A Survey Paper on Reduction in Loss of Signals in Wireless Sensor Network Using Data Aggregation Techniques

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1 International Journal for Modern Trends in Science and Technology

A Survey Paper on Reduction in Loss of Signals in Wireless Sensor Network Using Data Aggregation Techniques

Vishakha Varma¹ | Komal Shinde²| Rohini Shinare³| Sangita Varpe⁴| Prof. M D Ingle⁵

1,2, 3,4,5 Department of Computer Engineering, Jayawantrao Sawant College of Engineering , Pune, Maharashtra, India

To Cite this Article

Vishakha Varma, Komal Shinde, Rohini Shinare, Sangita Varpe, Prof. M D Ingle, “A Survey Paper on Reduction in Loss of Signals in Wireless Sensor Network Using Data Aggregation Techniques”, International Journal for Modern Trends in Science and Technology, Vol. 02, Issue 12, 2016, pp. 1-6.

A wireless sensor network is a group of special transducers with a communications infrastructure for monitoring and recording conditions at different locations. Commonly monitored parameters are temperature, humidity, pressure, wind direction, speed illumination, vibration intensity and sound intensity, power-line voltage, chemical concentrations, pollutant levels and necessary body functions. The main aim of data aggregation algorithms is to gather and aggregate data in an energy efficient way so that network lifetime is enhanced. The proposed algorithm based on reducing amount of loss of signals between sink node and one or more Data Aggregation Nodes(DAN).In our Literature we proposed Jacobi Decomposition Algorithm which reduces energy consumption and enhance network lifetime.

KEYWORDS:Wireless Sensor Network, DAN, Data Aggregation, Energy Efficiency, Network Lifetime

I. INTRODUCTION

Wireless sensor networks is used for many applications including military surveillance, facility and environmental monitoring. WSNs have a large number of sensor nodes with the ability to communicate among themselves and also to an external sink. The sensors could be spread randomly in harsh environments such as a battlefield. The sensors coordinate among themselves to form a communication network such as a single multi-hop network or a hierarchical organization with different clusters and cluster heads. The sensors periodically sense the data, process it and transmit it to the sink.

Data gathering is defined as the ordered collection of sensed data from multiple sensors to be finally transmitted to the sink for processing.

Since sensor nodes are energy constrained, it is not efficient for all the sensors to transmit the data directly to the sink. The amount of data generated in sensor networks is unusualfor the sink to process. Hence, we need methods for combining data into high quality information at intermediate nodes which can minimize the number of packets transmitted to the sink resulting in preservation of energy and bandwidth.

Data aggregation is defined as the process of aggregating the data from many sensors to exclude redundant transmission and provide combined information to the sink. Data aggregation usually involves the union of data from many sensors at intermediate nodes and transmission of the aggregated data to the sink.

In this paper we introduces multiple data aggregation techniques such as Jacobi ABSTRACT

International Journal for Modern Trends in Science and Technology

Volume: 02, Issue No: 12, December 2016

ISSN: 2455-3778 http://www.ijmtst.com

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Signals in Wireless Sensor Network Using Data Aggregation Techniques Decomposition, compressed sensing (CS)

[1],principal component analysis (PCA)[10], Second-Order Data Coupled Clustering (SODCC), Compressive-Projections Principal Component Analysis (CPPCA) [2],distributed type-2 fuzzy based self-configurable clustering (SCCH)[3], adaptive aggregation scheme referred to as (A-HDACS)[4],data density correlation degree (DDCD)[5], iPC3 and oPC3[6],amplify-and-forward compressed sensing (AF-CS)[7],Consensus Based Distributed Principle Component Analysis (CBDPCA) [9], MLR(Maximum Lifetime Routing)[11],Grid-based Routing and Aggregator Selection Scheme(GRASS)[13],energy-aware spanning tree algorithm (E-Span)[14], Energy-efficient and Secure Pattern-based Data Aggregation protocol (ESPDA)[15].

This paper includes various fields. Field II consist Literature Review carried out for this paper.

Field III shows Problem statement.Field IV shows proposed method. Field V concludes the paper.

II. LITERATURE SURVEY

The authors in various papers proposes multiple algorithms that are used to achieve different parameters those are as follows. Clustering approaches, principal component analysis (PCA) and compressed sensing (CS) strategies. It Reduces power consumption, energy consumption and boost scalability of the network[1].Second-Order Data Coupled Clustering (SODCC) and Compressive-Projections Principal Component Analysis (CPPCA) algorithm. The aim of this combination is to increase energy efficiency and network lifetime without sacrificing the data quality[2]. Distributed type-2 fuzzy based self-configurable clustering (SCCH) mechanism [3]

propose a self-configurable clustering mechanism to detect the disordered CHs and replace them with other nodes. It is used to prolong the network lifetime and it reduces data loss. Adaptive aggregation scheme referred to as (A-HDACS)

dynamically determines sparsely values based on signal variation in local regions[4]. Data density correlation degree (DDCD) clustering method which have a lower data distortion and useful for the application where the sensor nodes are densely deployed and the data change slowly with time [5].

IPC3 and OPC3 models to optimize amount of energy use for data transmission[6].

Amplify-and-Forward Compressed

Sensing(AF-CS), in order to increase the existing trade off among reconstruction error, energy consumption, and resource utilization[7].

Consensus Based Distributed principle Component Analysis (CBDPCA) which is based on finding the eigenvectors of local covariance matrices[9]. Principle Component Analysis (PCA)to reduce the amount of data traffic in WSNs[10].An optimal routing and data aggregation technique for wireless sensor networks. The objective is to increase the network lifetime by jointly optimizing data aggregation and routing[11].thus save the limited energy [12]. Grid-based Routing and Aggregator Selection Scheme (GRASS) increases the network lifetime by utilizing data aggregation and in-network3D-Zigzag sorting is an aggregation technique wasproposes that can reduce the traffic inside WSN and processing techniques and system lifetime with capable levels of latency in data aggregation and without losing data quality[13].

E-Span, which is an energy-aware spanning tree algorithm is a distributed protocol and sources are facilitates within an event region to perform data aggregation. It extends the lifetime of sources significantly and preserves a low average packet transfer delay and a high packet delivery ratio [14].Energy-efficient and Secure Pattern-based Data Aggregation protocol (ESPDA) is energy and bandwidth effective because cluster-heads prevent the transmission of redundant data from sensor nodes. It increases the energy and bandwidth efficiency the protocol minimizes the number of packets transmitted [15].

TABLE1.LITERATURE SURVEY TABLE

Paper

no. year Paper Name (Author

Name) Techniques/Algorithm Advantages Disadvantages Parameters

Achieved 1. 2016 Data Aggregation and

Principal Component Analysis in WSNs ( Antoni Morell )

Use clustering approaches, principal component analysis (PCA) and/or compressed sensing (CS) strategies

Reduces power

consumption When transmitted data is analyzed computational effort

& signaling required to find eigenvectors basis is not rewarded

Boost scalability of network

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Signals in Wireless Sensor Network Using Data Aggregation Techniques

Reduce energy

consumption with a reduction in the energy consumption 2. 2016 Energy Efficiency and

Quality of Data Reconstruction Through Data-Coupled Clustering for

Self-Organized Large-Scale WSNs.

(Mihaela I. Chidean)

Use Second-Order Data-Coupled

Clustering (SODCC) and Compressive-Projections Principal Component Analysis (CPPCA) algorithm combination

Increase energy

efficiency No preservation of the balanced performance of the SODCC algorithm in rapidly changing environments

Increase energy efficiency

Increase network lifetime

3. 2015 An Alternative Clustering Scheme in WSN.

(Davood Izadi)

Use distributed type-2 fuzzy based

self-configurable clustering (SCCH) mechanism

Prolong network lifetime

No coverage preservation, because complete coverage of the monitored area over long period of time

Increase network lifetime

Reduce data loss 4. 2014 Adaptive Hierarchical

Data Aggregation using Compressive Sensing (A HDACS) for Non-smooth data field (X. Xu)

CS(Compressive Sensing) yield poor compression Efficiency hence we propose Adaptive Hierarchical Data Aggregation using Compressive Sensing (AHDACS)

Improvement in energy efficiency

Cluster size is fixed.To improve communication cost cluster size should depend on local density of the nodes

Energy Efficiency, Accuracy in signal Recovery

improvement in accuracy in signal recovery 5. 2014 Data Density

Correlation Degree Clustering Method for Data Aggregation in WSN.

(F. Yuan)

Recent spatial

correlation models are not appropriate for measuring the

correlation in a complex environment hence we proposed data density correlation degree (DDCD) clustering method which have a lower data distortion

More energy

efficient In data transmitting process ,the energy of sensor nodes not considered to construct an energy balanced networks

Energy Efficiency, data

representation performance Useful for the

application where the sensor nodes are densely deployed

6. 2014 Advanced Principal Component-Based Compression Schemes for Wireless Sensor Networks.

(C. Anagnostopoulos)

To improve PC3 we proposed two models iPC3 and oPC3 models to optimize amount of energy use for data transmission

Manage to extend lifetime of WSN

recovering schemes are slow in the event of an upstream node failure

Lifetime of WSN

Energy Efficiency

Improving energy efficiency 7. 2014 Amplify-and-Forward

Compressed Sensing as an Energy-Efficient Solution in Wireless Sensor Networks.

(J. E. Barcelo-Llado)

We used

amplify-and-forward compressed sensing (AF-CS) to improve the existing tradeoff among reconstruction error, energy consumption, and resource utilization

reduce the number of transmissions

sensor readings are

space-correlated energy efficiency

Robustness against noise Reduce the

number of channel uses 8. 2011 Distributed

Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks.

(Y. Ma)

An –local spatial clustering algorithm.

The algorithm can construct a dominating setas the sensor network backbone to realize the data aggregation

The aggregated network can provide the environmental information in very high accuracy

Overloading of information when monitoring and analyzing large data sets

higher accuracy

9. 2010 Consensus-Based Distributed Principal Component Analysis in Wireless Sensor Networks.

(S. Valcarcel-Macua)

We present two

algorithms CBDPCA and CB-EM-DPCA which are the first solutions for fully distributed PCA computation.

PCA is a powerful technique for data analysis and

compression

Uses only single-hop communications among neighbour

Data analysis and Data Compression

Provides a flexible trade-off between the desired

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performance and the resulting energy expenditure 10. 2009 Multivariate Reduction

in Wireless Sensor Networks.

(O. Silva)

PCA(Principle

Component Analysis) to reduce the amount of data traffic in WSNs

Reducing the amount of traffic in the network

Less evaluation of the reduction impact in the network behavior

Due to low errors ,reduction in energy

consumption and delay, reducing the amount of traffic in the network it maintain data representativeness Considering

energy consumption and delay, the solution proposed presented good results 11. 2008 Optimal Routing and

Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks.

(C. Hua)

Distributed Algorithm: It can converge to the optimal value efﬁciently and calculate the amount of reduction

maximize the network lifetime by jointly

optimizing data aggregation and routing

multiple sink nodes and for nodes with sleeping mode are not used

Reduce the data trafﬁc

Improve the network lifetime MLR(Maximum Lifetime

Routing): has better aggregation results and record the lifetimes of all nodes

12. 2008 3D-DCT Data

Aggregation Technique for Regularly Deployed Wireless Sensor Networks.

(F. Bai)

3D-Zigzag sorting algorithm makes sure that the aggregation point transmits the frequency coefficients from lower frequencies which contain main energy of the original data to higher frequencies

Prolong lifetime

of WSN majority of energy consumed in WSNs is for transmitting data inside the network

Prolong lifetime of WSN

Reduce traffic inside WSN

13. 2008 Data aggregation and routing in Wireless Sensor Networks:

Optimal and heuristic algorithms.

(J. N. Al-Karaki)

Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality

Maximize network lifetime

Node energy

constraint Maximize network lifetime

Limited computing resources

Achieve low energy dissipation &

low latency 14. 2005 An Energy-aware

spanning tree algorithm for data aggregation in wireless sensor networks.

(Marc Lee)

E-Span, which is an energy-aware spanning tree algorithm

the average node lifetime for sensors using E-Span is higher than that of the Directed Diffusion

More Simulation

time is required Average node lifetime

High packet delivery ratio

maintains a low average packet transfer delay and a high packet delivery ratio

15. 2003 ESPDA:

Energy-efficient and Secure Pattern- based Dataaggregation for

ESPDA( Energy-efficient and Secure

Patten-based Data Aggregation protocol)

Energy and bandwidth efficient

Results are generated only for small networks

Data redundancy increases

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wireless sensor network.

(H. cum)

Secure because it does not require the encrypted data to be decrypted by

cluster-heads to perform data aggregation

Increases the number of packets transmitted

III. PROBLEM STATEMENT

There is big issue in wireless sensor network, it is not possible for all the sensors to transmit the data directly to the sink since sensor nodes are energy constrained. It requires more energy and more numbers of transmissions. If each sensor node sends data to the sink node individually. Generally source nodes have limited available energy and sink node has unlimited available energy.

Sometimes while transmitting data from source node to sink node, source node gets destroyed. It leads to losses of signals hence improper transmission of data occurs. This problem is shown in fig.1.

Figure1.Data Transmission

IV. PROPOSED METHOD

Data aggregation attempts to collect the most criticize data from the sensors and make it available to the sink in an energy efficient way with minimum transmissions. It is critical to develop energy efficient data aggregation algorithms so that lifetime of network is improve. There are various factors which determine the energy efficiency of a sensor network such as architecture of network, the data aggregation mechanism and the underlying routing protocol. In this paper, we describe the manipulation of these factors on the energy efficiency of the network, lifetime of network in the context of data aggregation.

Figure2.Cluster with data aggregation node

We proposed Jacobi Decomposition Data Aggregation Method to send the data from n number of source nodes to sink node through data aggregation node(DAN). There are various Data Aggregation Nodes in which we aggregate the data from multiple source nodes. Compression of readings and signaling takes place at Data Aggregation Nodes. This algorithm reduces losses of signals and number of transmissions while transmitting data from source nodes to sink node.

V. CONCLUSION

The main motivation of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is increased. Wireless sensor networks (WSN) offer an increasingly Sensor nodes need less power for processing as compared to transmitting data. The proposed algorithm based on reduced transmission between sink node and one or more Aggregation nodes. Aggregation reduces the amount of network traffic which helps to reduce energy consumption on sensor nodes. . Thus, while data aggregation improves energy efficiency of a network, it complicates the existing security challenges.

The proposed algorithm based on reducing amount of loss of signals between sink node and one or more Data Aggregation Nodes(DAN).In our Literature we proposed Jacobi Decomposition

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Signals in Wireless Sensor Network Using Data Aggregation Techniques Algorithm which reduces energy consumption and

enhance network lifetime. Furthermore, when the total amount of transmitted data (compressed readings and signalling information) is analysed, the conclusion is that the computational effort and signalling required to find the eigenvectors basis is not rewarded with a reduction in the energy consumption.

REFERENCES

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[3] Davood Izadi, Jemal Abawajy, and Sara Ghanavati,

“An Alternative Clustering Scheme in WSN”, IEEE sensors journal, VOL. 15, NO. 7, July 2015.

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[9] S. Valcarcel-Macua, P. Belanovic, and S. Zazo,

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