WEIGHTED COMPRESSIVE SENSING WITH K-MEANS … · In recent scenario, compressive sensing based data...

WEIGHTED COMPRESSIVESENSING WITH K-MEANS

ALGORITHM IN WIRELESS SENSORNETWORKS

M. Shanmukhi1, Dr. Anitha Patil2,Amudahavel3, G. Naga Sathish4,

1,4 Department of CSE,BVRIT HYDERABADCollege of engineering for Women,

Bachupally, Hyderabad, [email protected]

2Computer Science & Engineering,Pillai Hoc College of Engineering & Technology,

HOC Colony, Taluka - Khalapur,Rasayani, Maharashtra-4102073Department of CSE, KLEF,

Vaddeswaram, Andhra Pradesh, [email protected]

June 13, 2018

Abstract

At present compressive sensing has turned to be recentresearch in the wireless sensor networks. Generally,compressive sensing can minimize the number of datatransmissions and extends the life span of the entirewireless sensor networks. It balances the data aggregationand traffic load during the nodes performance in thenetwork. For effective data distribution in the networks,the extended comb- needle model was originated for

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International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 3681-3706ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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dynamically maintaining the sensor data for pushing andpulling on data distribution in the wireless sensornetworks. However, the push-pull data distributionapproach may overload certain wireless sensor nodes andmake the hotspots to consume energy ultimately. Theseissues cause excess amount of energy loss in the network.However, in previous compressive sensing method utilizesthe data aggregation tree which has the link schedulingproblem with higher complexity. It dismisses the naturalsignal of sparsity, which varies in temporal and spatialfield. This paper proposes weighted compressive sensingmethod in the wireless random network for estimating thesuitable network in order to minimize the energyconsumption in the wireless sensor network.

Keywords:Weighted Compressive Sensing Method,Wireless Sensor Networks, Push-Pull

1 INTRODUCTION

Wireless Sensor Networks ( WSNs ) are been utilized forapplications such as health care, monitoring, domestic,surveillance systems, and disaster management [1]. Generally, Asshown in Figure 1, WSN have a enormous number of sensor nodeswith the capability to transmit the data within themselves and tobase station or sink node [2].Most of the applications needs sensorto sporadically sense and forward information to the sink fortransmitting frequently all the way through multi hop path. Suchenergy restrict sensors, once it is deployed it does not requiremaintenance at all and therefore collecting the information in theenergy efficient way is very critical process in the WSN nodes. Itis essential to maintain the less energy consumption and that isclassified into three types such as data processing, datacommunication and data sensing. Typically, sensing and dataprocessing requires a lesser amount of energy when judge againstd to the data transmission. If the transmission cost is reduced inthe WSN than the life span of the WSN will be extended.

Therefore decrease in the total energy consumption in theWSN is considered as major confront to the researchers in theWSN design [2], data collectors, network topology module and

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International Journal of Pure and Applied Mathematics Special Issue

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control data and data aggregation. The main purpose of thisstudy is to present a proper deployment of the sensor nodes (SN)for the data aggregation and routing in the SN and to improve theenergy efficiency using compressive sensing type and thatobviously extend the network life span with considering theinfluence of load balancing techniques[3].

Figure 1: Wireless Sensor Networks architecture [4]

Data aggregation utilizes the parameters of sensor nodes forcombining the cluster that attributes have been chosen and storedin an aggregated structure for future utilization. Aggregationdenotes to the mechanisms that the pattern forms the informationand data in a dimensional form which is simple to store andretrieve. The major issues is found in the recent past years wasthe higher energy consumption in the battery [5,6,7].

In recent scenario, compressive sensing based data aggregationtechniques had received the attention from researchers as thesemethods can improve the network life by decreasing the amount ofdata transmission and equilibrating the traffic load throughputthe wireless sensor networks. The efficient compressive dataaggregation ( CDA) technique is used to improve the both thenetworks life span and the data transmission cost in large-scalewireless sensor networks. The author [8] have evaluated thenetworks capability by utilizing the compressive data aggregationtechnique and demonstrated that the capacity was directlyproportionally to the sensed data and disseminate themthroughout the network utilizing a simple algorithm.

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Compressive data gathering (CDG) is best efficient approachfor the compressive sensor theory, which is used to en-route thecollected sensed data to the base node. It have received thegreater attention from the researchers, as it has the capability tominimize the overall communication cost without obtaining thetransmission overhead or intensive computation. In compressivedata gathering instead of obtaining all the readings from the nsensors, the base node would obtain only certain weighted (encoded ) sums from[9]of all the readings. Hence, the base nodewould be capable to retrieve( decode) the original information, asfar as the reading can be compressed or transformed in somesparse orthonormal transform field [9]; Thus, k symbolizes aboutthe transform field and its sparsity representation of the data.This methods have capacity to preserve the substantial energy soit obliviously improving the network life span and aims to obtainthe load balancing by distributing the communication costs toentire wireless sensor networks with the given route [10].

This paper makes an important contribution to the wirelesssensor network, by estimating the best suited network for thelower energy consumption, compressive data aggregation withhotspots elimination. It uses the compressing sensing techniquewith hybrid data distribution management for grid and randomnetwork. The experimental results demonstrated best network forthe compressive data aggregation and energy efficiency.

2 RELATED WORK

As a key communication rule, data aggregation plays a vital rolein the field of wireless networks. Several performance aspectsassociated to data aggregation have been widely discussed in thisliterature. For example, significant construction of dataaggregation routing protocol was analyzed [11], energy-efficientalgorithms in data aggregation were studied [12] effectivescheduling algorithm for reducing the delay in the dataaggregation were discussed in [13], etc.

In compressive data gathering concentrates on the datagathering snapshots. In compressive data aggregation,measurement matrix is haphazardly generated and the signal was

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sparsified by utilizing wavelet transform or discrete cosinetransform. Compressive data aggregation was not desirable in thesmall scale wireless sensor networks where the possible capacitygain was very meager and the signal sparsity would not be highenough for obtaining the signal strength. Ji et al [14] hadanalyzed about the snapshots and uninterrupted data aggregationissues in the physical inference model. For increasing networkcapacity, snapshot data aggregation employs the networksegmentation to agenda the compressive sensing procedure of eachsub-network and therefore optimal capacity of the network can bereceived; uninterrupted data aggregation applies a pipelinescheduling algorithm to accelerate the compressive sensingprocedure in the network.

Luo et al [15] had evaluated about the advantages of usingcompressive sensing to data aggregation tree construction in orderof improving the network through put. This hybrid methodseliminates the overflowing traffic conditions at leaf nodes and alsoutilize the benefits of compressive sensing. It minimize the trafficload conditions near the sink nodes and then hybrid compressivesensing can obtain the effective improvement in the throughput.The compressive sensing is depend on the data collectiontechniques demonstrated in [16] analyzed about the impacts ofsparse projection matrix produced by routing topology forachieving higher precision. While equating compressive sensing onclustered topology with compressive sensing on tree topology, itclearly illustrates that CS based clustered topology has highercompression ration and greater recovery quality in thecompressive sensing data technique [17]. According to the Lee etal [18] have investigated the influence of clustering on thecompression ratio and recovery quality and consequentlydeveloped clustered based compressive sensing approach. Majorityof the existing work have used only spatial correlation and theyconsider determined compressive sensing pattern that means thecompressive ratio is also determined.

Authors [19] had proposed a distributed compressive sensingmethod for the correlated signals. It creates a greedy algorithmdepend on joint signal recovery scheme, which rebuilds varioussignals developed by sensor nodes in a wireless sensor nodes wherethese signals are accepted to meet pre-defined joint sparsity

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prototype. In existing methods, compressive sensing determinetheir basic retrieval algorithm to greedy algorithm and linearprogramming [7]. These methods suffers from accuracy,complexity, and speed issues. Bayesian compressive sensing (BCS)is a method, which uses statistical characterization of the signal tocomplement the conventional approach.

According to the Gong et al [20] have demonstrated about thedistributed and centralized clustering algorithms for wirelesssensor networks. It forwards the data from the group heads to thebase node through a backbone structure tree applying a hybridcompressive sensing mechanism. Moreover, the process hasdismissed the truth that the sparse random computation can beused in each group to reduce the total number of forwardingpackets. They had deals with the problems and represents aeffective solution, clusterbased data aggregation scheme withsparse random computation in a star topology based wirelesssensor networks. Therefore, this topology is applied in each groupthat causes higher energy consumption in the intra-cluster

Shanmukhi et al[21] have demonstrated about the cluster-basedComb- needle model for improving the energy efficiency in the dataaggregation method. It have minimized the communication costand wastage of resources in wireless sensor networks. Sensor nodesuses the distributed data bases, whereas the required data is aloneretrieved in the networks.

Thus the survey of the security scheme on the dataaggregation and more energy consumption process were estimatedtheir mechanism and the drawbacks, the proposed method wouldbe efficient than all methods and overcome those drawbacks.

3 METHODOLOGY

In this proposed method, we are evaluating about the clusterbased comb-needle model in grid network as well as randomnetwork to identify the energy efficient data aggregation inwireless sensor networks. By using the k means algorithm , it canclassify the region or blocks in the grid as well as in the randomnetworks. We have consider certain assumption in the proposedmodel for describing the performance of comb needle model in the

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grid and random networks. Hence, we assume a wireless sensornetworks made up of n stationary and location-aware sensornodes, which are disseminated haphazardly in the network.

3.1 MODEL DESCRIPTION

To make it simple, the network topology is presented as a n x ngrid, where the top-left node is the reference node (0,0) and theremaining nodes are with coordinate (a, b) as represented in givenfig. In our proposed model, Initially the wireless sensor network willbe grouped depend on the cluster based comb-needle model. Eventhough various clustering methods are present, in this research weutilize the K- means algorithm for classifying the region in the gridand random networks.

To precise the network model, we consider few assumption inthe proposed model as follows:

1. N wireless SN are randomly distributed in the square fieldregion (nxn)

2. All the sensor nodes have the common transmission cost andtransmission power.

3. Each sensor node is cognizant of its location in thegeographical region, that can be received via connectedGeographical Positioning system (GPS) or using some otherlocalization method. The location data is utilized in thedistributed system.

4. The network communication is carried out using the singlehop

5. In this model, the communication is symmetric and a singlesensor can calculate the exact distance depend upon thereceived signal strength, in case the transmission power isprovided.

6. During oversampled compressive sensing of source codingstrategy, there is no transmission errors occurs in thesystem.

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7. Sensor nodes turn to be aggregator, if the size of the data isbigger than the desired one.

In our research after considering all these assumption, we utilizethe K-means algorithm which is a effective way to gather the dataset and form the cluster with the present block number k. In theK-means algorithm, at first is used to determine K clusters headsfor the K regions. Afterwards, it is used to consider the each sensornode and relate it with its nearest cluster heads and once againchoose the K regions cluster heads. The entire process is continuedunless K cluster heads becomes static. When both the energy andgeography are taken for consideration, it is also so significant to leadthe K-means algorithm for both the features simultaneously. Eventhough pre-defining K value is not small, it is generally apparentin various wireless sensor networks applications.

Consider each sensor node as nx in the figure .X . It isestablished as a vector tx = (ix, jx, ex)and where (ix, jx) representsthe geography position, and ex refers to the energy stage. At first,it requires to maintain the impact of each element value, wegeneralize each element of tx into the limit [0,1] utilizing the zscore normalization:

ix =ix − iσi

(1)

Where i and σi are the mean and standard deviation and valueof attribute i.

jx =jx − jσj

(2)

Where j and σj are the mean and standard deviation and valueof attribute j.

ex =ex − eσe

(3)

Where e and σi are the mean and standard deviation and valueof attribute e.

By using z score normalization, it estimates the exact value ofthe attribute of i , j and e in all the vector t . Thus, we obtain thenormalized vector t′x = (i′x, j

′x, e

′x) for nx . This process determines

attributes geographical position and amount of energy required forthe sensor to perform in both random and grid networks, that arecalculated using z score normalization.

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3.2 WEIGHTED COMPRESSIVE DATAAGGREGATION

For proposing the significant data aggregation technique, we createthe energy efficient compressive data aggregation algorithm, whichtakes the benefits from the sparse random measurement matrix andthe energy control capacity in wireless sensor nodes. The proposedalgorithm organizes the energy efficiency path for the sensor nodesto select the effective cluster head then it creates intra cluster dataaggregation and inter cluster data aggregation. This method aimsto obtains the energy efficiency and improves the life span of thenetworks, which additionally focuses on resolving load balancingissues.

In this proposed model, we divide the wireless sensor networksinto nc as non- overlapping clusters, represented byC = c1, ...., cnc, utilizing the simple and well establishmentK-means algorithm [], in which the base node separatelyaggregates the information of all clusters. The proposed algorithmis applied in both the random and grid networks for selecting thecluster heads randomly and then compressive sensing technique isutilized after that the communication have been taken place fromone cluster head to another cluster head. After particular timeinterval the efficient cluster head is selected from the previousperformances of the cluster head. Hence, we analysis and evaluatethe best and suitable networks for the efficient datacommunication and energy consumption.

3.3 GRID NETWORK

In this grid networks, we consider certain assumption and formthe clusters in the regions when the clustering procedure isexecuted orderly, the number of sensors in cluster for a bigger,aggregates given the grid area is divided into 9 regions in thewireless sensor networks. Each region selects the cluster headsrandomly by using the sparse random measurement matrix andthen compressive sensing technique is applied in the gridnetworks. It is presented in the figure 2. The diagram statesabout the region classification according to the K means clusteringalgorithm and the cluster head has been selected in randomly

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using the sparse random measurement matrix by identifying thesensor nodes magnitude and geographically position

Figure 2: Grid networks for cluster head

3.4 RANDOM NETWORK

For real time applications, wireless sensor networks are generallyunreachable and the makes nearby environment has a complexand uncertain. In generally for most of the application, senornodes deployment is classified into two types such as random-type deployment and grid-type deployment. In this section, wedescribe about the random type deployment for wireless sensornetworks, at first the blocks have be classified with help of kmeans clustering algorithm. It is represented in the figure 3.Further, the selected attributes are used in the sparse randommeasurement matrix to determine cluster head and thencompressive sensing technique is utilized in the process, later onthe communication have been carried out from one cluster head toanother cluster head. After analyzing the performance of the

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previous cluster head, best cluster heads will be considered forfurther data transmission.

Figure 3: Random networks with cluster heads

3.5 SPARSE RANDOM MEASUREMENTMATRIX

In this process, it has analyzed about the sensor nodes energy andlocation in the networks. For selecting the cluster head in thewireless sensor networks, we utilize the sparse randommeasurement matrix for estimating the cluster head node for boththe grid network and random network.

3.5.1 BLOCK REPRESENTATION FOR SPARSERANDOM MEASUREME

We assume wireless sensor nodes would evaluate about the originaldata vector vεn×n comprising of a set of orthonormal basis vectorsΨ1, .....,Ψn. Ψ denotes the, . we consider that obtained values havesparse representation in Fast Fourier Transformation ( FFT).

The orthonormal transformation coefficients is represented asθ = [ΨT

1 v, .....,ΨTnv]Tof the original data can be placed in the

magnitude of |θ|(1) ≥ |θ|(2) ≥ .... ≥ |θ|(n) maintains the greatestk transform coefficients and consider rest of the value as zero. Theapproximation error is ‖v2 − v̂2‖ = ‖θ2 − θ̂2‖ =

∑ni k + 1|θ|2(i) .

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We conclude that the data would be able to compress if supposethe magnitude of its transform coefficients decay like the energylaw. By utilizing this matrix, overall the transmission cost isreduced and moreover energy consumption is higher in thewireless sensor networks.

Let’s indicate φS the sub matrix for xth cluster. For xth cluster,cluster head is represented as CHx and data vector of the cluster iHx . The CHx is capable to calculate the x projections of all data iHgathered from the nodes inside the cluster on the sub-matrix, whichis represented as φHiHx . The CHx gives M projections from thedata inside the cluster by utilizing compressive sensing technique.The value of M is influenced by number of nodes N and the sparsitystage of the original data. Then it transmits the data to the basenode in M rounds, which relates all the cluster heads to the basenode. Considering all the sensor nodes and dividing its area intoseveral region using the K means clustering algorithm.

As represented in the figure.2, the cluster head is selectedrandomly in the given region of the grid area using the randommeasurement matrix φ is the total sum of the projectionsproduced from the clusters. Therefore, in each region the cluster

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head aggregates its own expulsion and the expulsion is obtainedfrom remaining sensor nodes in the same region and transmits itto the base using the multi-hop communicaiton. When the basereceives all the M rounds of expulsion from the cluster heads, theoriginal information for all wireless sensor nodes can be retrieved.

There are two stages of transmission occurring in our proposedmethod by applying hybrid compressive sensing: Inter- clusterand Intra cluster in the given grid region. In general, intra- clustertransmission that does not adopt the compressive sensing method,whereas the inter cluster utilize the compressive sensing method.The size of the data is same for both intra and inter cluster.Hence, by effectively minimizing the number of transmissionwould significantly minimize the energy consumption in the senornodes. As intra-cluster would forwards the original data to theircluster heads using the shortest path routing methods. For intercluster transmission, we develop the algorithm in order to obtainthe minimal cost while forwarding all the aggregates data to thecluster head with its expulsion in the wireless sensor networks.

The substantial process in the proposed work is to find outthe cluster size. Since, the cluster size would increase the numberof transmission aggressively but while diminishing the size of thecluster, the number of cluster will increase and the number of inter-cluster transmission would also develop in the networks. Therefore,it aims to obtain the optimal size of the cluster, which reducesthe overall data transmissions in the hybrid compressive sensingtechnique. It is used to estimate the optimal cluster size and designa distributed clustering approach so that the overall transmissionwould be reduced.

3.5.2 CLUSTER DATA MAINTENANCE

We organize the proposed architecture to handle the clusteringprocedure, but it preserves only the one standard block head nodeat the sink. As the organizer node does not know, which node tobe investigated to determine the suspicious event occurred in thesensor node. Therefore to make a efficient data discovery, weutilize the push- pull technique in the proposed model foridentifying the untrusting events that occurs in the sensor nodes,it would sporadically push the data throughput the network to

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predict the enemys action.Generally in this comb- needle model, a wireless sensor nodes

would produce huge number of events which always repeats itsinformation on the same routing direction. Wireless sensor nodestakes higher traffic overhead in this path while equating with thedistant sensor nodes. As sensor nodes in wireless sensor networksare provided by batteries and normally it does not have anyopportunity to restore their power sources, like unbalanced energyconsumption outcome will fast reduce power supply in the certainparts of the network. It may turn into hotspots, and they canimmediately deplete the energy and bring down the completewireless senor networks.

Figure 4: Basic Comb- Needle model

The main focus of the military unit used to gather the eventdata by employing the pull based data query in the networks. It isbasic mechanism in the comb-needle model that demonstrated inthe figure 4. To further develop this model, we include certaintechniques is adopted in []. The sensor nodes in the comb- needlemodel push their information along with the nearby nodes detail

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and the query is disturbed to those nodes based on the fixed spacelines of the network. Therefore, the query procedure is normallybased on the dynamical nature. It develops a comb-needle routingstructure and then construct the needle-like data duplicationstructure that organizes an conceivable view of combining forneedles in a haystack. In this part, we will formulate theheuristics on both the node energy and block energy and improvethe needle-like push process for effective data diffusion byobviating hotspots.

Figure 5: Enhanced comb- needle model

The enhanced comb needle model remains the as it but theneedle is collapsed into two types of sub-needles . Consequently,the query based pulling process stays the same, when the data isdistributed through the pushing process, which is refreshed toexploit as a hybrid distribution management. In the hybriddistribution management contains the multiple intra cluster andinter cluster dispersion, which attempts to exit before the pushingneedle action gets completed in the process. The appropriateselection node for the wireless sensor nodes would help to

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minimize the inter dispersion attempts, thereby which cansignificantly enhance the energy and time efficiency.

It encourages the system extend the network life span byutilizing the node with a greater energy level ζe. By assuring theproper dispersion direction ζe. We can estimate the comb needlecharacteristic, which means that there should be γ nodesvertically organized for event dispersion. By choosing the nodewith a minimum distance from entry node Di improve thedispersion node efficiency. Consequently, three condition will beoptimized they are dispersion direction, node energy and thedistance from the entry node.

Thus, we can determine the metric as given below:

K(ei) = H2ζei − ξeiDei

(4)

Where H2 is a standard constant and the ζei is estimated as follows

Likewise, the Euclidean distance D is estimated as

It should be mentioned that in the ζei estimation, the entry theflag fent = center, which intends to state that the entry node is atthe center of the position of a block along the y axis, and the nexthop node is needed both in the two-hop up dispersion direction.While the entry flag is down or up, it indicates that the next-hopnode should be chosen as per the dispersion direction. Or else,the entry flag would be out the intra region comes under the inter

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region and even though, the distance and energy are optimized, theselection of a wireless sensor node would not be accepted since thedemand of the comb needle cannot be estimated without an entryflag.

4 ENERGY-BASED REGION

CONSTRUCTION ALGORITHM

USING THE GEOGRAPHY

LOCATION ALGORITHM

Let consider{Sn1, Sn2, ...Snq}be the group of sensor nodes, withthe resemblance described above, K means clustering algorithm isutilized as a first algorithm. In this algorithm, cluster head isconsidered as the centroid, which plays as a major role forforwarding the original data to the base node from the sensornodes. It returns the centriod of a block, c indicator shows themajority potential blocks in the desired regions in which sensornodes should belongs C = c1 ,...., cn be the group of blocks of thewireless sensor nodes, with ck(1 ≤ k ≤ n) as a sensor block.

The K means clustering algorithm has the time complexity as O( qni) , where n is defined as pre defined block number and where i isreferred as number of iterations. The algorithm’s space complexityis given as O (3q + 3n) .

The proposed algorithm is developed for the both random andgrid networks, it states about the detailed description about theprocess carried out in this algorithm.

ALGORITHM FOR BOTH NETWORKS1. The first step in the algorithm, it is used to initial block regionas K, sensor node set C = c1 ,...., cn2. Then, it chooses a K nodes from sensor node set C, which isused for partitioning the multiple blocks in the grid and randomnetworks.3. Each selected specific node K from the K block region, itcontains information about the nearby nodes in the block. It usesthe K means clustering algorithm for classifying the blocks in theboth grid and random networks.

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4. It randomly selects a node as a cluster head in the each blockregions. The geographically and energy based attributes areutilized in the sparse random measurement matrix.5. By using random sparse measurement matrix, it first analysisthe sensor nodes energy and position and gathers informationabout the nodes performance in the both the grid and randomnetworks.6. Afterwards it utilize the random spare measurement matrix foranalyzing the data gathering through sparse random projection.In each projection matrix, as it would take all the iteration valueusing the Fast Fourier Transformation. while these functionswould receive the highest k transformation coefficients with themagnitude.7. Thus, it minimizes the approximation error as well astransmission cost and obtains the energy consumption in both thegrid and random networks.8. Once again, it update the cluster heads performance andreformation of cluster head will be selected based on the previousperformance in the networks, best cluster head are determined byusing attributes like energy and geographical sensor nodes, whichis incorporated in the sparse random measurement matrix .9. Additionally, cluster data maintenance is included in thisprocess, where the enhanced comb-needle model is utilized forminimum energy consumption and the data distribution. Thehybrid distribution management is proposed to classify themultiple and intra and inter cluster data dispersion in the boththe networks.10. Thus all these steps in this methodology would produce theeffective energy consumption and cluster head and eliminate thehotspots in the block distribution management.

The block data of the sensor nodes and the sink node are twoimportant element in a wireless sensor networks used foreliminating the hotspot. This can briefly describes about the usesof hybrid distribution system for real time applications in wirelesssensor networks. A clustering algorithm is also developed to forma similar sensor nodes into blocks with the capacity to update itdynamically in the region. In this same manner, block data canbe produced to confirm efficient intra-block and inter-blockheuristic long-distance data distribution. To maintain the

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inter-block energy consumption as well as to avoid the hotspots inthe inter-block, a fusion based dispersion approach is proposed.The experimental set up demonstrates that this mechanism cansignificantly reduce the energy consumption cost among thevarious blocks.

5 EXPERIMENTAL ANALYSIS

In this experimental set up, we evaluate the performance of theboth the grid and random network using the NS2 network simulatorversion 2.33 (NS2.33) and compare them with the existing methods[]. We estimate the best suited network for the data transmissionand energy consumption for the wireless sensor networks. At first,we assume the wireless sensor node that evaluate the reading valueswhich have the spare representation in Fast Fourier Transformation( FFT). If the transmission is more in the networks and obviouslyit consumes higher energy consumption and very faster it dies out.Thus, in our numerical evaluation, two important conditions areconsidered, first it decreases transmission cost in the networks andsecondly, it obtains the lesser energy consumption throughput thenetwork. We consider two network categories; Grid Network andRandom Network.

In grid network, simulation is done using a thousand-scale 40x40 grid with a 95 nodes excluding the sink node. It uses thesquare sensor field, where the sensor nodes are independently anduniformly distributed in the square sensor field of the size 20 x10square units. A sink node is placed at the corner of the sensor field.It has coordinates (0,0). The number of nodes Cn varies from 100to 500, then the density of the sensor node λ from 1 to 4. The datatransmission rate is set to

√2 unit. The edge length α of the grid

region in this numerical evaluation is set to 1 unit. For randomnetwork, it uses the 20 x10 units in the wireless sensor network’snode deployment and node set up and density are same as the gridnetwork. Common parameters for both grid and random networkis estimated in the table 1.

Let assume that ρ = AB

, it is referred as compressive ratio. ρ isdetermine to 15 and 20, then the sparse projections are moreenough to retrieve the original information with the highest

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accuracy. The sparse random measurement matrix φand theorthogonal transformation coefficients ψ in the comb needle modelwould select the cluster head. Each simulation results is carriedout for determining the compressive sensing for random and gridnetworks.

Compressive ratio: 15 (1) Figure 6. For Grid Network

(2) For Random NetworkFig , it clearly demonstrates that the reduction ratio of the

data transmission of the proposed approach is compared withother approach. As represented in the figure. X( a), While thecompressive ratio is 15, proposed approach minimizes the numberof transmissions by around 50% when compared to other method.

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The reduction rate would be higher than the other method.Finally the comparative analysis for grid and random network willbe evaluated and estimate which network has higher capacity toreduce the transmission cost.

Compressive ratio: 20 (1) Figure 7. For Grid Network

(2) For Random NetworkAs demonstrated in the figure. X( b), While the compressive

ratio is 20, proposed approach minimizes the number oftransmissions by around 70 % when compared to other method.

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The stimulated results for grid and random network woulddetermine the best network for compressive data in thetransmission.

5.1 Hybrid Distribution Management

To execute the node energy consumption, we build the five differenttypers of data packets in table 2. The first type of data packetwill be received from the basic comb needle model and then it isforwarded from the common node ni to node n j . In this event,

n i does not convey to the sink node, but it informs to thecommon node n j with explicit location

cordination. The second and thirs types of data packets are usedhyrid distribution management and its is upadated dynamically inthe table. The fourth and fifth types of data packets will be receivedfrom the enhanced comb needle model. Hence, ni forwards thefourth type of data packet to the sink node ( Sn ) to get the nexthop location. When the sink node Sn gets the data

packet, it estimates the next hop node nj and forwards to thefifth type of data packet to ni . The is utilized to terminate an eventdistribution as mentioned in the hybrid distribution management.

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6 CONCLUSION

In this paper, we consider the issues in the push and pullapproach in very large-scale wireless sensor networks for datadiscovery. Due to static data distribution establishment amongthe wireless sensor nodes, energy loss must be significantlyrejected since they make larger energy consumption and highertransmission cost. These problems have been solved by usingweighted compressive sensing technique in the random wirelesssensor networks in order to achieve lower energy consumptionwith the minimum transmission cost. We have applied theK-means algorithm for the random network to evaluate the bestfor data compression in terms of energy efficiency and extend thenetwork life span. In our experimental analysis, energyconsumption, and compressive ratio are anlysed. we demonstratesthat proposed method have obtained the energy efficiency andhigher compressing sensing of data in the random networks andfinally we estimated that cluster based method has best in termsof lower energy consumption and minimum transmission cost. Infuture work, we are planning to resolve in traffic as well as loadbalancing issues throughout the random networks.

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WEIGHTED COMPRESSIVE SENSING WITH K-MEANS … · In recent scenario, compressive sensing based data...

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