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ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. 2, Issue 10, October 2015
1
All Rights Reserved © 2015 IJARTET
A New Energy Efficient Routing Scheme for
Data Gathering
S.Mathu Muhila1, N.Priyadharshini2,G.Sudha3, P.Venkateswari4, H.Vishali5,Christo Ananth6
UG Student, Department of ECE, Francis Xavier Engineering College, Tirunelveli, India1,2,3,4,5
Associate Professor, Department of ECE, Francis Xavier Engineering College, Tirunelveli, India6
Abstract: Sensor network consists of low cost battery powered nodes which is limited in power. Hence power efficient
methods are needed for data gathering and aggregation in order to achieve prolonged network life. However, there are
several energy efficient routing protocols in the literature; quiet of them are centralized approaches, that is low energy
conservation. This paper presents a new energy efficient routing scheme for data gathering that combine the property of
minimum spanning tree and shortest path tree-based on routing schemes. The efficient routing approach used here is
Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) which is robust and localized. This is based on
powerful localized structure, local minimum spanning tree (LMST). The actual routing tree is constructed over this
topology. There is also a solution involved for route maintenance procedures that will be executed when a sensor node fails
or a new node is added to the network.
Keywords: Power Efficient Protocol, Minimum Spanning Tree, Topology network.
I. INTRODUCTION
Wireless sensor networks are a new technology for
collecting data about the natural or built environment. They
consist of nodes comprising the appropriate sensors along
with computational devices that transmit and receive data
wirelessly. The nodes work independently to record
environmental conditions. Each cooperates with its
neighbours to wirelessly transmit their readings via an ad-
hoc network. It is applicable to the locations where
communication is difficult, or where it is difficult to provide
power to the node.
According to protocol operation ,routing protocols
are classified as multipath-based, query-based,negotiation-
based Quality of Service (QoS)-based, or coherent based.
In flooding, a node sends out the received
data or the management packets to its neighbours by
broadcasting, unless a maximum number of hops for that
packet are reached or the destination of the packets is
arrived.
In Gossiping , nodes can forward the incoming
data/packets to randomly selected neighbor node. SPIN
avoids the drawbacks of flooding protocols mentioned
above by utilizing data negotiation and resource adaptive
algorithms.
Directed diffusion is another data dissemination and
aggregation protocol. It is a data-centric and application
aware routing protocol for WSNs. LEACH is a self-
organizing, adaptive clustering-based protocol that uses
randomized rotation of cluster-heads to evenly distribute the
energy load among the sensor nodes.
PEGASIS (Power-Efficient GAthering in Sensor
Information Systems) is a greedy chain-based power
efficient algorithm . Also, PEGASIS is based on LEACH .
GEAR (Geographical and Energy Aware Routing is a
recursive data dissemination protocol WSNs. It uses energy
aware and geographically informed neighbor selection
heuristics to rout a packet to the targeted region.
II.EXISTING SYSTEM
In [1], Haibo Zhang and Hong Shen
presented a solution to maximize network lifetime
through balancing energy consumption for
uniformly deployed data-gathering sensor
networks and formulated the energy balancing
problem as the problem of optimal allocation of
ISSN2394-3777 (Print)
ISSN2394-3785 (Online)
Available online atwww.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. 2, Issue 9, Sept 2015
2
All Rights Reserved © 2015 IJARTET
transmitting data by combining the ideas of
corona-based network division and mixed-routing
strategy together with data aggregation. They
presented the solutions for balancing energy
consumption among nodes both within the same
coronas and within different coronas. For that
they first propose a localized zone-based routing
scheme that guarantees balanced energy
consumption among nodes within each corona.
We then design an offline centralized algorithm
with time complexity O(n) (n is the number of
coronas) to solve the transmitting data distribution
problem aimed at balancing energy consumption
among nodes in different coronas. The approach
for computing the optimal number of coronas in
terms of maximizing network lifetime is also
presented. Based on the mathematical model, an
energy-balanced data gathering (EBDG) protocol
is designed and the solution for extending EBDG
to large-scale data-gathering sensor networks is
also presented.
In [2] Ivan Stojmenovic and Xu Lin
described several localized routing algorithms that
try to minimize the total energy per packet and/or
lifetime of each node. The proposed routing
algorithms are all demand-based and can be
augmented with some of the proactive or reactive
methods reported in literature to produce the
actual protocol. These methods use control
messages to update positions of all nodes to
maintain efficiency of routing algorithms. These
control messages also consume power and the
best trade-off for moving nodes is to be
established. The focus of this paper was to
examine power consumption in case of static
networks. The method was tested only on
networks with high connectivity and their
performance on lower degree networks remains to
be investigated. Based on experience with basic
methods like GEDIR, improvements in the power
routing scheme to increase delivery rates or even
to guaranty delivery and are necessary before
experiments with moving nodes are justified.
Power efficient methods tend to select well
positioned neighboring nodes in forwarding the
message while the cost efficient method favors
nodes with more remaining power. The node
movement, in this respect, will certainly assist
power aspect of the formula since the movement
will cause the change in relative node positioning.
In [3] Jae-Hwan Chang and Leandros
Tassiulas proposed algorithm which is shortest
cost path routing. Information obtained by the
monitoring nodes needs to be routed to a set of
designated gateway nodes. In these networks,
every node is capable of sensing, data processing,
and communication, and operates on its limited
amount of battery energy consumed mostly in
transmission and reception at its radio transceiver.
If we assume that the transmitter power level can
be adjusted to use the minimum energy required
to reach the intended next hop receiver then the
energy consumption rate per unit information
transmission depends on the choice of the next
hop node, i.e., the routing decision. They
formulate the routing problem as a linear
programming problem, where the objective is to
maximize the network lifetime, which is
equivalent to the time until the network partition
due to battery outage. Two different models are
considered for the information-generation
processes. One assumes constant rates and the
other assumes an arbitrary process. A shortest
cost path routing algorithm is proposed which
uses link costs that reflect both the
communication energy consumption rates and the
residual energy levels at the two end nodes. The
proposed algorithm is a shortest cost path routing
whose link cost is a combination of transmission
and reception energy consumption and the
residual energy levels at the two end nodes.
ISSN2394-3777 (Print)
ISSN2394-3785 (Online)
Available online atwww.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. 2, Issue 9, Sept 2015
3
All Rights Reserved © 2015 IJARTET
In [4] Jennifer C. Hou, Lui Sha, and Ning
Li proposed a minimum spanning tree (MST)-
based algorithm, called local minimum spanning
tree (LMST), for topology control in wireless
multihop networks. In this algorithm, each node
builds its LMST independently and only keeps
on-tree nodes that are one-hop away as its
neighbors in the final topology and analytically
prove several important properties of LMST such
as the topology derived under LMST preserves
the network connectivity and the topology can be
transformed into one with bidirectional links
(without impairing the network connectivity) after
removal of all unidirectional links. Results shows
that LMST can increase the network capacity as
well as reduce the energy consumption.
III.PROPOSED SYSTEM
A special route discovery packet is
broadcasted by the sink and when a node receives
that packet, it decides its parent according to the
information in the packet. After selecting the
parent, it rebroadcasts the packet. Three different
methods are used to construct tree: first parent
path method , nearest minimum hop path method ,
and shortest weighted path (i.e., least cost)
method .
In data information phase, each sensor
node periodically senses its nearby environment
and generates the data to be sent to the sink.
However, before sending it directly to the parent
node, it will wait all the data from its child nodes
and aggregate the data coming from them together
with its own data, and then, send the aggregated
data to the parent node. There may be a chance of
collision while sending data. So that, by using
appropriate ways we can reduce traffic loads.
The tree must be recomputed at specified
intervals. Since the computation depends on the
remaining energy of nodes, each time the
computation takes place and a different and more
power-efficient plan is yielded. In our case, we
handle this requirement by broadcasting a new
ROUTE-DISCOVERY packet with a new
sequence ID. Apparently, in order to utilize the
power-aware methods, each node must know the
remaining energy levels of its neighbors. In order
to exchange the remaining energy levels, we use
HELLO messages. So, at the beginning of each
recomputation phase, the nodes advertise their
remaining energy levels. After that, ROUTE-
DISCOVERY packet with a new sequence ID can
be broadcasted by the sink. Also consider traffic
load by using appropriate way.
Each ROUTE-DISCOVERY packet has
three fields a sequence ID which is increased
when a new discovery is initiated by the sink, an
optional distance field which shows the cost of
reaching the sink, and an optional neighbor list
field which is the list of the neighbors of the
sending node in the chosen topology. It holds the
minimum number of hops or minimum energy
cost to reach the sink, respectively. The neighbor
list field must only be used if the LMST topology
is chosen. But an important point to mention is
that in our approach, since the LMST
computation is combined with the route
computation, no extra messages are used for
negotiation among LMST neighbors.
We compare the comparison of Energy
Consumed, Network Lifetime, node lifetime and
number of nodes.
International Journal of Advanced Research
Vol. 2, Issue 9, Sept 2015
Fig.1. Network coverage
Fig.2. Simulated graph
ISSN
Available online at
International Journal of Advanced Research
Trends in Engineering
and Technology
All Rights Reserved © 2015 IJARTET
IV.C
ONCLUSION
Sensor network consists of low cost battery powered
nodes which is limited in power. Hence power efficient
methods are needed for data gathering and aggregation in
order to achieve prolonged network life. However, there are
several energy effic
ient routing protocols in the literature;
quiet of them are centralized approaches, that is low energy
conservation. This paper presents a new energy efficient
routing scheme for data gathering that combine the property
of minimum spanning tree and shortes
routing schemes. The efficient routing approach used here is
Localized Power-
Efficient Data Aggregation Protocols (L
PEDAPs) which is robust and localized. This is based on
powerful localized structure, local minimum spanning tree
(LMS
T). The actual routing tree is constructed over this
topology. There is also a solution involved for route
maintenance procedures that will be executed when a sensor
node fails or a new node is added to the network.
R
EFERENCES
1. Jae-Hwan Chang and Leandros
Lifetime Routing in Wireless Sensor Networks’, IEEE/ACM
Transactions on Networking, Vol.12, No. 4,pp. 609
2.
Jennifer C. Hou, Lui Sha, and Ning Li (2013), ‘Design and
Analysis of an MST-
Based Topology Control Algorithm
Proce
edings on IEEE INFOCOM, pp. 144
3. Kai-
Wei Fan, Prasun Sinha, and Sha Liu (2012),
Forwarding over Tree-on-
DAG for Scalable Data Aggregation
in Sensor Networks’,
IEEE Transactions on Mobile
Computig,Vol.6,No.10,pp. 1271-
1284.
4. Kai-Wei Fan,
Prasun Sinha, and Sha Liu (2011), ’
Free Data Aggregation in Sensor Networks
on Mobile computing, Vol. 6, No. 8,pp. 929
5.
H.Anusuya Baby, Christo Ananth,” Encryption And Decryption
In Complex Parallelism”,
International Jour
Research in Computer Engineering & Technolog, Volume 3
Issue 3, March 2014
6.
Ness B. Shroff , Sonia Fahmy, and Yan Wu (2008), ‘On the
Construction of a Maximum-
Lifetime Data Gathering Tree in
Sensor Networks: NP-
Completeness and Approximation
Algorithm’, proceedings on
IEEE INFOCOM,
ISSN2394-3777 (Print)
ISSN
2394-3785 (Online)
Available online at
www.ijartet.com
and Technology
(IJARTET)
4
ONCLUSION
Sensor network consists of low cost battery powered
nodes which is limited in power. Hence power efficient
methods are needed for data gathering and aggregation in
order to achieve prolonged network life. However, there are
ient routing protocols in the literature;
quiet of them are centralized approaches, that is low energy
conservation. This paper presents a new energy efficient
routing scheme for data gathering that combine the property
of minimum spanning tree and shortes
t path tree-based on
routing schemes. The efficient routing approach used here is
Efficient Data Aggregation Protocols (L
-
PEDAPs) which is robust and localized. This is based on
powerful localized structure, local minimum spanning tree
T). The actual routing tree is constructed over this
topology. There is also a solution involved for route
maintenance procedures that will be executed when a sensor
node fails or a new node is added to the network.
EFERENCES
Tassiulas (2014), ‘Maximum
Lifetime Routing in Wireless Sensor Networks’, IEEE/ACM
Transactions on Networking, Vol.12, No. 4,pp. 609
-619.
Jennifer C. Hou, Lui Sha, and Ning Li (2013), ‘Design and
Based Topology Control Algorithm
’,
edings on IEEE INFOCOM, pp. 144
– 150.
Wei Fan, Prasun Sinha, and Sha Liu (2012),
‘Dynamic
DAG for Scalable Data Aggregation
IEEE Transactions on Mobile
1284.
Prasun Sinha, and Sha Liu (2011), ’
Structure-
Free Data Aggregation in Sensor Networks
’, IEEE Transactions
on Mobile computing, Vol. 6, No. 8,pp. 929
-942.
H.Anusuya Baby, Christo Ananth,” Encryption And Decryption
International Jour
nal of Advanced
Research in Computer Engineering & Technolog, Volume 3
Ness B. Shroff , Sonia Fahmy, and Yan Wu (2008), ‘On the
Lifetime Data Gathering Tree in
Completeness and Approximation
IEEE INFOCOM,
pp. 356-360