tamilnadu, India
S.Suvitha, UG Student KLN college of Information Technology
–Tamilnadu, India
G.Rekha , Assistant Professor, KLN college of Information Technology
–Tamilnadu, India
minimization of energy consumption because these clustering
algorithm uses Greedy algorithm. In [11] the limitation is the
maximum acceptable latency of data collection. It also defines
the limitation by a node’s buffer size. In our paper we present
how to evaluate the optimal number of clusters to minimize
the energy consumption of the sensor nodes.
The rest of this paper is ordered as follows: Section
II discusses the techniques such as Rendezvous algorithm,
Mobile Sink, Clustering. Section III detailed network model,
and analysis of the HEED algorithm. Section IV evaluates the
performance of the HEED algorithms. Section V concludes
the paper.
II. RELATED WORK
A wireless sensor network (WSN) (sometimes called a
wireless sensor and actor network (WSAN) of spatially
distributed autonomous sensors to monitor physical or
environmental conditions and to cooperatively pass their data
through the network to a main location. To gather a Big Data
generated by the densely distributed WSN is not an easy task
since the WSNs may be divided into sub-networks because of
the limited wireless communication range of the sensors. First
the sensor nodes are clustered. Then data from all nodes are
forwarded to the cluster head. Then these data will be
gathered by Mobile Sink. The Mobile Sink can collect large
amount of the packets without travelling long distance. So by
the use of this Mobile Sink we can reduces the Energy
Consumption and Data Loss. Here we introduced a data
collection protocol that is based on Rendezvous Point. This
creates a RP point based on the shortest path between the
Node and the Mobile Sink that is whose location is at the
place where the Sink node meets the Cluster. The CH
transfers the packets to this RP. Then these packets are
collected by the Sink node through the RP.
In this paper, we also present how to evaluate the optimal
number of clusters to minimize the Energy consumption of the
sensor nodes. Here we assume that the required energy for
data transmission of a node is directly proportional to the
square of transmission distance. The best Clustering
algorithm should minimize energy consumption for data
transmission must minimize the sum of square of data
transmission distance in a network. To demonstrate that our
approach is possible, we proposed HEED algorithm. Since it
is an area based clustering algorithm, all the nodes in the area
will be clustered. Also this algorithm is designed to select
different Cluster Heads in a field according to the amount of
energy that is distributed in relation to a neighboring node.
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All Rights Reserved © 2014 IJDCN
International Journal of Digital Communication and Networks (IJDCN)
Volume 2, Issue 3, March 2015
III. HEED ALGORITHM
In this section, we describe the HEED protocol. First, we
deﬁne the parameters used in the process of clustering.
Second, we present the design of protocol and pseudo-code.
Here we consider a Network which consists of 100
sensor nodes with one Mobile sink distributed within a limited
communication range. By using Localization Technology
each sensor nodes its own location and the Mobile sink knows
all nodes locations. Let R be the limited communication
range. So, the communication within R will always be a
successful one. In order to minimize the energy consumption
the Mobile sink go around the cluster centroids and collects
the datas. We assume a WSN having L*L area and N-number
of sensor nodes are distributed in the network. Let K be the
clusters centroid. Then the nodes are grouped based on its
communication range. Let G-number of groups are in the
field. Then Ng-number of nodes in the gth group. Kg-number
of cluster in the gth group.
HEED Algorithm is used to select different cluster
heads in a field according to the amount of energy that is
distributed in relation to a neighbouring node. Parameters for
Electing cluster heads are,
1. Primary parameter: Residual Energy(er).
2. Secondary parameter: Communication cost i.e., to
maximize energy and minimize cost.
A. Pseudocode for HEED
I. Initialize:
1. Snbr ←{v:v lies within cluster range }
2. Compute and broadcast cost to ∈Snbr
3. CHprob ← max(Cprob × Eresi/ Emax,pmin)
4. is ﬁnal CH ← FALSE
II. Repeat
1. If ((SCH ←{ v: v is a CH})6= φ)
2. my cluster head ← least cost(SCH)
3. If (my cluster head = N ID)
4. If (CHprob = 1)
5. CH msg(N ID,ﬁnal CH,cost)
6. is ﬁnal CH ← TRUE
7. Else
8. CH msg(N ID, Ten CH,cost)
9. ElseIf ( CHprob = 1)
10. CH msg(N ID,ﬁnal CH,cost)
11. is ﬁnal CH ← TRUE
12. ElseIf Random(0,1) ≤ CHprob
13. CH msg(N ID,Ten CH,cost)
14. CHprevious ← CHprob
15.CHprob←min(CHprob×2,1)
Until CHprevious =1,
Ten=tentative
III. Finalize
1. If (is ﬁnal CH = FALSE)
2. If ((SCH ←{ v:v is a ﬁnal CH}) 6= φ)
3. my cluster head ← least cost(SCH)
4. join cluster(CH ID, N ID)
5. Else CH msg(N ID, ﬁnal CH, cost)
6. Else CH msg(N ID, ﬁnal CH, cost)
B. Steps for HEED Algorithm
Initialization
Main
Processing
 Discover neighbour within
cluster range .
 Compute the initial CH
probability
 CH prob= f(Er/Emax)
 If we receive some cluster head
messages, choose one head
with minimum cost.
 If there is no CH elect to
become a CH with CH prob.
 CH prob= min(CH prob*2,1)
 If there is CH, join its cluster,
otherwise elect to be CH.
Finalization
HEED also has many major advantages. They are,
1. Create well distributed clusters.
2. Terminates in constant Time.
3. Reduces energy load.
4. Extends network lifetime.
IV. PERFORMANCE EVALUATIONS
This section is focused on the comparision of our
paper performance with the existing paper performances. In
Existing paper LEACH algorithm is used.
A. Optimal Number of Clusters
To evaluate our proposed method of optimizing
number of clusters, the Energy Consumption is measured by
varying the number of clusters. The energy consumption will
be reduced by reducing the number of clusters. By optimizing
the number of clusters, Throughput will be increased.
B. Energy Consumption
Energy Consumption is defined as the sum of energy
consumption of data transmission (Dtran) and the energy
consumption of data request (Dreq). Thus the Energy
Consumption will be reduced by the use of Mobile Sink. Even
by increasing the number nodes, Energy consumption can be
reduced.
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All Rights Reserved © 2014 IJDCN
International Journal of Digital Communication and Networks (IJDCN)
Volume 2, Issue 3, March 2015
Throughput
60%
90%
Packet Delivery Ratio
85%
98%
References
1. Four Vendor Views on Big Data and Big Data Analytics:
IBM IBM, Jan., 2012, Armonk, NY, USA, [online]
C. Efficient Data Collection
Since our proposed algorithm (HEED) is area based
routing protocol, all the sensor nodes in the given area will
be clustered. So that almost of the data generated by the
nodes will be received by the sink node. Therefore the data
loss will be reduced. So the Efficiency will be increased.
2. S. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard and J.
Crowcroft
“XORs in the air: Practical wireless network
coding”IEEE/ACM Trans. Netw., vol. 16, no. 3, pp.
497-510, Jun., 2008
3. S. He, J. Chen, D. Yau and Y. Sun
“Cross-layer optimization of correlated data gathering in
wireless sensor networks”Proc. 7th Annu. IEEE Commun.
Soc. Conf. Sensor Mesh and Ad Hoc Commun. and Netw.
(SECON), pp. 1-9, Jun. 2010
4. C. Jiming, X. Weiqiang, H. Shibo, S. Youxian, P.
Thulasiraman and S. Xuemin“Utility-based asynchronous
flow control algorithm for wireless sensor networks”IEEE
J. Sel. Areas Commun., vol. 28, no. 7, pp. 1116-1126,
Sep., 2010
5. R. C. Shah, S. Roy, S. Jain and W. Brunette“Data MULEs:
Modeling and analysis of a three-tier architecture for
sparse sensor networks”Ad Hoc Netw., vol. 1, no. 2–3, pp.
215-233, 2003
D. Lifetime
The Energy Consumption is inversely proportional
to the Lifetime. So the Lifetime of the sensor node will be
increased automatically by reducing the Energy
Consumption. So the Lifetime of the network is also
increased.
V. CONCLUSION
In this paper we have created a wireless sensor
network with number of static sensor nodes and a mobile sink
node that act as a base station. Energy Efficiency by using
Mobile Sink can be increased more than by using static sink
node. Since our proposal algorithm is area based routing it
gives the better result than the existing algorithm. By using
our proposed HEED algorithm and Rendezvous based data
collection efficiency will be increased more than the existing
LEACH algorithm. The Energy absorption will also be
reduced and the lifetime of the sensor nodes will also be
increased by optimizing the number of clusters. The
numerical representation for the effectiveness of our proposal
is 80% that is 20% more than of Existing. The effectiveness of
our proposed algorithm is proven by the following
comparision table.
Parameters
LEACH
HEED
Packet Dropped
63%
40%
Efficiency
60%
80%
Energy Remaining
60%
95%
6. W.
Heinzelman,
A.
Chandrakasan
and
H.Balakrishnan“Energy-efficient
communication
protocol for wireless microsensor networks”Proc. 33rd
Annu. Hawaii Int. Conf. Syst. Sci., vol. 2, Jan. 2000
7. M. Youssef, A. Youssef and M. Younis“Overlapping
multihop clustering for wireless sensor networks”IEEE
Trans. Parallel Distrib. Syst., vol. 20, no. 12, pp.
1844-1856, Dec., 2009
8. T.Khac, C.Hyunseung “Connectivity-based clustering
scheme for mobile ad hoc networks”Proc. IEEE Int. Conf.
RIVF, pp. 191-197, Jul. 2008
9. S.Lindsey, C. Raghavendra“PEGASIS: Power-efficient
gathering in sensor information systems”Proc. IEEE
Aerosp. Conf., pp. 1125-1130, Mar. 2002
10. H. Nakayama, N. Ansari, A. Jamalipour and N.
Kato“Fault-resilient sensing in wireless sensor
networks”Comput. Commun., vol. 30, no. 11–12, pp.
2375-2384, Sep., 2007
11. L. He, Z. Yang, J. Pan, L. Cai, J. Xu and Y. Gu“Evaluating
service disciplines for on-demand mobile data collection
in sensor networks”IEEE Trans. Mobile Comput., vol. 13,
no. 4, pp. 797-810, Apr., 2014
12. Daisuke Takaishi, Hiroki Nishiyama, Nei Kato, and Ryu
Miura.,” Towards Energy Efficient Big Data Gathering in
Densely Distributed Sensor Networks”,IEEE Sensors
Journal DOI 10.1109/TETC.2014.2318177.
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All Rights Reserved © 2014 IJDCN
International Journal of Digital Communication and Networks (IJDCN)
Volume 2, Issue 3, March 2015
AUTHOR PROFILE:
P.N.N.PriyaDharshini,
UG
student,
Department
of
Electronics
and
Communication Enginnering, KLN college
of Information Technology, Sivagangi. Her
area
of
interest
includes
wireless
ccommunication and sensor networks.
S.Suvitha UG student, Department of
Electronics
and
Communication
Enginnering, KLN college of Information
Technology, Sivagangi. Her area of interest
includes wireless sensor networks.
G.Rekha , received BE degree in Electronics
and Communication Engineering from KLN
college
of
Information
Technology
–Tamilnadu, India in 2008 and M.E degree in
Communication Systems from KLN college
of Enginering , Tamilnadu, India in 2012.
She has been working as an Asst.prof. in
KLN college of Information Technology
since 2008. Her main research areas are
wireless communication and Mobile
computing.
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All Rights Reserved © 2014 IJDCN