Smart LCM for Energy-Efficient Mechanism Using CTX In Wireless Sensor Network

International Journal of Advance Foundation and Research in Computer (IJAFRC)

Volume 1, Issue 4, April 2014. ISSN 2348 - 4853

47 | 2014, IJAFRC All Rights Reserved www.ijafrc.org

Smart LCM for Energy-Efficient Mechanism Using CTX In

Wireless Sensor Network N.Sugumar1, C.K.Gomathy2

1M.E Student, Department of Computer Science and Engineering, SCSVMV University Enathur 2Asst.Professor, Department of Computer Science and Engineering, SCSVMV University Enathur

[email protected] [email protected]

A B S T R A C T

Link-aware clustering mechanism (LCM) is to support energy efficient routing in WSNs. The

main goal of the LCM is to establish a persistent and reliable routing path by determining

proper nodes to become cluster-heads and gateways. In the LCM, cluster-head and gateway

candidates use the node status (e.g., residual energy) and link condition (e.g., quality) to

determine a clustering metric, called the predicted transmission count and additionally the

bandwidth is also considered. The cluster head or gateway candidate having the highest priority

is elected as a cluster-head or a gateway, respectively. The main contribution of this project is

that it proposes a link aware clustering mechanism, and the proposed mechanism can support

energy-efficient routing. Simulation results confirm that the LCM can achieve a high packet

delivery ratio, extend the network lifetime, and reduce transmission latency.

Index Term: Cluster Head, Gateway, Capable Transmission Count, Cluster Head ready, Gateway

ready.

I. INTRODUCTION

A wireless sensor network (WSN) is a computer 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.[1] 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 civilian application areas,

including environment and habitat monitoring, healthcare applications, home automation, and traffic

control.

The Wireless Sensor Network (WSN) has recently become promising network architecture and is

widely used in many applications, environmental monitoring, object detection, event tracking, and

security surveillance. In general, WSNs consist of large numbers of tiny autonomous wireless devices,

called sensor nodes, which perform multiple functions such as sensing, computing, and communication.

In typical WSNs, sensor nodes (i.e., source nodes) must report the sensing or monitoring data to a

central node, called the sink, when receiving query messages sent by the sink. Because sensor nodes

are battery-powered devices, charging batteries for sensor nodes is often difficult. Operations such as

communication, and computation, consume the energy of sensor nodes, and data transmission is the

major source of energy consumption. Thus, it is a serious challenge to design an energy efficient routing

scheme for reporting sensory data to achieve a high delivery ratio and prolong the network lifetime

[10].

Wireless sensors are devices that range in size from a piece of glitter to a deck of cards. They are

functionally composed of:




Sensing unit that is designed and programmed to sense whatever characteristic is of interest;

some common examples of properties that are monitored are light, temperature, humidity,

pressure, etc.

A converter that transforms the sensed signal from an analog to a digital signal;

A microprocessor controlling component that includes an operating system for the unit,

processor and memory; a radio component that includes both a receiver and a transmitter [2].

Powering these components is typically one or two small batteries. There are also wireless sensors

utilized in applications that use a constant, wired power source and do not use batteries as a power

source. This type of wireless sensor is not considered in this paper.

In an external environment where the power source is batteries, which this paper will concentrate on,

wireless sensors are placed in an area of interest that is to be monitored, either in a random or known

fashion. The sensors self-organize themselves in a radio network using a routing algorithm, monitor the

area for whatever parameter it was designed to monitor, and transmit the data to a central node,

sometimes called a base station, or sink node, that collects the data from all of the sensors. This node may

be the same as the other nodes, or because of its increased requirements, may be a more sophisticated

node with increased power. The unique advantage of wireless sensors is that they may be deployed in an

environment for extended periods of time, continuously monitoring the environment, without the need

for human interaction or operation. This, however, establishes the power source as the limiting

component of the sensor [8].

II. LITERATURE REVIEW

[1]. An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks Seema

Bandyopadhyay and Edward J. Coyle , School of Electrical and Computer Engineering , Purdue

University ,West Lafayette, IN, USA

A wireless network consisting of a large number of small sensors with low-power transceivers can be an

effective tool for gathering data in a variety of environments. The data collected by each sensor is

communicated through the network to a single processing center that uses all reported data to determine

characteristics of the environment or detect an event. The communication or message passing process

must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups,

so that sensors communicate information only to cluster-heads and then the cluster-heads communicate

the aggregated information to the processing center, may save energy [2].

Advantage

EC is suitable for any data collection protocol that focuses on energy conservation.

The EC extends network lifetime and achieves energy equalization more effectively than two well-

known clustering algorithms, HEED and UCR.

EC effectively controls cluster sizes, which allows an approximately uniform use of the overall energy

resources of a WSN.




Disadvantage

It is still based on single-hop transmissions to the sink from the CHs and is not scalable to large-scale

networks.

The analysis of energy consumption in control overhead caused by route discovery and cluster

formation is not fully covered.

[2]. A Density and Distance based Cluster Head Selection Algorithm in Sensor Networks

Kyounghwa Lee #1, Joohyun Lee #2, Hyubgun Lee #3, Youngtae Shin#4 Department of Computer,

Soongsil University,Sangdo5-dong Dongjak-gu, South Korea

One of the most important considerations in designing sensor nodes in a wireless sensor networks is to

extend the network lifetime by minimizing energy consumption with limited resources. In this paper, we

propose a Density and Distance based Cluster Head Selection (DDCHS) algorithm in sensor networks. The

proposed algorithm divides cluster area into two perpendicular diameters, and then selects cluster head

by the density of member nodes and the distance from cluster head. Through the simulation experiments,

we showed that our algorithm has improves the performance of cluster head selection and provides

more energy-efficient [3].

Advantage

DDCHS algorithm has improves the network lifetime about 50% better than LEACH and about

10 % better than HEED.

DDCHS algorithm which improves the performance of clustering.

DDCHS algorithm has improves the performance of cluster head selection and provide more

energy-efficient

Disadvantage

HEED does not guarantee the number of selected cluster head. If the energy of all nodes is similarly

low, most nodes can become cluster head.

Sensor networks consist of large number of small, relatively inexpensive and low-power sensors

that are connected to the wireless network.

III. STATEMENT OF THE PROBLEM

Existing System

In the passive clustering technique, each node in a cluster has an external cluster state, and the cluster-

head and gateway nodes are major participants in packet delivery. When a node receives a data packet, it

depends on its current state and the state of the sender of the packet to determine whether it must

change its current state [1]. Each node piggybacks its state onto the transmitted packet, and thus a node

can realize the cluster states of all its neighbors. The passive clustering technique can effectively decrease

the number of explicit control packets to constantly maintain cluster information, and thereby reduce

communication overhead. If there are many cluster-head candidates, most of the existing clustering

approaches use a random strategy to determine cluster-heads. However, this strategy is likely to

determine improper cluster-heads. Note that cluster-heads generally consume more battery power than

other nodes in cluster-based routing protocols. If the cluster-head exhausts its battery power, the routing

path may be destroyed. This threatens persistent transmission, thereby reducing the packet delivery

ratio. However, if the cluster-head is associated with a poor quality link, it generates additional

retransmissions, which leads to unnecessary energy consumption.

Proposed System




In wireless sensor networks, the entire sensor nodes have to transmit the sensed data to the sink

periodically, whenever the sink is in the need of sensing report. This project proposes a link-aware

clustering mechanism, called LCM, to determine an energy-efficient and reliable routing path. The LCM

primarily considers node status and link condition, and uses a novel clustering metric called the

predicted transmission count (PTX) and additionally bandwidth, to evaluate the qualification of nodes for

cluster-heads and gateways to construct clusters. Each cluster-head or gateway candidate depends on the

PTX and bandwidth to derive its priority, and the candidate with the highest priority becomes the

cluster-head or gateway [1].

Advantages

LCM determine proper cluster-head by using novel parameter CTX value

The cluster-head is changed according to the source and destination and also the node with

highest CTX value is selected as the cluster head.

Avoid retransmission by considering link quality while choosing the cluster head and gateway.

IV. OBJECTIVE

The objective of this project is to determine the energy-efficient and reliable routing path using Link

Aware Clustering Mechanism.

Scope of the Work

This work had implemented an Energy LCM using CTX in a wireless sensor network with static nodes.

This scheme had provided improvement gains in Energy efficiency, Throughput, Delay, Bandwidth and

Delivery Ratio. But the superior nature of this scheme depends on many environmental factors, such as

operation scenarios, specific data types etc. Thus more research work needs to be done in future to find

the respective application scenarios for this scheme with all the related factors taken into consideration.

This technique needs to be implemented in a wireless sensor network with mobile nodes, since mobility

was not taken into account in this work.

V. SYSTEM ARCHITECTURE

We propose a link-aware clustering mechanism called LCM, to determine an energy efficient and reliable

routing path. The LCM primarily considers node status, link condition, capable transmission count (CTX)

and bandwidth to evaluate the qualification of nodes for cluster-heads and gateway to construct clusters.

Each cluster-head or gateway candidate depends on the CTX to drive its priority, and the candidate with

the highest priority becomes the cluster-head and gateway.

CTX using LCM Architecture, first calculate the CTX (Capable Transmission Count) value for the entire

node. Based on the CTX value each node will act as Cluster head or Gateway for completes the

transmission to sink. Source sensor would act as Cluster head and forming a cluster with in the 200

meter coverage. Coverage areas nodes are become Gateway ready state, high CTX value node will act as

Gateway node for the transition. Again finding the neighbor node (200 meters), this time coverage areas

nodes are become Cluster head ready state. Based on the high CTX value node will act as Cluster head to

precede the transition. Its a recursive process to find each node state based on the CTX value, first

process time high CTX value become Gateway and second process time become cluster head, this process

will be carried out till reach the destination. Each time cluster would be formed to identify the energy-

efficient path to transmit the date in to sink. The challenges in the hierarchy of: detecting the relevant

quantities, monitoring and collecting the data, assessing and evaluating the information, formulating

meaningful user displays, and performing decision-making and alarm functions are enormous. The

information needed by smart environments is provided by Distributed Wireless Sensor Networks, which

are responsible for sensing as well as for the first stages of the processing hierarchy.




A. Capable Transmission Count

Although random selection is an effortless strategy to determine CH and GW nodes, it is not an efficient

approach because of its disregard of node status and link condition. Moreover, using only a single factor

cannot expose the influence of other factors on routing performance. The proposed LCM considers node

status and link condition, and proposes a novel metric, called the predicted transmission count (PTX), to

evaluate the suitability of CH or GW candidates. The PTX represents the capability of a candidate for

persistent transmission to a specific neighboring node. This study considers the transmit power, residual

energy, and link quality to derive the PTX of CH or GW candidate. A large PTX value indicates a high

likelihood of becoming a CH or GW node. Because the channel condition of wireless links varies with

time, the link reliability often depends on the channel condition. If a node is associated with an unreliable

link, data delivery is likely to fail, thereby leading to packet retransmissions.

B. Cluster State Transition

The cluster state transition of the proposed LCM, upon receiving messages, a node uses algorithm 2 to

determine whether it must change its current state. For the lack of space, this paper uses the IN node as

an example to explain the state transition of LCM When an IN node receives messages from either a CH

node or a GW node, it changes its cluster identifier as that of the sender, because this IN node and the

sender belong to the same cluster. If the sender is a CH node, the IN node then transits its state to

GW_R. Otherwise, the IN node then transits its state to CH_R if the sender is a GW node. Meanwhile, the

IN node enters the contention procedure. When the IN node receives messages from either a CH node

or a GW node, it changes its cluster identifier as that of the sender, because this IN node and the sender

belong to the same cluster.

Source

sensor

Cluster

formation

Cluster

node sates

Gateway

Node

Neighbor node

states

Cluster

head

Destination

Until reach the Destination

Cluster

head




If the sender is a CH node, the IN node then transits its state to GW_R. Otherwise, the IN node then

transits its state to CH_R if the sender is a GW node. Meanwhile, the IN node enters the contention

procedure to calculate its priority and determine its ultimate state. If the node becomes a CH or GW node,

it then forwards the received message.

C. LCM Operation

The proposed LCM is a fully distributed mechanism because the CH and GW candidates self-determine

whether they must become the CH and GW nodes, respectively. After deployment, all nodes are in the IN

state. When Source node(S) intends to send a message to the sink, it checks the cluster states of all its

neighbors. Because node S has no CH neighbors, it consequently becomes a CH node. When receiving

messages from node S, all neighbor nodes enter the GW_R state because they are in the IN state, and then

each node performs a calculation to determine if it can become a GW node. Then the selected GW node

sends out the received packet when its waiting time expires. When receiving the message, GW nod send

out the message. On receipt of the message from GW, its neighbor nodes enter the CH_R state and then

each node performs a calculation to determine if it can become a CH node. This process will continue

until reach the sink node [1].

VI. SCREENSHOTS

Program has been implemented in NS2 simulation software, find the below screenshots for the execution

of CTX using LCM Architecture.

Execution of Smart LCM




Input for calculating Smart LCM

Data send from Source to Sink using Smart LCM




Detailed view of Data transmission from Source to Sink

Analysis graph of Proposed and Existing System




VII. CONCLUSION AND FUTURE ENHANCEMENT

Paper has proposed a link-aware clustering mechanism, called LCM, to provide energy efficient routing in

wireless sensor networks. The LCM introduces the predicted transmission count (PTX) and bandwidth to

assist in constructing cluster structures. The PTX represents the level of report quality that nodes can

support and is derived from the transmit power consumption, residual energy, and link quality. The

bandwidth is used to say the rate at which a node can transfer the data. The key concept of the LCM is to

use the PTX and the bandwidth as a primary clustering metric to determine a priority for each CH or GW

candidate. Based on the derived priority, the LCM can select the best nodes to become cluster-heads or

gateways. As a solution, the LCM considers node status (i.e., energy usage) and link condition (i.e., ETX

value) and bandwidth to efficiently construct a persistent and reliable routing path to guarantee the

report quality. Simulation results confirm that the proposed LCM achieves a better packet delivery ratio,

energy consumption, and delivery latency than the original PC technique, the PC with only the link

quality, and the PC with only residual energy. On-going research is investigating the practicability of

using the LCM for data gathering in WSNs.

This scheme had provided better life time of the sensor node to choose proper nodes for sending the data

to sink. But the superior nature of this scheme depends on many environmental factors, such as

operation scenarios, specific data types etc. More research work needs to be done in future to find the

respective application scenarios for this scheme with all the related factors taken into consideration.

VIII. REFERENCES

[1] LCM: A Link-Aware Clustering Mechanism for Energy-Efficient Routing in Wireless Sensor

Networks - Sheng-Shih Wang, Member, IEEE, and Ze-Ping Chen Vol. 13,no. 2,Feb.2013

[2] An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks Seema

Bandyopadhyay and Edward J. Coyle , School of Electrical and Computer Engineering , Purdue

University ,West Lafayette, IN, USA, in proceedings of IEEE INFOCOM, 2003

[3] HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad-hoc Sensor Networks

Ossama Younis and Sonia Fahmy Department of Computer Sciences, Purdue University 250 N.

University Street, West Lafayette, IN 479072066, USA

[4] C.-H. Tsai and Y.-C. Tseng, A path-connected-cluster wireless sensor network and its formation,

addressing, and routing protocols, IEEE Sensors J., vol. 12, no. 6, pp. 21352144, Jun. 2012.

[5] Wireless Sensor Networks: Application - Centric Design", book edited by Geoff V Merrett and Yen

Kheng Tan ISBN 978-953-307-321-7, 492 pages, Publisher: InTech, Chapters published December

14, 2010

[6] Wireless Sensor Networks: Architectures and Protocols Hardcover by Edgar H. Callaway

Published by Taylor Francis Inc, 2003. ISBN 10: 0849318238

[7] Wireless Sensor Networks Edited by Suraiya Tarannum, ISBN 978-953-307-325-5, 342 pages,

Publisher: InTech, Chapters published June 30




[8] Scoping in Wireless Sensor Networks A Position Paper - Jan Steffan Ludger Fiege Mariano Cilia

Alejandro Buchmann - Department of Computer Science - Darmstadt University of Technology

D64289 Darmstadt, Germany, July 2004.

[9] http://www.intechopen.com/books/authors/wireless-sensor-networks-application-centric-

design/wireless-sensor-network-for-disaster-monitoring.

[10] http://en.wikipedia.org/wiki/Wireless_sensor_network

[11] http://www.ni.com/white-paper/7142/en/

[12] http://www.ece.gatech.edu/research/labs/bwn/actors/publication.html

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Smart LCM for Energy-Efficient Mechanism Using CTX In Wireless Sensor Network