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A New Energy Efficient Routing Scheme for Data Gathering

October 2015
SSRN Electronic Journal 2(10):1-4

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Samarkand State University named after Sharof Rashidov

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.

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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

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

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)

Vol. 2, Issue 9, Sept 2015

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)

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)

Vol. 2, Issue 9, Sept 2015

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

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.

EFERENCES

1. Jae-Hwan Chang and Leandros

Lifetime Routing in Wireless Sensor Networks’, IEEE/ACM

Transactions on Networking, Vol.12, No. 4,pp. 609

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

H.Anusuya Baby, Christo Ananth,” Encryption And Decryption

In Complex Parallelism”,

International Jour

Research in Computer Engineering & Technolog, Volume 3

Issue 3, March 2014

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)

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

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On the Construction of a Maximum-Lifetime Data Gathering Tree in Sensor Networks: NP-Completeness and Approximation Algorithm

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Energy efficiency is critical for wireless sensor networks. The data gathering process must be carefully designed to conserve energy and extend the network lifetime. For applications where each sensor continuously monitors the environment and periodically reports to a base station, a tree-based topology is often used to collect data from sensor nodes. In this work, we study the construction of a data gathering tree to maximize the network lifetime, which is defined as the time until the first node depletes its energy. The problem is shown to be NP-complete. We design an algorithm which starts from an arbitrary tree and iteratively reduces the load on bottleneck nodes (nodes likely to soon deplete their energy due to high degree or low remaining energy). We show that the algorithm terminates in polynomial time and is provably near optimal.

Design and Analysis of an MST-Based Topology Control Algorithm

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Design and analysis of an MST-based topology control algorithm

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Maximum Lifetime Routing In Wireless Sensor Networks

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Feb 2000

Routing in power-controlled wireless sensor networks is formulated as an optimization problem with the goal of maximizing the system lifetime. Considering that the information is delivered in the form of packets, we identified the problem as an integer programming problem. It is known that the system lifetime can be significantly extended by using a link metric that utilizes the information about the residual energy of the sensor nodes as well as the energy expenditure in transmission of a unit information over the wireless links. In this paper, some of the routing algorithms are proposed and examined in order to find the best link cost function and the method of shortest path calculation. The performance comparison is made through simulation in a typical battlefield scenario where sensors detecting a moving target vehicle periodically send a reporting packet to one of the gateway nodes. The results are also compared with the optimal solution obtained by the linear programming relaxation of the problem, which showed close-to-optimal performance.

On the Construction of a Maximum-Lifetime Data Gathering Tree in Sensor Networks: NP-Completeness and Approximation Algorithm

Jan 2008
IEEE ACM T NETWORK
609-619

B Ness
Sonia Shroff
Yan Fahmy
Wu

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, EFERENCES Tassiulas (2014), 'Maximum Lifetime Routing in Wireless Sensor Networks', IEEE/ACM Transactions on Networking, Vol.12, No. 4,pp. 609-619.

Free Data Aggregation

Jan 2011
929

Kai-Wei Fan
Prasun Sinha
Sha Liu

Kai-Wei Fan, Prasun Sinha, and Sha Liu (2011), ' Free Data Aggregation in Sensor Networks on Mobile computing, Vol. 6, No. 8,pp. 929

A New Energy Efficient Routing Scheme for Data Gathering

Abstract

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