fault-tolerant identification in wireless sensor networks for

Fault-Tolerant Identification in Wireless Sensor Networks for Maximizing

System Lifetime

Middela Shailaja, Department of CSE,

SR Engineering College, Warangal,

India. Email: [email protected]

AnandaRaj S.P,

Department of CSE,


India.

Email: [email protected]

Poornima.S,

Department of IT,


India.

Email: [email protected]

Abstract

Wireless Sensor Network (WSN) is used by many

applications such as security, command and control and

surveillance monitoring. In all such applications, the

main application of WSN is sensing data and retrieval of

data. There are many WSN systems that are query

based. They give responses in a stipulated time based on

the user’s query word. However, the WSN has possible

sensor faults for it is not reliable and thus the network

energy level goes down. It results in reduction of lifetime

of network. To overcome the fault tolerance mechanisms

can be used to improve reliability of the finding failure

nodes and recovered by cluster heads. This paper

presents an algorithm that can effectively increase

lifetime of WSN besides satisfying the QoS requirements

of application. Such algorithm is adaptive and also fault

– tolerant. It uses path and source redundancy and based

on hop-by-hop data delivery. Empirical simulation

results revealed that the proposed system is feasible. This

system also proposed the authentication of all kinds of

identified faults and provides the services in quality

manner. It increases the data flow and reduces the faults.

Index Terms—WSN, QoS, query processing, energy

efficiency, network life time, fault tolerant, data

aggregation, data flow.

1. INTRODUCTION

Of late Wireless Sensor Networks (WSNs) became

popular for many applications such as industrial space,

military and security. Mostly they are used in surveillance monitoring and security applications. The

WSN is of two types basically. They are known as

query based WSN and source-driven WSN. Data flow

in WSN is the basis for the classification. In the

former data transmission is initialized by sensor while

in the latter, the data transmission is the result of user

query. Strict QoS requirements are associated with

user-based WSN and also expect best conservation of

energy. User query is targeted to select sensor only.

Query processing with completely satisfying QoS

requirements is a challenging problem. Recently it has

been addressed mostly in [2], [5], [1], and [6]. The

solution is generally applying redundancy. In this

paper we are interested in applying redundancy not

only for satisfying QoS requirements but also

timeliness and reliability requirements in case of query

based WSN. The proposed system also aims at

increasing lifetime of network besides satisfying QoS

requirements by using optimal redundancy level. In

order to achieve this, we use both path and source level redundancy. The algorithm proposed by is

known as AFTQC (Adaptive fault Tolerant QoS

Control). It is meant for achieving QoS requirements

and also ensuring conservation of energy in WSN.

2. RELATED WORK

The research work on WSN for achieving QoS

requirements by applying redundancy is divided into

three categories. They are known as reliability

assurance QoS, application specific QoS [2], and end

to end QoS. End to end QoS requirements

implementation is not feasible due to the nature of

WSN and its complexity. SAR (Sequential Assignment Routing) [5] is the kind of WSN that uses

path redundancy and ensures increase in lifetime of

network and also meeting QoS requirements.

However, reliability issue is not considered by the

algorithm. To overcome this problem ESRT [11] was

proposed. And to address end-to-end reliability issues

ReInFor.M is proposed [1]. The algorithm has

different allocation strategy of network resources and

also aware of adaptability to channel errors. Multiple

copies of a packet are sent by the protocol to multiple

paths to ensure reliable delivery of packet. It makes

use of dynamic concept to find required paths and

number of copies of the packet. With high probability,

the number of edge-disjoint paths between nodes equal

to average node degree is observed by protocol. The

protocol uses thin band and its disjoint paths. M.

Perillo et al. presented an approach which maximizes

network lifetime while achieving minimum reliability. It is achieved by using both concepts such as finding

paths and scheduling active sensor sets. The time used

by all sensors sets is calculated as life time of WSN.

Middela Shailaja et al ,Int.J.Computer Technology & Applications,Vol 3 (5), 1752-1757

IJCTA | Sept-Oct 2012 Available [email protected]

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mailto:[email protected]



The approach used here is to turn off many sensors for

some time so as to conserve energy. However, it is at

the cost of reliability. As it uses intelligent scheduling

it can reduce power consumption and thus extend

network life time. The downside of this is that its QoS

is confined to reliability only and it is not scalable.

This algorithm for dynamic adjustment of number of

sensors uses mathematical paradigm. The aim of the

approach is to increase life time of network by scheduling sensors and at the same time ensuring

enough number of sensors is powered. Reliability and

timeliness are not considered in QoS metrics for data

delivery. For improving life time of WSN, clustering

also can be used [7], [10]. This is because the

clustering can support data aggregation and

forwarding at cluster heads and reduce contention on

wireless channels [4]. In hostile environments REED

[8] reduces the failures of SNs by using redundancy.

Finally our approach is based on the path, source

levels. It is aimed at satisfying timeliness and

application reliability requirements and maximizing

lifetime of WSN. Multiple sensors’ usage represents

source level redundancy while the multiple paths

usage is represented by path level redundancy. The

proposed system provides redundancy at best level to

satisfy QoS requirements besides increasing life time of WSNs.

3. SYSTEM MODEL

The cluster based WSN is used for the experiments of

this work. The architecture is as shown in fig. 1.

Fig. 1: Architecture of cluster – based WSN

As can be seen in fig. 1, the cluster based WSN is

constructed. The network is made up of a set of

clusters and each cluster is made up of sensor nodes

and also clusters head. The communication between

sensors and base station is done. However, the cluster

header determines which sensor node has to

communicate with base station.

4. MODULES

A. General Approach

In this method we are also interested in applying

redundancy to satisfy application specified reliability

and timeliness requirements for query-based WSNs.

Moreover, we aim to determine the optimal

redundancy level that could satisfy QoS requirements

while prolonging the lifetime of the WSN.

Specifically, we develop the notion of “path” and

“source” level redundancy. When given QoS

requirements of a query, we identify optimal path and source redundancy such that not only QoS

requirements are satisfied, but also the lifetime of the

system is maximized. We develop adaptive fault

tolerant QoS control (AFTQC) algorithms based on

hop-by-hop data delivery to achieve the desired level

of redundancy and to eliminate energy expended for

maintaining routing paths in the WSN. Based on the

approach now we go for uploading the files to identify

the quality of service from server. fig[3] represents the

uploading and size of the file and content of the file.

Fig. 2: Representation for uploading the files from server

Here we are transferring those three files to by

selecting nodes .every node will send one file to

cluster head. At Cluster head the three files were

aggregated. The processing center is a centralized

control of firewalls. Then cluster head send all data to

processing center.



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Fig. 3: Screen shot for transferring files from server to client via

cluster region

B. Data Flow from Server to Client

Fig. 4: View on Data flow from server to client

The processing center is a centralized control of

firewalls. Then cluster head send all data to

processing center. Here the cluster head aggregated

with the data in the form of three files.

Fig.5. Data aggregation view from server

Once three files reached the cluster head displays the

data aggregate in the server transaction.

C. Speed violation due to data aggregation

Fig.6. Finding speed violation

Fig.6. describes the speed violation with all contents

of the actions involved in user interface application.

D. Verification of the source file in Client

Here we start the verification of received file from

source server and go for source redundancy, Number

of sensor nodes per cluster in response toa query, sensor nodes are used for returning sensor readings. If

we consider both hardware and software failures of

sensor nodes, the system will fail if the majority of

sensor nodes does not return sensor readings (due to

hardware failure), or if the majority of sensor nodes

returns sensor readings incorrectly (due to software

failure). Assume that all sensor nodes have the same

software failure probability, denoted by sensor node

reading software failure probability. Also assume

that all sensors that sense a given event make the same

measurements. That the probability of the majority of

number of sensor nodes per cluster in response to a

query, sensor node failing to return sensor readings

due to hardware failure, and the second expression is

the probability that the majority of number of sensor

nodes per cluster in response to a query, sensor

nodes returning sensor readings but no majority of

them agrees on the same sensor reading as the output

because of software failure.



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Fig.7. Verifying Requested Files

Fig.8. Verified files from client source

The above schematic view shows the user is verifying

the files for knowing which user want which file.

Based upon t the admin send files to the clients.

E. Authentication for client transaction

Fig.9. Checking authentication

Once the verification is completed the client is ready

to download the file, it is checking the user id and

password. If the user id and password were

authenticated then only user can download file.

5. EVALUATION

This section presents the optimal set of values that

maximize the sensor system.Fig.10 shows parameters

and the default values. The parameters and the default

values were used in sensor networks. Accordingly here

the WSN invokes the transaction through three nodes

and 1000 sensor nodes are distributed in 400 m by 400

m. The transmission radio range of each SN is 40 m.

Fig.11. User receiving data

In the below figure 12 represents the user receives the

data as packets. Before that, enter the IP address. Then

only user can receive data as packets.

Fig.12.Representation of user received files data successfully

Finally user received three files data. the following

figure 13 shows the downloading complete data from

client side.



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Fig.13. Downloaded content to view

Here user downloaded a file that will display the

content of the file.

6. CONCLUSION

This paper introduced an algorithm that runs in nodes of WSN that is meant for improving QoS

requirements of an application and also reduces

consumption of energy usage of wireless network.

This is significant as the WSN nodes have limited

resources available. The proposed WSN is the

algorithm is fault-tolerant identification and improves

the QoS Control which delivers packets in “hop-by-

hop” fashion. With a real time constraint the

probability of successful data delivery of proposed

approach is more. At the same time the energy

consumed by WSN nodes is also reduced. When

certain parameters are set, we identified optimal

setting that maximized MTTF and satisfying QoS

requirements.

7. REFERENCES

[1] K Sohrabi, J. Gao, V. Ailawadhi, and G. Pottie, “Protocol for

Self-Organization of a wireless Sensor Network,” IEEE Personal Communications, October 2000, pp. 16-27.

[2] W. Heinzelman, C. Chandrakasan and H. Balakrishnan, “An

Application-Specific Protocol Architecture for Wireless

Microsensor Networks,” IEEE Transactions on Wireless Communication, Vol. 1, No. 4, 2002, pp. 660-670.

[3] M Perilo, and W. Heinzelman, “Providing Application QoS

through Intelligent Sensor Management,” 1st IEEE International Workshop on Sensor Network Protocols and Applications, May

2003.

[4] R. Iyer, and L. Kleinrock, “QoS Control for Sensor Networks,”

IEEE Conference on Communications, May 2003.

[5] Y. Sankarasubramaniam, O. B. Akan, and I. F. Akyildiz, “ESRT:

Event-to-sink Reliable Transport in Wireless Sensor Networks,” 4th ACM International Symposium on Mobile Ad Hoc Networking and

Computing, Annapolis, MD, USA, June 2003,pp.177–188.

[6] B. Deb, S. Bhatnagar and B. Nath, “ReInForM: Reliable

Information Fowarding using Multiple Paths in Sensor Networks”,

28th Annual IEEE Conference on Local Computer Networks, Bonn,

Germany, Oct. 2003 35.

[7] O. Younis, S. Fahmy and P. Santi, “Robust Communication for

Sensor Networks in Hostile Environments,” 12th IEEE International Workshop on Quality of Service, June 2004, pp. 10-19.

[8] D. Chen, and P. Varshney, “QoS Support in Wireless Sensor

Networks: A Survey,” International Conference on Wireless

Networks, Las Vegas, Nevada, USA, June 21-24, 2004.

[9] O. Younis and S. Fahmy, “HEED: A Hybrid Energy Efficient,

Distributed Clustering Approach for Ad Hoc Sensor Network”,

IEEE Transaction on Mobile Computing, Vol. 3, No. 3, October-

December 2004, pp. 366-379.

[10] J. A. Gutierrez, E. H. Callaway, Jr., and R. L. Barrett, Jr., Low-Rate Wireless Personal Area Networks, IEEE Press, New York, NY,

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[11] E. Felemban, C. G. Lee, and E. Ekici, “MMSPEED: Multipath

Multi-SPEED Protocol for QoS Guarantee of Reliability and

Timeliness in Wireless Sensor Networks,” IEEE Transactions on

Mobile Computing, Vol. 5, No. 6,June 2006, pp. 738-754.

[12] Anh Phan Speer, Ing-Ray Chen,” On Optimal Path and Source

Redundancy for Achieving QoS and Maximizing Lifetime of Query-

Based Wireless Sensor Networks,” International Symposium on Modeling, Analysis, and Simulation of Computer Systems,

September 2006 , pp. 51-60.

[13] Azadeh Forghani, Amir Masoud Rahmani,”Multi-state Fault

Tolerant Topology Control Algorithm for Wireless Sensor

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December 2008 , pp. 433-436.

[14] Jun Wen, Jie Jiang, Guofu Wu, Dongsong Ban, Wenhua Dou,”

Preserving Relay Connectivity and Coverage in Heterogeneous

Wireless Sensor Networks,”

International Conference on Networking, Architecture, and

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[15] Ing-Ray Chen,Anh Phan Speer,Mohamed Eltoweissy,

“Adaptive Fault-Tolerant QoS Control Algorithms for Maximizing

System Lifetime of Query-Based Wireless Sensor Network,” IEEE

Transaction on Dependable and secure Computing, March 2011,

pp. 161-176.

[16] Suchetana Chakraborty,Sandip Chakraborty,Sukumar

Nandi,Sushanta Karmakar,” A Tree-Based Local Repairing

Approach for Increasing Lifetime of Query Driven WSN,”

IEEE International Conference on Computational Science and

Engineering, August 2011, pp. 475-482.

8. BIOGRAPHIES

M.Shailaja received M.Sc degree in Computer

Science from University of Arts and Science

College, Warangal, A.P, India. Currently

pursuing M.tech in Computer Science and

Engineering at SR Engineering College,

Warangal, A.P, India. Her research interests

include networks and Secure computing.

S.P.Anandaraj received B.E (CSE) degree

from Madras University, Chennai in the year

2004, M.Tech (CSE) with Gold Medal from

Dr. MGR Educational and Research Institute,

University in the year 2007 (Distinction with

Honor).He is presently working as Senior

Assistant Professor in SR Engineering

College, Warangal. He has 8+ Experience.

His areas of interest include Information

Years of Teaching security and Sensor Networks. He has published



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papers in various national and International Journals, national and

International Conferences. He also attended many National

Workshops/FDP/Seminars etc. He is a member of ISTE, CSI,

Member of IACSIT and Member of IAENG.

S.Poornima received B.Tech (IT)

degree from Anna University in the

year 2005. She has 6+ years of experience in teaching field. Her

areas of interest include Neural

Networks and Wireless Sensor

Networks. She has published research papers in various National and International Journals,

National and International Conferences. She also attended

many National Seminars/FDP/Workshops Etc., She is a life

member of ISTE.



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fault-tolerant identification in wireless sensor networks for

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