Data Mining In WSN: A Survey

International Journal of Advanced Engineering Research and Technology (IJAERT)Volume 2 Issue 2, May 2014, ISSN No.: 2348 8190

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Data Mining In WSN: A SurveyRobins Royal

Department of Computer Science and Applications, Kurukshetra University, Kurukshetra

ABSTRACTData clustering has been applied in various fieldslike machine learning, data processing, wirelesssensor network and pattern recognition.Association rule in data mining techniquefrequently plays a necessary role. Data mining hastypes of application areas like clustering in WSN,medical speciality and biological sequences. Thedisadvantages and advantages of K-means andPSO technique have been discussed and in theprojected model, K- means algorithm and ParticleSwarm optimisation (PSO) will be hybridized toproduce efficient results. For each application,technical details that are required for applyingPSO, such as its type, particle formulation andfitness function is discussed.

Keywords: k-means, Clustering, Particle Swarmoptimisation, WSN, PSO

1. INTRODUCTIONData cluster has infinite applications in knowledgecategorization, knowledge compression, dataprocessing, pattern recognition and compactingand image segmentation. Data processing indatabases is the outstanding automatic extractionof fascinating patterns from massive set ofknowledge collections. It mentioned the extractionor mining of huge quantity of information fromdata sources. The information sources embodydatabases, knowledge warehouse etc. [7]. In dataprocessing, association rule learning is that themost well-liked and extensively used methodologyto spot the interesting relations between theinformation keeps in giant database.

1.1 Introduction to WSNWireless sensor network (WSNs) may be a networkcomposed of distributed autonomous devices that maysense or monitor physical or environmental conditionscooperatively. WSNs are employed in totally differentapplications like environmental observation,surroundings monitoring, prediction and detection ofnatural calamities, medical observation and structuralhealth observation [2]. WSNs contain sizable amountof tiny, cheap, disposable and autonomous sensingelement nodes that would be simply deployed in an adhoc manner in large geographical areas for remote

operations [8]. Sensors produce ad-hoc networks thatmay send the detected event in a very multi-hop modeof transmission to the sink [2]. During this mode oftransmission, WSNs generates an oversized quantity ofknowledge within the sort of streams. As a result, realtime knowledge streams and therefore the distributednature of sensing element networks give newchallenges for the info mining techniques [1].

1.2 Clustering in WSNAn arrangement of sensor nodes into totally differentvirtual teams is named clustering.

Figure 1: shows the clustering in wsn [2]. Each clusterincludes of CH and its members. A CH typicallyserves sort of a leader for its cluster, performing intracluster transmission arrangement, information sending,and so on. [2] The cluster heads will summarize theinformation and send it to the information centre or bsas one packet, therefore reducing the overhead frompacket headers. In every round of the cluster formationsection, the network has to be compelled to choosecluster heads and transfers the aggregative informationto bs. For choosing a cluster head, the subsequentqueries are to be considered. Who can initialize the cluster head selection? What are the parameters necessary for deciding therole of a sensor node? Which sensing element nodes are going to benominated as cluster heads? will it require re-initiation of cluster formationprocess? Whether or not the chosen cluster heads are equallydistributed? Whether or not it assures load reconciliation ofclusters? Which information transmission is suitable for bignetwork, Single-hop or Multi-hop?


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clustering is extremely a lot of essential for WSNsfor the subsequent reasons. Reduces energy consumption by up bandwidthutilization. Load reconciliation for even distribution of sensors Re-clustering once fault tolerance occur CH transmitssolely aggregative information to the information sinknode Reducing range of nodes collaborating intransmission to bs by choosing CH. Scalability for big number of nodes Reduces information delay by regulating the amountof hops Economical utilization of resources

1.2 Introduction to Data miningData mining in WSN is the method of extractingapplication directed patterns with the accuracy tosimply accept from endless and fast flow of knowledgestreams from detector networks. During this case theentire knowledge can't be hold on and should beprocessed forthwith [9]. Data mining algorithmicprogram are sufficiently quick to process high-speedincoming knowledge. The traditional data processingalgorithms were meant to handle the static knowledgeand use the multistep and multi scan mining algorithmsto analyse static data-sets [12]. Therefore typical dataprocessing techniques aren't compatible for handlingthe big amount, high spatial property, and distributednature of the info created by the WSNs. cluster is avery important mechanism in wireless sensing elementnetworks for measurability and achieving higherperformance [1]. Cluster heads were created supportedworld and native parameter sets to realize improvednetwork performance. Cluster primarily basedalgorithmic program imply gradable routing andknowledge gathering protocols [8].

Generalized Algorithm1. Initialize the overall particle population using PSO.2. Every particle contains K clusters centres that weredisplaced in problem area by activity PSO algorithmicprogram.3. PSO continues to perform till the particle populationconverge.4. When convergence global best position wasencountered together with the simplest cluster centrethat was found by particles.5. This global best position from the network isconsidered as input to K-means. This algorithmicprogram can start operating till it converged.6. The global best position is termed as cluster centres.7. The initial cluster centres of the network areoptimised using k-means algorithmic programtherefore remove take away sensitivity of weakness.

8. The K-means algorithmic program relies on nativesearch optimisation. Due to its low process complexitylarge quantity of computations for native search areprevented.9. If the end result of K-means is best than global bestsolution then global best solution is replaced byoutcome of K-means [9].

2. LITERATURE REVIEWMd. Mamunur Rashidet. al [1] projected associated-correlated device patterns that may be a new form ofsensor behavioural pattern. The projected behaviouralpatterns not solely capture association-like co-occurrences however they additionally capture thesubstantial temporal correlations silent by such co-occurrences within the sensing element information.This study additionally showed that the plannedapproach was time and memory economical to findassociated-correlated patterns than the present mosteffective algorithms. S.Nithyakalyaniet. al [2]projected two typical data processing processes likeclustering and knowledge report that were needed tocut back the energy consumption of WSNs. The firstgoal of Node cluster in WSN was network pre-processing that aimed to gather qualified data and tolimit the energy consumed. The algorithms beneathstudy were Fuzzy C- means that cluster algorithms,knowledge relay K-means cluster algorithmic program.During this paper a comparative study from totallycompletely different analysis proposals was createdthat instructed different cluster head choice approachesfor knowledge aggregation.Sridhar Mandapatiet. al [3] mentioned that there's adesire to confirm bar of disclosures of confidentialpersonal data that is context sensitive. Literature isabundant with progressive strategies for privacyconserving biological process algorithms (EAs) thatoffer solutions to real-world optimisation issues.Existing EA solutions are specific to value performanalysis in privacy-preserving domains. This workproposes implementation of Particle Swarmoptimisation (PSO) to find an optimum generalizedfeature set. DivyaBansalet. al [4] detailed that theutilization of association rule mining in extractingpatterns that occur often within a knowledgeset andshowcases the implementation of the Apriorialgorithmic program in mining association rules from adataset containing crimes data regarding girls. For thisone knowledgeset is taken from UCI repository anddifferent knowledge is collected manually from thesession court of sirsa to gather data on heart meltingcrimes against ladies. the most motive to use UCI is to1st check the right operating of dataset so applyApriori on real dataset against crimes on ladies thatextracts hidden data that what cohort is chargeable forthis and to seek out wherever the real offender is


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concealing. SagarTiwaret. al [5] represented that theforemost renowned cluster approaches was K-meansthat effectively were employed in several clusterissues, however this algorithmic program had somedownside like native optimum convergence andsensitivity to preliminary points. During this study acooperative algorithmic program based on PSO and k-means is given. The proposed algorithmic programutilizes each global search ability of PSO and nativesearch ability of k-means. The projected algorithmicprogram and PSO with Contraction issue (CF-PSO), k-means algorithmic programs and KPSO hybridalgorithm are used for clustering six datasets and theirefficiencies were compared with one another. AnjanDas et. al [6] given a comparative study betweendistributed extraction algorithmic program (DEM) anda unique association rule mining mechanism (NARM)for wireless sensing element networks. The authorinstructed that the advances of wireless sensingelement network and their ability to get an oversizedquantity of knowledge, data processing techniques,notably association rule mining technique, forextracting helpful information concerning theunderlying network had received a good deal ofattention. Mehdi Neshatet. al [7] projected Associatein Nursing algorithmic program that utilizes eachworld search ability of PSO and native search abilityof k-means. Knowledge cluster was applied in multiplefields like machine learning, data processing, wirelesssensing element networks and pattern recognition. Theplanned algorithmic program and PSO withContraction issue (CF-PSO), k-means algorithmicprograms and KPSO hybrid algorithm were used forcluster six datasets and their efficiencies werecompared with one another. Wen-Hwa Liaoet. al [8]projected an object chase theme for OTSNsmistreatment data processing approach. The author hadimproved the Apriori algorithmic program for miningassociation rules and created it applicable to theOTSNs. The data mining algorithmic program wasapplied to the past movement information of the articleand helpful association rules are excavated, that werethen accustomed predict ensuing location of the article.The authors theme foreseen ensuing location of thearticle additional accurately and will increase thenetwork lifespan. The author showed that the themeoutperforms the present schemes in terms of energypotency and accuracy of chase. S.Z. Erdoganet. al [9]projected a method to avoid the necessity of full timecare giving service, the particular trend was toencourage older to remain living autonomously in theirhomes as long as potential. This study given analgorithmic program of fall detection, that observes fallevents by mistreatment data-mining approach. Theauthors projected methodology performed detection in2 steps. First, it collected the wireless sensing element

network (WSN) knowledge in stream format fromsensing element devices. Second, it used k-nearestneighbour algorithmic program well-known lazylearning algorithmic program to observe falloccurrences. It detected falls by distinctive the autumnpatterns within the knowledge stream. EnriqueGarcaet. al [10] detailed that the appliance ofassociation rule mining in e-learning systems andparticularly, learning management systems. The authordescribe the precise information discovery method itsmains drawbacks and a few potential solutions toresolve them.Teresa M.A. Basileet. al [11] descriedthat wireless sensing element networks (WSNs)represent a typical domain wherever there areadvanced temporal sequences of events. During thispaper the authorpropose a relative framework to modeland analyse the info ascertained by sensing elementnodes of a wireless sensing element network.Particularly the author extend a general purposerelative sequence mining algorithmic program to tackleunder consideration temporal interval-based relations.Real-valued statistic are discretized into similarsubsequences and represented by employing a relativelanguage. Preliminary experimental results prove thepertinence of the relative learning framework toadvanced globe temporal knowledge. GianlucaBontempiet. al [12] proposed a two layered modulararchitecture to perform data mining on large sensornetworks.The main extract of theapproach is that amodular aggregation of sensor data gives dualbenefit.The first benefit is the clustering of sensors andthen reducing the communicationeort.Secondly, thereduction in dimensionality of the datamining task andimproving the accuracy of the sensingtask. DianPalupiRiniet. al [13] projected that PSO may be abiologically galvanized process search andoptimisation methodology. Modification PSO isdeveloped for finding the essential PSO downside. Theessential PSO is additional applicable to method static,easy optimisation downside. [14]Andrea Kulakovet.alsuggested that some of the algorithms developedwithin the artificialneural-networks tradition can beeasily adopted to wirelesssensor network platforms andwill meet several aspects ofthe constraints for datamining in sensor networks like:limited communicationbandwidth, limited computingresources, limited powersupply, and the need or faulttolerance.[15]IanDavidson et. al developeda non-parametricversion ofEM specifically for sensor networks. Theyformallyshowed that the E-step can be solved inpolynomialtime. [16]Denis Krivitskiet. Aldemonstrated that in some cases hill climbing in wsn can besolved usinga local algorithm. Local algorithms areimportantfor sensor networks because they havesuperb messagepruning capabilities and because theyperform theirentire computation in-network. A sensor


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taking partof a local algorithm computes an exactresult using,in many cases, data it gathers from just itsnearestneighbourhood.

3. FINDINGSAs discussed and studied in the survey shown justabove we come to conclude that we can get importantinformation and limit energy consumed by efficientclustering technique. Apart from that it helps in loadbalancing, fault tolerance and coverage. We canassume that the sensor nodes are randomly distributedand not mobile. The coordinates of base station andsensors and other dimensions of sensors are alsopresumed. The following parameters of a network aregiven some appropriate value.

Number of Nodes Network size Initial node energy Minimum energy Network threshold Data transfer rate Location of base station Sensor nodes locations

The techniques studied are discussed below-: K-means:- K-means algorithmic program starts

with k random cluster centre and divides a groupof objects into k subsets [2]. This methodology isthat the most well-liked and most used clustertechniques because it is well graspable and will beperformed and had linear time quality. Thisalgorithmic program was initialized by choosing kpoints in a very d- dimensional vector D = one. . .N}, because the initial k clusters representatives orcentroids. The cluster condition method results in atwo-level hierarchy wherever the N nodes kind theupper level and therefore the cluster-membernodes form the lower level. The sensing elementnodes intermittently transmit their knowledge tothe corresponding N nodes [6]. The N nodes thenmixture the info and transmit them to the bottomstation (BS) either directly or through theintermediate communication with different Nnodes. The k-mean algorithmic program takes theinput parameter, k, and partitions a collection ofobjects into k clusters so the ensuing intra-clustersimilarity is high however the inter-clustersimilarity is low [9]. This algorithmic program hasfollowing advantages-:o K suggests that provides quicker results

most of the days as compared to gradablecluster with giant no. of variables providedthe worth of K is tiny.

o K-Means produces tighter clusters thangradable cluster, particularly if the clustersare circular.

The following disadvantages of K-meanssuggest that render it imperfect for our miningtask.

o Difficult to predict K price.o It doesnt work well with world cluster.o Different initial partitions may result in

numerous final clusters.o It isn't good for clusters of various sizes and

density.

Particle Swarm optimisation (PSO): - PSO maybe a population based mostly search algorithmicprogram that relies on the simulation of the socialbehaviour of birds or bees. Every individualparticle among the swarm is described by a vectorin four-dimensional search area. This vector isadditionally allotted a vector that verifies ensuingmovement of the particle that is understood as ratevector. The PSO verify the way to update thespeed of a particle. Every particle update its ratesupported current rate and the additionally the bestposition it's traversed to this point and alsosupported the worldwide best position explored byswarm. [13]Thistechnique has the subsequentadvantages-:o The technique is applicable in each research

project and engineering use.o It doesnt have any overlapping or mutation

calculation.o During the process of many generations, the

foremost person particle solely will transmitdata onto the opposite particles, andtherefore the speed of the researching isincredibly quick[13].

The disadvantages are as listed as follows-:o The methodology is susceptible to partial

optimism that causes the less precise at theregulation of its speed and therefore thedirection.

o The methodology cannot estimate the issuesof non-coordinate system, like the answer tothe energy field and therefore the movingrules of the particles within the energy field.

4. PROPOSED WORK4.1 Proposed ModelIn the projected model a replacement cluster approachis employed to beat to the disadvantages of PSO andK-Means algorithmic program. This model may be ahybrid of K- suggests that algorithmic program andParticle Swarm optimisation (PSO) [1]. In projectedalgorithmic program at initial step the nodes of WSNare initialized mistreatment PSO algorithmic program.


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Every particle contains M cluster centres which is ableto be displaced in downside area by activity PSOalgorithmic program which is able to continue tillconvergence. When convergence, the simplest answerare thought of as input of k- suggests that algorithmicprogram. The k- suggests that algorithmic program canbegin operating and whereas it's not converged itcontinue its operating. When convergence of PSOsparticles, PSOs output would have an applicableinitial cluster centres for k-means. When obtainingoutput from PSO, k-means algorithmic program cansearch regionally for best answer thanks to its highconvergence rate. When convergence of PSOsparticles, PSOs output would have an initial clustercentres for k-means. The nodes of WSN network arecollected and referred as particle population. Thebottom of cluster algorithmic program is measurementthe similarity between knowledge and it is determinedwhat quantity similar these 2 knowledge vectors areperformed.

5. CONCLUSIONIn the projected model, particle structure was designedfor cluster in PSO and k-means was used as behaviourin PSO that's performed on some swarm on everyiteration. The optimizing fitness perform associatedwith intra-cluster distance can show that the projectedalgorithmic program can cross well with high rate fromnative optimums and converge toward world optimumsand obtained results that are comparatively stable innumerous performance.

References1. Md. Mamunur Rashid, IqbalGondal,

loarderKamruzzaman ACSP-Tree: A TreeStructure for Mining Behavioural Patterns FromWireless Sensor Networks IEEE Conference onLocal Computer Networks, 2013

2. S. Nithyakalyani and Dr. S. Suresh Kumar DataAggregation In Wireless Sensor Network UsingNode Clustering Algorithms - A ComparativeStudy Proceedings Of IEEE Conference OnInformation And Communication Technologies,2013

3. Sridhar Mandapati1, DrRaveendraBabuBhogapathi and DrM.V.P.Chandra SekharaRao Swarm OptimizationAlgorithm for Privacy Preserving in Data MiningIJCSI International Journal of Computer ScienceIssues, Vol. 10, Issue 2, No 3, March 2013

4. DivyaBansal andLekhaBhambhu Execution ofAPRIORI Algorithm of Data Mining DirectedTowards Tumultuous Crimes ConcerningWomenInternational Journal of AdvancedResearch inComputer Science and SoftwareEngineering Volume 3, Issue 9, September 2013

5. SagarTiwari, SamtaGajbhiye Algorithm ofSwarm Intelligence Using Data ClusteringInternational Journal of Computer Science andInformation Technologies, Vol. 4 (4) , 2013

6. Anjan Das and Ganapati Das DistributedExtraction and A Novel Association Rule MiningMechanism for WSN: An Empirical AnalysisIEEE, 2013

7. Mehdi Neshat and ShimaFarshchianYazdi A NewCooperative Algorithm Based on PSO and K-Means for Data Clustering Journal of ComputerScience 8 (2): 188-194, 2012

8. Wen-Hwa Liao and Kuo-Chiang Chang AnObject Tracking Scheme for Wireless SensorNetworks Using Data Mining Mechanism IEEE,2012

9. S.Z. Erdoganand T.T. Bilgin A data miningapproach for fall detection by using k-nearestneighbour algorithm on wireless sensor networkdata Published in IET Communications, Receivedon 28th March 2011

10. Enrique Garca, CristbalRomer, SebastinVentura, ToonCalders Drawbacks and solutionsof applying association rule mining in learningmanagement systems Proceedings of theInternational Workshop on Applying Data Miningin e-Learning 2007

11. Teresa M.A. Basile, Nicola Di Mauro, StefanoFerilli, and Floriana Esposito RelationalTemporal Data Mining for Wireless SensorNetworks

12. GianlucaBontempi and Yann-Al Le Borgne AnAdaptive Modular Approach to the Mining ofSensor Network Data

13. Dian PalupiRini, Siti Mariam Shamsuddin andSitiSophiyatiYuhaniz PSO technique, system andchallenges

14. Andrea Kulakov and DancoDevcevData miningin wireless sensor networks based on artificialneural-networks Algorithms

15. Ian Davidson and SS Ravi Distributed Pre-Processing of Data on Networks of BerkeleyMotes Using Non-Parametric EM

16. DeniKrivitski,AssafSchustery, and Ran WolffLocal Hill Climbing in Sensor Networks

Data Mining In WSN: A Survey

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